esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 IoT Overview for Predictive Maintenance in Smart Manufacturing
### Comparative Analysis of Predictive Maintenance Applications
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Monitored System** & **Monitored Parameters** & **Communication Technology** & **Prediction Algorithm** & **Performance** & **Cost–Benefits and Implementations** \\ \hline \multirow{3}{*}{Nangia} & \multirow{3}{*}{Monitoring machine} & Temperature, & \multirow{3}{*}{Ethernet on IIoT} & C\&RT & 89.1\% (C\&RT) (A) & High performance, complex monitoring \\  & & & current, vibration & & cloud & 87.9\% (BCT) (A) & process, improved \\  & & & and other & & SVM & 89.3\% (SVM) (A) & using cloud \\ \hline \multirow{3}{*}{Chen} & \multirow{3}{*}{Real-time operating status of centrifugal pumps} & \multirow{3}{*}{Vibrations signals} & Wired and & \multirow{3}{*}{N.A.} & \multirow{3}{*}{85\% (A)} & low-cost and \\  & & & wireless technology (4G, & WIFI) & & & easy-to-implement \\ \hline \multirow{3}{*}{Fordal} & \multirow{3}{*}{Splitting saw monitoring} & Temperature, & \multirow{3}{*}{Ethernet on IIoT cloud} & \multirow{3}{*}{ANN} & \multirow{3}{*}{N.A.} & \multirow{3}{*}{Entry-level solution to enable I4.0} \\  & & vibrations, and & & & & \\ \cline{1-1}  & & & & & & \\ \hline \multirow{3}{*}{Ayvaz} & \multirow{3}{*}{Monitoring of production lines} & Motion, speed, & \multirow{3}{*}{MQTT} & ML & \multirow{3}{*}{98.2\% (A)} & Complex monitoring process with \\  & & & temperature, & & Random & \\ \cline{1-1}  & & and air pressure & & & forest & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative evaluation of references previously discussed related to IoT-based predictive maintenance.
# OrientedDiffDet: Diffusion Model for Oriented Object Detection in Aerial Images
## 3 Approach
### Proposed Method
#### 3.2.3 Accelerated Diffusion  
Random or estimated boxes from the previous step are sent to the detection decoder for category classification and box coordinate prediction in each sampling step. After obtaining the boxes of the current step, accelerated diffusion, which leverages the semi-linear structure of the diffusion ordinary differential equations (ODEs), is adopted to estimate the boxes for the next step. With an initial value \(\mathbf{x}_{T}\) and \(M+1\) time steps \(\{t_{i}\}_{i=0}^{M}\) decreasing from \(t_{0}=T\) to \(t_{M}=0\), we start with \(\widetilde{\mathbf{x}}_{t_{0}}=\mathbf{x}_{T}\) and iteratively compute the sequence \(\{\tilde{\mathbf{x}}_{t_{i}}\}_{i=1}^{M}\) as follows:  
\[\tilde{\mathbf{x}}_{t_{i}}=\frac{\alpha_{t_{i}}}{\alpha_{t_{i-1}}}\tilde{\mathbf{x}}_ {t_{i-1}}-\sigma_{t_{i}}(e^{h_{i}}-1)\mathbf{\epsilon}_{\theta}(\tilde{\mathbf{x}}_{t _{i-1}},t_{i-1}),\text{where }h_{i}=\lambda_{t_{i}}-\lambda_{t_{i-1}} \tag{6}\]  
Accelerated diffusion approximates the exact solutions of diffusion ODEs by leveraging their semi-linearity through a simplified formulation involving an exponentially weighted integral of the noise prediction model. In our study, we apply the accelerated diffusion method to expedite the sampling process within our proposed OrientedDiffDet model for object detection in remote-sensing images. This implementation significantly enhances convergence speed and maximizes the utilization of known information embedded in the diffusion ODE.  
Figure 3: Illustration of oriented feature alignment. Blue box is a proposal generated by diffusion model, and the red box is its corresponding rectangular proposal used for projection and feature alignment.  
The integration of accelerated diffusion results in our OrientedDiffDet model, which achieves swifter and more efficient inference. This translates to reduced sampling time for diffusion models, ultimately enhancing the performance of our object detection system. These improvements make our system well suited for real-time applications in remote-sensing image processing.  
By incorporating this technique into our methodology, we not only address the inherent challenges of oriented representation and computational efficiency in object detection but also achieve faster and more precise detection results in the context of remote-sensing images.
