esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of IEEE802.15.4g for Environmental Observations
## 4 Analysis
### Urban Canyon Scenario
#### 4.3.1 RX at 540 m  
PDR values of all radio settings are depicted in Figure 11a for short packets. Overall, all PHYs have a high reliability, with values between 84% and 100% PDR. The exceptions are OFDM3-600 and OFDM4-300, with values of 21% and 60% PDR respectively. The reference 2 FSK-50 has 95% PDR. We do not highlight any since there are many PHYs with their PDR greater that the reference.  
For long packets, the PDR values are shown in Figure 11b. We can see the PDR values decay a few points, but still several PHYs with higher PDR that the reference (with 84% PDR). The two PHY with highest reliability with long packets are 2 FSK-FEC-100 and OFDM4-100 with 95% and 96% PDR respectively. Noticeably, O-QPSK radio settings have a lower PDR, even though their sensitivity is higher. Their low data rate makes the time needed to send each frame longer, increasing the probabilities of collision with other networks/technologies during transmissions.  
Figure 11c shows the maximum number of packets that can be sent under the 0.1% duty cycle regulation. OFDM1-800 stands alone with the possibility of sending 1485 packets. Some hundreds of packets behind, OFDM2-600 and OFDM2-800 with roughly 1115 packets. The reference 2 FSK-50 can send up to 158 packets per hour.  
Figure 11d shows the maximum number of long packets that can be sent per hour. Still, OFDM1-800 is the PHY that allows the maximum number of transmissions per hour, under the duty cycle regulation, with roughly 132 packets. OFDM2-600 allows 110 full packet length transmissions, while the reference only 9. Consequently, if the potential user wants to maximize the amount of packets until the duty cycle regulation limit, OFDM1-800 is shown to be the best for short and long packets.  
The average amount of electric charge used to transmit a 127 B packet for all the radio settings can be seen in Figure 11e. The lower electric charge consumption to get one packet across is achieved with OFDM1-800, spending 184 \(\upmu\)C. The reference 2 FSK-50 spends 1900 \(\upmu\)C, one magnitude higher.  
Concerning the electric charge consumption for long packets, Figure 11f shows these values. The tendency is maintained, OFDM1-800 is the most efficient radio setting to send a packet of 2047 B, with an average consumption of 2070 \(\upmu\)C. OFDM2-600 is close to this value, with 2507 \(\upmu\)C per 2047 B sent. 2 FSK-50 consumes, for the same transmission, around 32,760 \(\upmu\)C.
# On the Virtual Cell Transmission in Ultra Dense Networks
## 3 Transmission Schemes Based on Virtual Cell Merging
### Virtual Cell Merging with ZF and MVC-MMSE Transmission  
#### 3.1.1 Interference Graph Construction  
With each user's information symbol transmitted by equal power, the ergodic interference from an RBA to a user can be measured by the large-scale fading coefficient between them. Based on this observation, we propose an intuitive metric to describe the potential interference strength between two different virtual cells, which can be formulated as:  
\[\gamma_{j,k}=\gamma_{k,j}=\begin{cases}+\infty&\text{for }k=j\\ \frac{\left\|\lambda_{j,k}\right\|}{\left\|\lambda_{j,j}\right\|}+\frac{ \left\|\lambda_{k,j}\right\|}{\left\|\lambda_{k,j}\right\|}&\text{otherwise} \end{cases} \tag{7}\]  
where \(\gamma_{j,k}\) is the potential interference strength between initial virtual cells of user \(k\) and user \(j\) and \(\lambda_{k,j}\) is the large-scale fading vector from user \(j\)'s initial virtual cell to user \(k\). Generally, larger \(\gamma_{j,k}\) indicates more severe interference. We model each user's initial virtual cell as a vertex in an undirected graph \(\mathcal{G}\) where the edges are weighted by the potential interference strength between the vertexes. To determine whether two virtual cells belong to the same cluster, an interference strength threshold \(g_{th}\) is introduced. Based on \(\mathcal{G}\) and \(g_{th}\), we generate a binary graph \(\mathcal{G}_{b}\), which can be described by a \(K\) by \(K\) binary matrix \(\mathbf{T}\). We have:  
\[T_{k,j}=\begin{cases}1&\text{for }\gamma_{k,j}\geq g_{th}\\ 0&\text{otherwise}\end{cases} \tag{8}\]  
this implies that the vertexes corresponding to the initial virtual cells of user \(k\) and user \(j\) are connected in \(\mathcal{G}_{b}\) if and only if the interference strength exceeds the given threshold. Note that the infinite value of \(\gamma_{k,k}\) ensures that the initial virtual cell of user \(k\) is always self-connected.
