esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 New Production Scheduling Methodology
### Criterion of Maximum Completion Time  
The value of the constraint defined in the previous section determines whether the production schedule is feasible. Optimization, based only on the feasibility constraint, will end after finding a solution for which \(C_{F}=0\). This production schedule is correct, but it cannot be said to be optimal because we did not define the optimality measure. Therefore, it is necessary to define the criterion of the maximum completion time \(C_{max}\), which aims to optimize the production schedule to achieve the shortest execution time for a set of production orders. Determining the value of the measure \(C_{max}\) consists of determining the difference between the end time of the last task and the start time of the first task, which is presented in Equation (6):  
\[C_{max}=max\{t_{e,o_{1}}(R_{1}),t_{e,o_{2}}(R_{1},R_{2}),\ldots,t_{e,o_{n}}(R_{ 1},R_{2},\ldots,R_{n})\}-min\{t_{s,o_{1}},t_{s,o_{2}},\ldots,t_{s,o_{n}}\} \tag{6}\]  
where: \(R_{i}\)--set of resources for the \(i\)-th operation; \(t_{e,o_{i}}\)--end time of operation \(i\); \(t_{s,o_{i}}\)--start time of operation \(i\).  
Figure 2: An example of a resource access conflict, where the technological operation \(x\) started before the end of the technological operation \(y\).  
When this criterion is defined in the proposed new methodology, it should be noted that the completion time of each operation results not only from the resources allocated to that operation but also from the selection of resources in all preceding operations. This is because the feasibility constraint in Equation (5) must be met. As a result, the influence of the selection of resources on the duration of the operation affects the achieved values of the criterion of the maximum completion time. Thanks to this, by using the new scheduling methodology, we can increase productivity. For the simplicity of Formula (6), it was assumed that the operation index indicates its order in the schedule.
## 2 Materials and Methodology
### Methodology
#### 2.2.2 Material Characterization  
XRD measurements were carried out using a SHIMADZU XRD-6000 diffractometer with a Cu K\(\alpha\) source (\(\lambda\) = 1.54178 A) operating at 40 kV and 40 mA, in the 2\(\theta\) range of 20-80\({}^{\circ}\). XRD refinement was performed using the GSAS-EXPGUI program.
# Optimal User Association Strategy for Large-Scale IoT Sensor Networks with Mobility on Cloud RANs
## 6 Conclusions  
In this work, we studied a C-RAN with large-scale IoT devices. We particularly focused on the optimal strategy of associating RRHs with large-scale, mobile IoT devices. To prevent the possible network congestion and control message flooding caused by unbalanced traffic load among RRHs and frequent handovers, respectively, we first proposed an optimal load-balancing user association strategy. By slightly revising the problem definition, thereafter, we propose an effective way of minimizing the number of handovers as well. We implemented a discrete time network simulator to conduct the performance evaluation, and comparison against conventional association algorithms. The results show that, in comparison to the conventional association schemes, the proposed methods allow balancing the traffic load effectively among RRHs overall and minimizing the number of handovers overall.  
The proposed solutions in this work can be applied to general wireless network scenarios with densely deployed access points (APs). In addition to dense C-RANs, another possible example is the Wi-Fi or IEEE 802.11 network which is widely available nowadays. Due to their dense deployment, the proposed load balancing and handover minimization technique can significantly enhance the quality service for users. In addition, the proposed methods can also be used for networks that do not have dense APs as long as their transmission range is large, resulting in large coverage overlapping areas. Example networks are general cellular networks, Wi-Fi HaLow (or IEEE 802.11 ah) [33], Sigfox, LoRaWan, and NB-IoT [34], to name a few.  
As mentioned in Section 5, sectorizing or partitioning a given network into multiple disjoint areas to reduce the problem complexity is left as our future research direction. Using the decomposition theory to transform the proposed method into a distributed algorithm of a low complexity will also be considered in the future.  
Conceptualization, T.K., C.C. and W.C.; methodology, T.K. and W.C.; software, T.K.; validation, T.K., C.C. and W.C.; formal analysis, T.K.; investigation, C.C. and W.C.; resources, C.C. and W.C.; data curation, T.K.; writing--original draft preparation, T.K.; writing--review and editing, C.C. and W.C.; visualization, T.K.; supervision, W.C; project administration, C.C. and W.C.; and funding acquisition, W.C. This research was a part of the project titled \"Development of ICT-based PSC countermeasure technology and core equipment for implementation of IMO ballast water management convention\", funded by the Ministry of Oceans and Fisheries, Korea, and was supported by research fund from Chosun University, 2019. The authors declare no conflict of interest.