## 2 Materials and Methods
### Deployment Platforms
#### 2.7.2 Raspberry Pi 4  
Raspberry Pi is a low-cost, credit-card-sized single-board computer that was developed in 2012 by the Raspberry Pi Foundation in the UK. Raspberry Pi has its own operating system, previously called Raspbian, based on Linux. The Raspberry Pi includes 26 GPIO (General Purpose Input/Output) pins, allowing users to connect a larger variety of external hardware devices. Furthermore, it supports practically all of the peripherals offered by Arduino. This board accepts code in practically any language, including C, C++, Python, and Java. The Raspberry Pi has a faster processor than the Arduino and other microcontroller modules [37]. It can function as a portable computer. The details of the Raspberry Pi 4 are given in Table 6.  
The Tensorflow Lite model was deployed on the Raspberry Pi 4 through a script written in Python that provides basic information, including the real-time prediction of chickpea variety and the percentage of prediction accuracy. The script takes real-time images as input and feeds them one by one to the TFLite model to process them. The model provides a set of confidence scores, which represent the probability of the image belonging to different categories. Then, the script will select the index corresponding to the highest confidence score and present the image along with the label associated with that index. The experimental arrangement for model deployment using the Raspberry Pi 4 is given in Figure 5.  
Figure 4: Deployed model in Android device (smartphone) showing the prediction accuracy for Orion chickpea variety.
## 3 Modeling Methods
### Multivariate Linear Regression  
MLR is a curve-fitting approach that utilizes the criteria of minimizing the ordinary least square errors. The basic form of the function to predict a variable 'Y' can be expressed as in Equation (2).  
\[\mathrm{Y}=\mathrm{a}+\sum b_{i}x_{i} \tag{2}\]  
where a is the intercept for the equation, \(b\) is the vector of regression coefficients, and \(x\) is the vector of independent variables [23]. It is a statistical technique, hence, selection of parameters in vector \(x\) depends upon their effect on the model. A t-statistic is used for this selection process [23].
# Distributed-Satellite-Clusters-Based Spectrum Sensing with Two-Stage Phase Alignment
## 2 System Model
Mathematically, a constrained optimization problem for the design of the beamforming weight vectors can be formulated as  
\[\max_{\mathbf{w}}\gamma=\frac{p_{t}}{\sigma_{n}^{2}}\frac{\mathbf{ w}^{H}\mathbf{\Phi h}^{H}\mathbf{\Phi}^{H}\mathbf{w}}{\mathbf{w}^{H}\mathbf{w}} \tag{5}\] \[s.t.\|\mathbf{w}\|=1\]  
In general, the maximum ratio combining (MRC) scheme can obtain the optimal SNR; by applying the Cauchy-Schwarz inequality, it is not difficult to find that the optimal solution is given by  
\[\mathbf{w}=\frac{\mathbf{\Phi h}}{\|\mathbf{\Phi h}\|} \tag{6}\]  
However, the MRC reception requires full channel-state information (CSI) (i.e., both channel amplitude and phase) for all diversity branches, which is difficult to obtain for the SS_DSC system due to the lack of sensing-target location information. By comparison, equal gain combining (EGC) offers a somewhat reduced complexity alternative. In EGCreception, the sensing signals are weighted by phase only and the SNR at the master satellite is maximized when \(\mathbf{w}_{opt}^{H}=\left[e^{-j\Phi_{1}(t)},e^{-j\Phi_{2}(t)},\ldots,e^{-j\Phi_{ L}(t)}\right]\) is satisfied; then, we have  
\[\gamma_{opt}^{EGC}=\frac{\mathcal{T}}{L}\bigg{|}\sum_{i=1}^{L}h_{i}\bigg{|}^{2} \tag{7}\]  
Therefore, the optimization problem of maximizing SNR becomes finding the approximation of \(\mathbf{\Phi}\), so as to achieve phase coherence at the master satellite. However, due to the random time-varying channel response, satellite high dynamics and non-ideal synchronization, the \(\mathbf{\Phi}\) can change in a random manner. In general, \(\Phi_{i}(t)\) can be divided into two parts: one is the deviations between the master satellite and the concomitants, and the other is the deviations between DSC and ground sensing target. Thus, the phase deviations at the master satellite \(i\) at the \(t\) timeslot can be expressed as  
Figure 1:0  
where \(a_{i}(t)\) is caused by the change in the distance between the satellite and the ground, which leads to the initial phase difference between the master satellite and the concomitants; \(b_{i}(t)\) is caused by the change in the satellite formation, and the change in distance between satellites leads to the rapid change of phase; and \(c_{i}(t)\) is the random phase deviations caused by the perturbation of the DSC. Further, \(\varphi_{i}(t)\) is the channel phase response from sensing target to the distant DSC, and \(\xi_{i}(t)\) is the effect of non-ideal synchronization between them. Note that all values of the deviation variable are assumed to be uniformly distributed over \([0,2\pi)\). Therefore, since \(\mathbf{\Phi}\) is caused by many reasons, we need to accommodate the remedy to the case for different reasons to obtain the approximate value of \(\mathbf{\Phi}\), so as to obtain the \(\gamma_{opt}^{EGC}\) at the master satellite.