## 2 Numerical Methodology
### Pebble Packing by DEM  
The pebble packing process is simulated by the DEM. In this method, each pebble is treated as a separated element. By the Hertz-Mindlin contact theory [32], the contact force between two touched particles is calculated. After applying the gravity and contact force on each pebble, the particles' motion can be simulated by solving [PERSON]'s second law of motion. The position, velocity, contact force, contact state, etc., can be obtained and updated at each time-step.  
During the pebble packing process, the pebble motion can be expressed as the following equations:  
\[m_{i}\frac{\mathrm{d}V_{i}}{\mathrm{d}t}=\sum_{j=1}^{N_{\mathrm{cr}}}\left(F _{n_{ji}}+F_{t_{ji}}\right)+m_{i}\mathcal{G} \tag{1}\]  
\[I_{i}\frac{\mathrm{d}\omega_{i}}{\mathrm{d}t}=\sum_{j=1}^{N_{\mathrm{cr}}}r_{ ij}\times F_{t_{ji}} \tag{2}\]  
where \(V_{i}\) and \(\omega_{i}\) are the velocity of the translational and rotational movements of pebble \(i\), respectively. \(m_{i}\) and \(I_{i}\) are the pebble mass and the motion of inertia. \(N_{\mathrm{cr}}\) is the number of surrounding pebbles. \(F_{n}\) and \(F_{t}\) are normal and tangential contact forces between two touched pebbles. \(F_{g}\) is the gravity force. \(r_{ij}\) is the vector pointing from the pebble \(i\) to pebble \(j\). Under the influence of the friction between two pebbles, the normal contact force and the tangential contact force satisfy \(\mid\!F_{t}\!\mid_{max}\leq\!\eta_{\
eq i}\mid\!F_{t}\!\mid\!_{1}\), where \(\eta_{\
eq i}\) is the friction coefficient.  
Based on the Hertz-Mindlin [32] contact theory, \(F_{n}\) and \(F_{t}\) can be defined as follows:  
\[F_{n}=k_{n}\delta_{n_{ij}}-\eta_{n}v_{n_{ij}}\text{ and }F_{t}=k_{t}\delta_{t_{ ij}}-\eta_{t}v_{t_{ij}}. \tag{3}\]  
For normal contact force, \(k_{n}\)_is_ the elastic constant of normal contact; \(\eta_{n}\) is the normal viscoelastic damping coefficient. \(\delta_{n_{ij}}\) is the overlap of two normal contact pebbles. \(v_{n_{ij}}\) is the normal relative velocity of two pebbles. \(k_{n}\) and \(\eta_{n}\) can be expressed as  
\[k_{n}=\frac{4}{3}E^{*}\sqrt{R^{*}\delta_{n_{ij}}}\text{ and }\eta_{n}=-2\sqrt{ \frac{6}{5}}\mathcal{\beta}\sqrt{S_{n}m^{*}} \tag{4}\]  
Figure 1: Flow chart of the simulation process by combining DEM and CFD modeling.  
where \(S_{n}=2E^{*}\sqrt{R^{*}\delta_{n_{ij}}}\), \(\eta_{n}\geq 0\).  