# A Hybrid Watermarking Approach for DICOM Images Security
## 3 Proposed Method
### Zero-Watermarking Secret Share and Watermark Generation  
The generation of the zero-watermarking secret share and the watermark is based on the extraction of preselected pertinent statistical features (skewness, entropy, and median) from the anatomical part of the original image. The pertinent statistical features were chosen after applying a statistical analysis aiming at selecting the smallest set of statistical parameters making it possible to uniquely identify each medical image within a large database of medical images.  
The initials of the patient name (first letter of the given name and first letter of the family name) are extracted from the header of the DICOM image and presented as a matrix of size 16 \(\times\) 16 pixels. An additional matrix is also generated from the anatomical part. The three pertinent features, the patient initials matrix, and the additional matrix are used as input of the Jacobian model to construct the zero-watermarking secret share of size 16 \(\times\) 16 pixels. After generation, the zero-watermark is encoded into a watermark of size 32 \(\times\) 32 pixels before its embedding in the original image.  
Figure 3 illustrates the process of watermark generation. Figure 4 shows an example of a watermark generated from the original DICOM image of Figure 3, using the proposed watermark generation process.
# Light-Weighted Password-Based Multi-Group Authenticated Key Agreement for Wireless Sensor Networks
## 3 Proposed Multi-Group Authenticated Key Agreement Protocol for WSNs
### Initialization Phase  
1. The authentication server \(AS\) randomly selects \(mk\) as its master key.
2. \(AS\) computes \(pk_{s}=T_{mk}(x)\ mod\ p\), where \(x\) is a random number and \(p\) is a large prime number.
3. \(AS\) publishes parameters (\(pk_{s}\), \(T(.)\), \(x\), \(p\)).
# Age Estimation from fMRI Data Using Recurrent Neural Network
## 4 Experiments
### Training  
The data are divided into three independent sets: training, validation, and test sets. We first divide all the data into two parts, with a ratio of 8 to 2 as shown in Table 2. In all, 20% of the total data, namely 155 samples, are assigned for the test. The remaining 80% of the total data, namely 640 samples, are used for training and validation.  
We use \(k\)-fold cross-validation with \(k=10\) due to an insufficient amount of data. The data except for the test set (total 640 data points) are divided into 10 subsets, one subset is used as the validation dataset and the other 9 subsets are used as the training dataset. This procedure is repeated 10 times, then the average performance is computed. We choose the model hyperparameters that provide the best average performance.  
For model training, we use the Adam optimizer with learning rate attenuation. The initial learning rate value is set to 0.001 for the GRU model and 0.0001 for the Transformer model. The learning rate is attenuated by 0.99 every 100 steps. In our experiment, the training batch size is set to 144 where the number of epochs is 10,000. The mean squared error (\(MSE\)) is employed to compute the loss while training  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}(f_{NN}(X^{(i)})-y^{(i)})^{2}\]  
where \((X^{(1)},y^{(1)}),\ldots,(X^{(n)},y^{(n)})\) denotes the preprocessed data sample and label pair, and \(f_{NN}(\cdot)\) denotes a proposed neural network. Thus, the training is to minimize \(MSE\).