# Feasibility of a Patient-Specific Bolus Using the Life-Casting Method for Radiation Therapy
## 3 Results  
### Air Gap Quantification  
Figure 4 shows the air gaps between the skin surface and each bolus. In the case of curved surfaces, the patient-specific bolus fabricated using the LC method demonstrated a reduction in the air gap between the skin surface and bolus compared to the SuperFlab bolus. When utilizing the patient-specific bolus fabricated in this study, the total air-gap volume was measured as 9.51 cm\({}^{3}\), whereas the SuperFlab bolus yielded a volume of 42.5 cm\({}^{3}\). These findings demonstrate a substantial 77.62% reduction in the total air gap when using the patient-specific bolus compared to the commercially available alternative.
# An Efficient Framework for Securing the Smart City Communication Networks
## 2 Related Work
### Literature Review
The approach is not scalable because as the number of devices grows, the trust anchor's ability to perform mutual authentication and key generation may be limited. The authors of [13] demonstrated that a key management scheme for unicast, multicast, and broadcast has been discussed using the Iolus framework, which is based on a secure distribution tree using group security controllers (GSC) to manage top level subgroups and group security intermediaries to manage the remaining subgroups. The GSC is in charge of all subgroup keys, whereas subgroups are in charge of all node keys. In terms of scalability, the Iolus framework can be advantageous because changes in membership within a group will not affect other subgroups. The Iolus-based technique offers limited multicasting functionality as well as the production of a larger number of keys stored in remote terminal units, but at the cost of higher computational overhead and complexity [19]. From the above literature review, it should be emphasized that one of the most basic requirements for grid systems to enable appropriate resilience to attacks is an improved security system. Smart grid systems are expected to use a variety of authentication and secure key management mechanisms. Most of these secure key management systems and practices, on the other hand, are either incomplete or do not correspond to real-world scenarios and emerging technologies. As a result, it is crucial to understand that security requirements differ from one system to the next. As a result, in terms of secrecy, a planned design is required. However, [20] estimated that the D-H key exchange algorithm provides security for unprotected channels by exchanging secret keys over unprotected channels, whereas [21] argued that because the traffic between smart meters and utilities in the smart grid system is predicted to be massive, the most secure authentication and encryption solutions may not be the fastest. As a result, it is apparent that secure authentication and session key exchange systems have an impact on the smart grid communication network's performance. Therefore, the goal of this paper is to enhance the performance of security in smart city communication networks by using a new framework and scheme that provide an authentication and high confidentiality of data by implementing them along with session key exchange schemes through the OPNET simulator modeler.
# Experimental Study on Scour Resistance Performance Enhancement of Chongqing Red Clay
## 3 Results and Discussion
### Scour Resistance Analysis
The influence of water-to-solid ratio on critical shear stress is shown in Figure 15. As curing time increases, the critical shear stress of solidified soil gradually rises, while under the same curing duration, higher water-to-solid ratios result in lower critical shear stress, indicating that the scour resistance of solidified soil is highly sensitive to water content. This phenomenon may occur because high water-to-solid ratio conditions increase free water between red clay particles, elevating porosity and weakening interparticle cementation, thereby reducing shear resistance. Conversely, low water-to-solid ratios promote denser cement particle distribution, enabling hydration-generated cementitious materials to better fill pores and encapsulate red clay particles, thereby enhancing shear resistance.
## 5 Conclusions  
The continued increase in data has a more significant impact on the latency and energy consumption of edge computing. The development of big data analytics and intelligent devices increases the amount of computation for edge nodes, which is related to the latency and energy consumption of edge computing. This centralization of data storage and computing power results in overloading and lowers efficiency.  