For tangential contact force, \(k_{t}\) is the elastic constant of tangential contact; \(\eta_{t}\) is the tangential viscoelastic damping coefficient. \(\delta_{t_{ij}}\) is the tangential relative displacement vector of two contact pebbles. \(v_{t_{ij}}\) is the tangential relative velocity of two pebbles. \(k_{t}\) and \(\eta_{t}\) can be shown as  
\[k_{t}=8G^{*}\sqrt{R^{*}\delta_{t_{ij}}}\text{ and }\eta_{t}=-2\sqrt{\frac{6}{ \overline
## 6 Results and Discussion
### Experimental Results
#### 6.2.2 Classification Results
[4] in terms of accuracy, sensitivity and specificity scoring an increase of 2.5%, 1.6% and 0.8% respectively. In addition, compared to [PERSON] et al. [20], the proposed HyCAD model achieves a noticeable increase in sensitivity by 1.8%, a comparable specificity and a slight increase in accuracy. HyCAD architecture attains the highest sensitivity, scoring an increase of 1.6% compared to the best model of the state of art models [4; 20] to reach 99.4% for the Urgent referral group. Such a high sensitivity is required for this group as they represent the critical group, which needs immediate attention. This improvement is achieved while maintaining a competitive specificity with the state of the art. Another important aspect to study is the performance of our HyCAD architecture using hybrid integration of Norm-VGG16 CNN features and hand-crafted features in case of binary classifications. Hence, multiple binary models of CNV vs. NORMAL, DME vs. NORMAL and DRUSEN vs. NORMAL classes are trained and achieve significant results. The respective confusion matrices are shown in Figure 17 and the results are compared to [PERSON] et al. [4] and [PERSON] et al. [20] in Table 7. The two binary models CNV vs. NORMAL vs. DME and NORMAL achieve an accuracy of 100%, sensitivity of 100% and specificity of 100% without any wrong classification of 500 testing set as can be seen from the confusion matrix in Figure 17A,B. The binary model between DRUSEN vs. NORMAL achieves an accuracy of 99.79%, sensitivity of 99.6% and specificity of 100% by classifying only one wrong image as shown in Figure 17C The CNN features are extracted by training Norm-VGG16 for only 15 epochs with learning rate starting with 0.001. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{3}{c}{**CNV vs. NORMAL**} & \multicolumn{3}{c}{**DME vs. NORMAL**} & \multicolumn{3}{c}{**DRUSEN vs. NORMAL**} \\ \cline{2-10}  & **Acc** & **Sn** & **Sp** & **Acc** & **Sn** & **Sp** & **Acc** & **Sn** & **Sp** \\  & **(CNV)** & **(CNV)** & **(DME)** & **(DME)** & **(DME)** & **Acc** & **(DRUSEN)** & **(DRUSEN)** \\ \hline [PERSON] et al. 2018 [4] & 100\% & 100\% & 100\% & 98.2\% & 96.8\% & 99.6\% & 99\% & 98\% & 99.2\% \\ \hline [PERSON] et al. 2019 [20] & 100\% & 100\% & 100\% & 98.8\% & 98.8\% & 99.2\% & 98.4\% & 100\% \\ \hline
**HyCAD (Proposed Model)** & **100\%** & **100\%** & **100\%** & **100\%** & **100\%** & **100\%** & **99.79\%** & **99.6\%** & **100\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison between binary classifiers of HyCAD architecture, [PERSON] et al. [4] and [PERSON] et al. [20] models. Figure 16: **(A)** HyCAD architecture Confusion Matrix **(B)** [PERSON] et al. model [20] Confusion Matrix **(C)** [PERSON] et al. [4] Confusion Matrix. Figure 17: HyCAD architecture binary classifier **(A)** CNV vs. NORMAL **(B)** DME vs. NORMAL **(C)** DRUSEN vs. NORMAL.
## 2 Materials and Methods
### Improvement of Fiber Feeder System through Spreading  
Approximately 126 strands of 24K tows are required to produce CFRP bars with a hollow rectangular cross-section of 40 \(\times\) 10 \(\times\) 2 mm. The arrangement of initially fed fibers is critical for forming this shape since bars cannot be produced in the desired shape with an uneven tow arrangement. An uneven fiber arrangement may produce a cross-section that is too thick on one side or bars with a rough surface. Dry fiber spreading is a method of widening and flattening fiber bundles by mechanically spreading them with several geometric rolls before the resin impregnation. This method employs either cylinder pins or a combination of geometric rolls, such as oval, angular, and cylinder. In the case of using only cylinder pins, previous studies show that the cylinder distance and height are variables of the fiber spreading [19; 20; 21]. [PERSON] identified that, as fibers were passed through cylinder rolls, the width of the fibers was dictated by the following relationship:  
\[w=\left(12 AH\right)^{1/3} \tag{1}\]  
Figure 1: Comparison of (**a**) open bath and (**b**) closed impregnation method.  