# Bioacoustic Classification of Antillean Manatee
## 2 Materials and Methods
### Experimental Setup
The performance of the model was related to the capacity to provide true predictions: true positive (TP) and true negative (TN), it was also accounted for prediction errors or false prediction: false positive (FP) and false negative (FN). From the confusion matrix and the relation between (TP, FP, TN and FN) a five metrics can be determined, described hereafter:  
* Accuracy: used to evaluate the number of true predictions made by the model, calculated with the following formula: \(\frac{\#TP+\#TN}{\#TP+\#TN+\#FP+\#FN}\). * Precision: used to evaluate the proportion of positive predictions that were correctly classified, it is also called _positive predictive value (PPV)_ and is calculated using the formula \(\frac{\#TP}{\#TP+\#FP}\). * Recall: used to evaluate what proportion of the actual (observed) positive predictions that were correctly classified, it is also called _sensitivity_ or _true positive rate (TPR)_ and is calculated using the formula: \(\frac{\#TP}{\#TP+\#FN}\). * F1 Score: used to evaluate the model accuracy, considering both the precision and recall. It ranges from 0 (worst) to 1 (best, perfect precision and recall). It is often used when there is class imbalances in the data set, is calculated using the formula: \(2*Precision+Recall\). * Area Under the Receiver Operator Characteristics Curve (AUROC): is a graph used to evaluate the performance of a model at different thresholds of classification, usually allowing a greater number of positive predictions at at lower thresholds, thus increasing both false positives and true positive. The Receiver Operator Characteristics (ROC) curve is used to show two parameters: the true positive rate (TRP or recall) and the false positive rate (FPR) using the formula \(\frac{\#FP}{\#TP+\#TN}\). \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Actual**} \\ \cline{3-4}  & & **Positive** & **Negative** \\ \hline \multirow{2}{*}{**Prediction**} & Positive & TP & FP \\ \cline{2-4}  & Negative & FN & TN \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Theoretical confusion matrix between actual and predicted classes.
# Drill Hole Orientation: Its Role and Importance on the Compression Response of Pure Magnesium
## 3 Results and Discussions
### Experimental Results  
#### 3.2.1 Compression Testing  
Experimentally observed effects of drill hole orientation on the compressive response of magnesium are presented in Figure 9. The primary mechanical properties obtained from the stress-strain curves for different drill hole orientations, such as compressive yield strength (CYS) (based on 0.2% offset), ultimate compressive strength (UCS) (the highest stress taken) and failure strain, which are compared to monolithic magnesium, as shown in Table 2.  
For the vertical drill hole orientation, the CYS increased from \(65\pm 1\) MPa to \(105\pm 2\) MPa, a net 61% increase, and the UCS increased from \(297\pm 4\) MPa to \(330\pm 3\) MPa, a net 11% increase when compared to the undrilled samples. This shows an improvement in the load-bearing capacity of the sample.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Drill Hole Orientation**} & **Compressive Yield** & **Ultimate Compressive** & **Fracture Strain at UCS** \\  & **Strength (CYS) in MPa** & **Strength (UCS) in MPa** & **(\%)** \\ \hline Monolithic (No Hole) & \(65\pm 1\) & \(297\pm 4\) & \(19.1\pm 0.5\) \\ \hline \multirow{2}{*}{Vertical Drill Hole} & \(105\pm 2\) & \(330\pm 3\) & \(19.7\pm 0.6\) \\  & (\(\uparrow 61.53\%\)) & (\(\uparrow 11.11\%\)) & (\(\uparrow 3.14\%\)) \\ \hline \multirow{2}{*}{Angular Drill Hole} & \(133\pm 4\) & \(245\pm 3\) & \(11.5\pm 0.5\) \\  & (\(\uparrow 104.61\%\)) & (\(\downarrow 17.5\%\)) & (\(\downarrow 39.79\%\)) \\ \hline \multirow{2}{*}{Horizontal Drill Hole} & \(121\pm 5\) & \(224\pm 2\) & \(10.3\pm 0.3\) \\  & (\(\uparrow 86.15\%\)) & (\(\downarrow 24.6\%\)) & (\(\downarrow 46.07\%\)) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of experimental results of compressive properties of various drill orientations to that of monolithic magnesium.  
Figure 9: Experimental compression response of magnesium for different drill hole orientations.  
However, in the case of angular and horizontal drilled samples, while the CYS was found to increase by \(\sim\)105% and \(\sim\)86%, respectively, the UCS decreased by 18% and 25%, respectively. Further, compressive failure strains in angular and horizontal drill samples also exhibited a drop, as shown in Table 2. This implies that the component is not as stable as the vertical drill hole scenario.
## 3 Experimental Design of the LHD Machine Autonomous Walking
### Using DQN to Realize the Autonomous Walking of an LHD Machine
#### 3.1.1 Hidden Layer Number Determination Experiment  
One of the more important parameters in a DQN is the number of neurons in the hidden layer of the neural network. To achieve the best effect of the algorithm, experiments are carried out on the number of neurons at the hidden layer of different neural networks. If the number of neurons is too large, the over-fitting phenomenon is likely to occur after training, and the universality decreases. However, if the number is too small, the phenomenon of under-fitting can appear, which is of no practical significance for guiding the actions that should be taken in different states of the LHD machine. To determine the optimal number, test training was conducted on the simulated LHD machine in the straight lane. The expected walking path is shown in Figure 6. In the five groups of experiments designed, the other parameters remained unchanged, and only the number of neurons was changed. The experimental parameters are shown in Table 3.  