In this context, we proposed a new intelligent offloading model that helps each other between edge nodes focusing on offloading technologies centered on data and tasks. To our knowledge, this is the first study of intelligent task distribution offloading, which focuses on data and tasks and cooperates between edge nodes. This model solves the overload caused by the limited capacity of the edge node while maintaining real-time benefits.  
Therefore, this paper describes a model based on wildfire scenarios that require rapid response and analysis. The process of applying the model and algorithm was suggesting the overload occurring in the process of predicting fire and predicting the spread of forest fire is presented in detail. Therefore, this scenario can prevent damage or reduce the scope of damage by quick analysis and response by collaborating with a relaxed edge node through the proposed model. In other real-time response areas such as disasters and emergencies, the proposed model can be useful for various studies.  
In this paper, virtual data is used instead of real data. In future research, we will investigate the function of hardware to specify energy consumption, execution time, etc. and further organize it using virtual data based on forest fire scenarios. Through this, we want to compare the efficiency by analyzing the waiting time and energy consumption when we randomly collaborate and apply the proposed model.  
Conceptualization, J.K. ([PERSON]), S.K., J.K. ([PERSON]), N.S. and Y.Y.; methodology, J.K. ([PERSON]), S.K. and Y.Y.; resources, J.K. ([PERSON]); writing--original draft preparation, J.K. ([PERSON]); writing--review and editing, S.K.; supervision, Y.Y.; project administration, J.K. ([PERSON]), N.S. All authors have read and agreed to the published version of the manuscript.  
Korea government(MSIT) and National Research Foundation of Korea (NRF).  
This work was supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (No.2018-0-01456, AutoMaTa: Autonomous Management framework based on artificial intelligent Technology for adaptive and disposable IoT). This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2019R1I1A1A01064054).  
Authors declare no conflicts of interest.
# The Carbon Sequestration Potential of Degraded Agricultural Land in the Amhara Region of Ethiopia
## Results
### Gross and Net Carbon Sequestration Potential by Agro-Ecological Zone  
As productivity follows topographic conditions and climatic patterns, we clustered our Biome-BGC simulation results for NPP, soil, and aboveground carbon according to the six agro-ecological zones (Table 2). The productivity differences across the agro-ecological zones are directly visible from the NPP, which is highest for Highland-wet and lowest for Midland-dry (Table 2). The results of the 100 year soil and aboveground carbon stock predictions by agro-ecological zone and thinning scenario are shown in Table 3. The highest accumulated soil carbon stocks ('gross', including initial carbon and carbon gain after reforestation) is evident on grass land, zone Highland-wet (91 t C ha\({}^{-1}\), no thinning), while the lowest stocks can be expected at bare land, zone Midland-dry (26 t C ha\({}^{-1}\), no thinning, Table 3). Similarly, the highest and lowest aboveground carbon stocks are found in grass land, zone Highland-wet (150 t C ha\({}^{-1}\), no thinning), and bare land, zone Midland-dry (51 t C ha\({}^{-1}\), no thinning), respectively. The spatial distributions of predicted soil and aboveground carbon and annual NPP across the reforestation areas are depicted in Figure 4.  
Figure 4: Spatial distributions of predicted soil and aboveground carbon and annual NPP 100 years after reforestation (unthinned) for the selected reforestation areas on the 9 km by 9 km resolution. (**a**) Soil\({}_{\rm{G}}\)—Gross soil carbon stock (t C ha\({}^{-1}\)); (**b**) Soil\({}_{\rm{N}}\)—Net soil carbon stock (t C ha\({}^{-1}\)), calculated as the difference between Soil\({}_{\rm{G}}\) and the initial soil carbon stock Soil\({}_{\rm{G}}\) (compare Tables 1 and 3); (**c**) Above\({}_{\rm{G}}\)—Gross aboveground carbon stock (t C ha\({}^{-1}\) year\({}^{-1}\)); (**d**) annual net primary production (NPP, 10-year mean, years 91-100 after reforestation).