Figure 2: (**a**) Closed impregnation system and (**b**) final curing mold of the system.  
where \(w\) is the width of fibers, \(A\) is the cross-sectional area of fiber tows, and \(H\) is the height difference between fins [20].  
In the conventional fiber feeder system with three guide parts, which prioritizes only the arrangement of the fibers, fibers are rolled up into a circular cross-section shape at the final guide, as shown in Figure 3a. Since the circular cross-section shape results in an uneven surface of CFRP bar, this system was improved by the following arrangement: the first guide holds the fibers, and the second and third roller guides spread the fibers while maintaining their arrangement, as shown in Figure 3b. The width of the 24K tow fibers is 7 mm, and roller guides are installed at intervals of 50 mm. Based on Equation (1), roller guides that were theoretically capable of spreading fibers from a width of 7 mm up to 34.7 mm were fabricated to maintain a constant surface evenness of the fibers entering the impregnation device.
# Decision Support System for Predicting Mortality in Cardiac Patients Based on Machine Learning
## 3 Validation and Evaluation  
The holdout validation approach is commonly used in the literature to investigate the effectiveness of ML-based diagnostic systems [31]. In a holdout validation approach, a dataset is split into two halves, with one half used for training and the other half used to test the proposed ML model. The dataset is split into 70% for training the ML model and 30% for testing [32; 33]. Therefore, our experiments used the above data partitioning criteria for training and evaluating the developed \(\chi^{2}\)_RF model. After data partitioning, we establish evaluation measures to compare the performance of the proposed model with existing state-of-the-art ML cardiac mortality prediction models. The evaluation criteria for the \(\chi^{2}\)_RF model are accuracy, precision, recall, F1 score, [PERSON]'s correlation coefficient (MCC) [34], and the area under the curve (AUC) [35] using the ROC curve [36; 37]. The evaluation metrics are mathematically presented as:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{8}\]  
In this equation, _TP_ stands for the number of true positives, _FP_ stands for the number of false positives, _TN_ stands for the number of true negatives, and _FN_ stands for the number of false negatives.  
\[Precision=\frac{TP}{TP+FP} \tag{9}\]  
\[Recall=\frac{TP}{TP+FN} \tag{10}\]  
\[F1\_score=\frac{2 TP}{2 TP+FP+FN} \tag{11}\]  
\[MCC=\frac{TP\times TN-FP\times FN}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}} \tag{12}\]  
The statistical analysis was performed for a binary classification problem [38; 39]. In this context, the F1 score was used as a measure of F. The F-score ranges from 0 to 1, where 1 represents the best predictions and 0 represents the worst predictions. MCC allows us to determine whether a test is reliable or not. MCC can take values between +1 and \(-1\), where +1 stands for the best and \(-1\) for the worst prediction.
## 2 Materials and Methods
### Three-Dimensional Reconstruction Methods  
The information obtained from the two-dimensional CT images of the rock samples is quite limited and only allows for a basic qualitative analysis. Avizo 2020.1 software is a professional 3D visualization modeling software. It is capable of completing 3D visualization modeling based on CT images. The software provides a large number of data types and modules to quickly complete the quantitative analysis of 3D model data. This paper mainly applies the ortho slice command, interactive thresholding command, volume rendering command, label analysis command, and fractal dimension command in Avizo 2020.1. These commands are used to extract the fracture of rock samples, reconstruct the fracture in three dimensions, count the fracture parameters, and calculate the three-dimensional fractal dimension. The reconstruction process is illustrated in Figure 5.  
Figure 4: Stress–strain curves of rock samples in three states of damage without confining pressure.  
The three-dimensional reconstruction process using Avizo 2020.1 software involves the following steps:  
1. Importing Images: First, the images obtained from the CT scan are imported into the software. Since the tape used to wrap the samples during scanning can introduce artifacts, the images are cropped to remove these artifacts and minimize their impact on the results.
2. Noise Reduction: Due to external noise during scanning, the original images may contain noise. To enhance image quality for subsequent analysis, the grayscale values are adjusted, and filtering is applied to reduce noise.