In the experiment, the action space was set as \(\overline{A}=\{a_{1},a_{2},a_{3},a_{4}\}\) straight, backward, left, and right, corresponding to \(a_{1}\), \(a_{2}\), \(a_{3}\), and \(a_{4}\), respectively. The state space was set as \(\overline{S}=\{s_{1},s_{2},s_{3},s_{4}\}\) speed, steering angle, distance from starting point, and distance from ending point, corresponding to \(s_{1}\), \(s_{2}\), \(s_{3}\), and \(s_{4}\), respectively. To avoid the LHD machine becoming too close to the tunnel, so that the LHD machine crosses the endpoint but is not considered to arrive endpoint, we set \(\epsilon=3\) m, \(\delta=1.7\) m. and made sure there were positive rewards after the LHD machine takes the correct behavior, otherwise the training of the LHD would not converge; thus, the reward rules during training are shown in the formula (7):  
\[\left\{\begin{array}{ll}R=-1&\\ R=R-1&if\;D_{l}<\delta\;or\;D_{r}<\delta\\ R=R+20&if\;s_{4}<\epsilon\\ R=R-10&if\;cashed\end{array}\right. \tag{7}\]  
where \(D_{l}\) and \(D_{r}\) are the distance from the left side and the distance from the right side, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group** & **Batch Size** & **Learning Rate** & **Reward Discount** & **Memory Capacity** & **Neurons** \\ \hline
1 & 32 & 0.01 & 0.9 & 100 & 5 \\
2 & 32 & 0.01 & 0.9 & 100 & 10 \\
3 & 32 & 0.01 & 0.9 & 100 & 15 \\
4 & 32 & 0.01 & 0.9 & 100 & 20 \\
5 & 32 & 0.01 & 0.9 & 100 & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter settings of the different groups.  
Figure 6: The optimal walking path of the LHD machine. The red point indicates the start location and the green point indicates the end location.
## 4 Performance Evaluation
### Simulation Results
#### 4.2.2 Transmission Point Change Rate  
Figure 4 shows the average transmission point change rate as a result of the transmission point selection at each time slot with different number of users (i.e., U = 1, 10, 20, 30,..., 100 users) during the simulation time, where a data packet with 4000 bytes is transmitted by the selected TP for each serving user. In particular, TP change includes from one TP to another TP, and from one serving beam to another serving beam, since we consider beamforming for FR2 and FR, as described in Section 4.1. \"Within FR (FR1 only)\", \"Within FR (FR3 only)\", and \"Within FR (FR3 only)\" denote the connection (i.e., TP and/or beam) selected in FR1, FR2, and FR3 may change within the initially selected FR1, FR2, and FR3, respectively, which are equivalent to TPS with single-connectivity in single-band wireless networks, e.g., [8,12]. \"Any FR among (FR1, FR2, FR3)\" also denotes that the connection can be changed within FR as well as across FR (e.g., from FR1 to FR3), which is equivalent to TPS with single-connectivity in multi-band wireless networks, e.g., [5]. \"Within FR (any FR)\", \"Across FR (any FR)\", and \"Within + Across FR (any FR)\" denote the connection (i.e., TP and/or beam) changes within the same FR, the connection (i.e., TP) changes across the FR, and any change within the same and across the FR, respectively. Note that the serving beam may change within TP or across TP and within/across FR. (i) The average TP change rates within FR2 and within FR3 are higher than that within FR1 for all cases. This is because beamforming covers narrow coverage in an angular domain  
Figure 3: The average received data rate with different number of users (U = 1, 10, 20,..., 100) in case of that (**a**) only one connection is allowed for a user (C = 1), (**b**) two connections are allowed for a user (C = 2), and (**c**) three connections are allowed for a user (C = 3).  
and the channel of mmWave and Sub-THz are unstable, which leads to (re-)selecting the TP and serving beams frequently. (ii) The average TP change rate across FR is higher than that within FR. We infer that multiple connections increase the time complexity to compare transmission points, but that multi-connection operation across multiple bands is beneficial for the system performance in terms of the received data rate for users, compared to the multi-connection operation with a single band, as explained in Section 4.2.1.