## 3 Results
### Rehabilitation after Femorotibial MFX  
The recommendations on weight-bearing in the early rehabilitation phase are characterised by 3-4 weeks of partial weight-bearing (PWB) and a consistent progression to full weight-bearing (FWB), which can be reached after 5.6 \(\pm\) 1.1 weeks. Restriction of ROM is recommended by only a few institutions. A proportion of 83.8% of orthopaedic surgeons release full range of motion after MFX of the femorotibial compartment directly after surgery. Motion therapy is recommended immediately or 3 to 7 days at the latest after surgery, and is applied for a period of 7.8 \(\pm\) 2.1 weeks (Figure 3). Rehabilitation training is recommended after 5.5 weeks \(\pm\) 1.8 weeks. Most training programs start with low-intensity exercises such as ergometer training or aqua-jogging to increase mobility with minimal loading. More specifically targeted training starts after 14.3 \(\pm\) 5.7 weeks.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Rehabilitation Criterion** & **Categories** \\ \hline \hline \multirow{4}{*}{Weight-bearing} & No body weight (NBW): no loading \\  & Partial body weight (PBW): \(\leq\)20 kg \\  & Half body weight (HBW): \(>\)20 kg \\  & Full body weight (FBW): no restriction \\ \hline \hline \multirow{4}{*}{Range of motion (extension/flexion)} & Immobilization 0\({}^{\circ}\) \\  & 0-0-30 \\ \cline{1-1}  & 0-0-60 \\ \cline{1-1}  & 0-0-90 \\ \cline{1-1}  & 0-0-free \\ \hline \hline \multirow{2}{*}{Use of braces} & Yes/No \\ \cline{1-1}  & Recommended wearing time (weeks) \\ \hline \hline \multirow{4}{*}{Continuous active/passive motion (CAM/CPM)} & CAM recommended \\ \cline{1-1}  & CPM recommended \\ \cline{1-1}  & No CAM/CPM recommended \\ \hline \hline Start of rehabilitation training & Weeks after surgery \\ \hline \hline \end{tabular}
\end{table}
Table 1: Categories of early rehabilitation.  
Figure 1: Flow of evaluation. MFX: microfracturing; fem./tib.: femorotibial compartment/main-load-carrying area; MACT: matrix-assisted cartilage transplantation; OATS: osteochondralal autograft transfer system; ROM: range of motion; CAM/CPM: continuous active/passive motion.  
Figure 2: Number of rehabilitation protocols of orthopaedic institutions concerning cartilage repair techniques of the knee joint. MFX: microfracturing; MACT: matrix-assisted cartilage transplantation; OATS: osteochondralal autograft transfer system.
# An Interpretable Deep Learning Method for Identifying Extreme Events under Faulty Data Interference
## 3 Example
### Neural Network Training and Validation  
As a classification task, accuracy, precision and recall are used as metrics to evaluate the model's performance. Accuracy is defined as the ratio of correctly predicted samples to the total number of predicted samples, regardless of their positive or negative status. Precision, on the other hand, is the ratio of true positive samples to the total number of samples predicted as positive for a given class, thus quantifying the proportion of correct positive predictions among all positive predictions made by the model. Recall is defined as the ratio of true positive samples to the total number of actual positive samples for a given class, measuring the proportion of positive samples that the model correctly identified. The mathematical expressions for these metrics are as follows:  
\[accuracy=\frac{tp+tn}{tp+tn+fp+fn} \tag{5}\]  
\[precision=\frac{tp}{tp+fp} \tag{6}\]  
\[recall=\frac{tp}{tp+fn} \tag{7}\]  
Figure 7: Visualization of samples distribution in: (**a**) seismic event No. 1; (**b**) seismic event No. 2. The vertical cyan lines indicate the time when the monitoring system detected the earthquake.  
where, _tp_ denotes \"true positive\" (Predict positive classes as positive classes); _fp_ denotes \"false positive\" (Predict negative classes as positive classes); _tn_ means \"true negative\" (Predict negative classes as negative classes); and _fn_ means \"false negative\" (Predict positive classes as negative classes).  
In this study, the neural network's training was performed on a computer with a CPU of Intel Core i9-11900K and a GPU of NVIDIA A6000. The batch size was set to 50, the learning rate was 0.001, and the number of epochs was 120. The learning rate reduced to half of its previous value after ten epochs for training stability. The model's performance metrics, i.e., accuracy and loss, on both the training and validation sets of each epoch were reserved. The validation accuracy was used as the criterion for selecting the best-performance model, which occurred at the 98 th epoch, as shown in Figure 8.  
The confusion matrix in Figure 9 shows the best-performance model's classification performance on the validation set. It is shown that the seismic precision and recall rates on the validation set are 91.5% and 92.0%, respectively, which suggests a high probability that the monitoring system correctly detects a real seismic event rather than a false alarm due to sensor faults. Although the deep model has achieved an accuracy of nearly 90% for multi-class anomaly classification, the precision and recall rates for the Bias class are relatively low. We will investigate this further in the next chapter.