3. Threshold Segmentation: Accurate segmentation of different phases in the image is crucial for three-dimensional reconstruction. Common algorithms for threshold segmentation include watershed segmentation, global thresholding, and region growing. In this study, Avizo 2020.1 software's built-in watershed algorithm is used, which is effective in accurately identifying voxels at the boundaries where different phases intersect. Additionally, there is a linear relationship between the grayscale value and the density of mineral components: higher brightness indicates higher density. In the scanned two-dimensional images, brighter regions correspond to mineral components (such as amphibole, feldspar, quartz, etc.), while darker regions indicate fissures. During processing, the grayscale values are used to differentiate between fissures and mineral matrix.
4. Three-Dimensional Rendering: After threshold segmentation, the original images are classified into two phases (mineral matrix and fissures). The software then renders and visualizes the rock samples in three dimensions, showcasing the internal structure and features.
## 4 Simulation Results and Discussion
### Simulation Results
Although estimation errors exist between the real external disturbance and estimated value, they can be as low as possible by the approximate selection of observer parameters. The model estimation result of adaptive NNs law is shown in Figure 3.2 With the RBFNNs, the developed control approach can provide a good tracking performance for pneumatic vehicle suspension. Simulation results show that the proposed control with the PPF is able to satisfy the performance requirements of the pneumatic suspension more than the traditional backstepping control. Figure 3.3 Estimation of \(x_{1}\) and estimation error. Figure 3.4 Estimation of \(x_{2}\) and estimation error. Figure 3.5 Estimation of \(x_{5}\) and estimation error. Figure 3.6 Adaptive law.
# Response of Global Terrestrial Carbon Fluxes to Drought from 1981 to 2016
## 3 Results  
### Validation of Simulated Monthly Terrestrial Carbon Fluxes  
The simulated monthly GPP, ER, and NEP were compared with eddy covariance tower data from the FLUXNET 2015 database across different ecosystem types (Figure 1). The model captured 71%, 73%, and 23% of variation in monthly GPP, ER, and NEP for 1320 site-months measurements at eddy covariance sites, respectively, indicating the applicability of the BEPS model in estimating carbon fluxes for different ecosystems across the globe, although in some cases the model underestimated or overestimated the measured values.  
To verify whether the performance of the BEPS model differs in months with and without drought, all site-months were classified into two groups according to values of SPEI. The coefficient of determinations, R\({}^{2}\) of simulated GPP, ER, and NEP against tower-based values were 0.70, 0.74, and 0.21 for 687 site-months with drought conditions (SPEI < 0) (red squares in Figure 1), respectively. As to the 633 site-months without drought (SPEI > 0) (blue circles in Figure 1), the R\({}^{2}\) values of simulated GPP, ER, and NEP against tower-based values were 0.73, 0.73, and 0.26, respectively. Therefore, whether in drought or non-drought conditions, the BEPS model could simulate the terrestrial carbon fluxes with good accuracy, and more importantly, the simulation ability was similar.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Site-ID** &
\begin{tabular}{c} **Vegetation** \\ **Type** \\ \end{tabular} & **Latitude** & **Longitude** & **Years** \\ \hline AU-Cpr & SAV & \(-\)34.0021 & 140.5891 & 2011–2013 \\ AU-Rob & EBF & \(-\)17.1175 & 145.6301 & 2014 \\ AU-Wac & EBF & \(-\)37.4259 & 145.1878 & 2008 \\ BR-Sa1 & EBF & \(-\)2.8567 & \(-\)54.9589 & 2002–2003 \\ BR-Sa3 & EBF & \(-\)3.018 & \(-\)54.9714 & 2001,2003 \\ CN-Cha & MF & 42.4025 & 128.0958 & 2003 \\ CN-Dan & GRA & 30.4978 & 91.0664 & 2004–2005 \\ CN-HaM & GRA & 37.37 & 101.18 & 2003–2004 \\ DE-Geb & CRO & 51.1001 & 10.9143 & 2002–2014 \\ DE-Kli & CRO & 50.8929 & 13.5225 & 2006–2007,2010–2012,2014 \\ FI-Hyy & ENF & 61.8475 & 24.295 & 1997–1999,2001–2004, \\  & & & 2006–2009,2011–2014 \\ GF-Guy & EBF & 5.2788 & \(-\)52.9249 & 2004–2014 \\ IT-CA2 & CRO & 42.3772 & 12.026 & 2012–2013 \\ MY-PSO & EBF & 2.973 & 102.3062 & 2003,2007–2008 \\ US-Blo & ENF & 38.8953 & \(-\)120.6328 & 2000 \\ US-CRT & CRO & 41.6285 & \(-\)83.3471 & 2011–2013 \\  & & & 2002–2006,2008–2009, \\ US-MMS & DBF & 39.3232 & \(-\)86.4131 & 2012–2014 \\ US-Me2 & ENF & 44.4523 & \(-\)121.5574 & 2005,2009 \\ US-Me5 & ENF & 44.4372 & \(-\)121.5668 & 2001 \\ US-NR1 & ENF & 40.0329 & \(-\)105.5464 & 2000–2014 \\ US-Ne3 & CRO & 41.1797 & \(-\)96.4397 & 2002–2012 \\ US-SRC & OSH & 31.9083 & \(-\)110.8395 & 2009–2011 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Site ID, latitude, longitude, vegetation types, and years of available data for the 22 FLUXNET sites used for validating BEPS model.