## 4 Measurement Results
The simulated and measured radiation patterns of the proposed CP 5G RDRA are presented in Figure 15. The radiation patterns were computed and measured at three different frequencies. The antenna provided stable radiation patterns with left-hand circular polarization, since the left-hand field component was greater than the right-hand field component by a margin of more than 20 dB at minimum AR frequency i.e., 4.43 GHz, as shown in Figure 15b. The right-hand CP response could be achieved by reversing the feeding strip. The simulated and measured gain of the CP antenna are shown in Figure 16. The antenna offers a useful gain of \(\sim\)6.2 dBic throughout the CP bandwidth. This high gain was achieved by excitation of the higher-order mode. A reasonable resemblance was observed between simulated and measured results. \begin{table}
\begin{tabular}{c c c c} \hline
**Excited Mode of 5G** & **DWM Estimation** & **CST Calculation** &
\begin{tabular}{c} **Experimental** \\ **Measurement** \\ \end{tabular} \\ \hline \(TE\) & \(f_{DWM}\) (GHz) & \(f_{CST}\) (GHz) & \(f_{Measurement}\) (GHz) \\ \hline \(\mathrm{TE_{S13}^{x}}\) & 3.89 & 4.13 & 4.12 \\ \hline \(\mathrm{TE_{S13}^{y}}\) & 4.53 & 4.52 & 4.51 \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison between predicted, simulated, and measured mode frequencies of the CP 5G RDRA. Figure 13: Simulated and measured axial ratio of the CP 5G RDRA. In Table 4, the proposed CP 5G RDRA is compared with recently reported RDRAs in the literature. It can be concluded that the proposed design configuration offers wide CP bandwidth achieved by excitation of the orthogonal higher-order mode pair, with  
Figure 14: Simulated and measured S\({}_{11}\) and AR overlapping bandwidths of the CP 5G RDRA. Figure 15: Simulated and measured radiation patterns of the CP 5G RDRA. (**a**) 4.4 GHz; (**b**) 4.43 GHz; (**c**) 4.7 GHz. very simple design geometry. Basic DR shape, i.e., rectangular, was used as radiating element along with the implementation of a simple feeding mechanism. Moreover, the field distribution inside the DRA was controlled by short adjacent magnetic dipoles. The spacing between these dipoles was responsible for the gain of the DRA, which could be improved by increasing the spacing. The spacing could be enhanced by excitation of the DRA in higher-order mode [28]. In literature, different efforts have been made to excite the RDRA in higher-order mode for high gain applications but the reported geometries are complicated and not easy to implement. In the proposed antenna higher-order orthogonal mode was excited using a new conformal feeding strip that was cut from an adhesive copper tape, which is a simple and cost-effective solution to the problem.
# Improving Energy Efficiency in LoRaWAN Networks with Multiple Gateways
## 5 EE-LoRa Algorithm
### Optimization Problem for SF Selection
#### 5.1.2 Convexity Study  
The constraints are linear and (\(\mathcal{E}\)) is affine. However, we need to study the convexity of (\(\mathcal{R}\)). Thus, we study the convexity of throughput \(\mathcal{S}(s)\) at each SF \(s\) that depends only on the number of nodes \(D(s)\). The second derivative of \(\mathcal{S}\) is given by \(4\lambda^{2}.b.ToA(s).(\lambda.D(s).ToA(s)-1)\).  
\(e^{-2.\lambda.D(s).ToA(s)}\). Hence, the condition to guarantee the concavity of \(\mathcal{S}\) is expressed by:  
\[\lambda.D(s).ToA(s)<1 \tag{22}\]  
Thus, the number of nodes \(D(s)\) at each SF \(s\) should verify this condition:  
\[D(s)<\frac{1}{\lambda.ToA(s)} \tag{23}\]  
For example, with the same packet generation rate \(\lambda=6\) packets/h and the same packet length \(k\) of 40 bytes for all nodes, we can deduce the maximum number of nodes at each SF to ensure the convexity of the optimization problem, as shown in Table 7. A network with 12,614 nodes with up to 5612 of them selecting SF 7 still works in the convexity domain. Therefore, the region that ensures the convexity of our optimization problem is large and sufficient in a realistic network.  
We will satisfy condition (23) in our study to guarantee that \(\mathcal{R}\) is concave, and therefore ensure the convexity of the optimization problem.