## 1 Introduction
Consequently, it is plausible that partially replacing high-melting-point materials with low-melting-point materials may also contribute to reducing the sintering temperature of ceramics. Given that the melting point of CuO is significantly lower than that of ZnO, and the ionic radius of Cu\({}^{2+}\) (0.73 A) is comparable to that of Zn\({}^{2+}\) (0.74 A), with a difference of less than 15%, Cu\({}^{2+}\) substitution emerges as a viable approach to achieve low-temperature sintering. Furthermore, low-melting-point compounds have frequently been employed as potent sintering aids, significantly decreasing the sintering temperature of microwave dielectric ceramics to below 960 \({}^{\circ}\)C [12; 15; 16; 17; 18]. These additive oxides not only melt during the sintering process but also interact with the constituent elements of the matrix ceramics, thereby facilitating ceramic densification through the mechanism of liquid-phase sintering [15]. Among the various sintering aids, LiF, with a melting point of 845 \({}^{\circ}\)C, is regarded as one of the most effective and economical additives for lowering the sintering temperature of ceramics [16; 17; 18]. For example, the sintering temperature of Li\({}_{2}\)Mg\({}_{0.2}\)Zr\({}_{0.32}\) ceramics, which exhibit the optimal microwave dielectric properties, can be reduced from 1350 \({}^{\circ}\)C to 950 \({}^{\circ}\)C with the addition of 2 wt.% LiF [17]. Similarly, the introduction of 8 wt.% LiF lowered the sintering temperature of Li\({}_{3}\)Mg\({}_{3}\)NbO\({}_{7}\) ceramics from 1200 \({}^{\circ}\)C to 900 \({}^{\circ}\)C [18]. In this study, a series of novel Cu\({}_{x}\)Zn\({}_{1-x}\)Ti\({}_{0.2}\)Zr\({}_{0.8}\)Nb\({}_{2}\)O\({}_{8}\) ceramics, with compositions ranging from \(x\) = 0.0 to \(x\) = 0.4, were synthesized using the conventional solid-state reaction method. Specifically, Cu\({}_{0.3}\)Zn\({}_{0.7}\)Ti\({}_{0.2}\)Zr\({}_{0.8}\)Nb\({}_{2}\)O\({}_{8}\) (i.e., \(x\) = 0.3) was selected for further investigation due to its relatively optimal performance within this series, and varying amounts of LiF were introduced into this composition to strive for a sintering temperature below 960 \({}^{\circ}\)C. A meticulous examination was then conducted to explore in depth the microstructural alterations and microwave dielectric properties of the LiF-doped Cu\({}_{0.3}\)Zn\({}_{0.7}\)Ti\({}_{0.2}\)Zr\({}_{0.8}\)Nb\({}_{2}\)O\({}_{8}\) ceramics. Additionally, the chemical compatibility of the ceramics with a silver electrode was evaluated. The findings of this work underscore a promising approach for the development of \(M^{2+}N^{4+}Nb_{2}O_{8}\)-type ceramics that not only exhibit low sintering temperatures but also possess exceptional microwave dielectric characteristics.