esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Classification of MAC Protocols Based on Channel
### Cooperative Multi-Channel MAC Protocols
#### 4.7.3 Oca-Mac  
The optimal cooperative ad hoc (OCA) MAC protocol was proposed to provide cooperative communication to improve transmission probability. The optimal cooperative node was chosen based on TDMA for successful transmission. A probabilistic model was designed to know the number of optimal cooperative nodes in each channel. The methods of cooperative forwarding and optimal node determination were used for reliable communication. The cooperative transmission mechanism was employed when the transmission rates go below the threshold. The node broadcast control frame periodically contains information about it such as node ID, neighbor node ID, and slot reserved. Choosing an optimal cooperative node is based on the distance calculated between the potential cooperative node and the mid-point between the sender and receiver, thereby increasing the network throughput. The node with a minimum value of distance calculated is chosen as the optimal cooperative node. To analyze the number of cooperative nodes, a probabilistic mathematical model was designed. In this model, the time slots are divided into free time slots, successful time slots, and failed time slots.  
The protocol provides QoS in terms of successful transmission rate, transmission delay, and transmission in highway scenarios. Since the protocol does not consider the dynamic topology scenario, there might be performance degradation in dynamic scenarios.
# Unidirectional Slow Light Transmission in Heterostructure Photonic Crystal Waveguide
## 2 Theoretical Background  
The group velocity \(v_{g}\) (or equivalently the group index \(n_{g}=c/v_{g}\), \(c\) denotes the speed of light in vacuum) is not the only key parameter to evaluate the performance of slow light devices. We should also take into account the frequency bandwidth, group velocity dispersion (GVD), and the propagation loss. The frequency bandwidth and the group index are encapsulated by the normalized delay-bandwidth product (NDBP) [16; 39]. The NDBP indicates the obtained compromise between a large operating bandwidth and a large group index, as given by  
\[\text{ND}B{P}=\widetilde{n}_{g}\times\frac{\Delta\omega}{\omega_{0}}, \tag{1}\]  
\(\widetilde{n}_{g}=\int_{\omega_{0}}^{\omega_{0}+\Delta\omega}n_{g}(\omega) \frac{\mathrm{d}\omega}{\Delta\omega}\) is an average group index in the desired slow light bandwidth. \(\Delta\omega/\omega_{0}\) denotes the normalized frequency bandwidth, where \(\Delta\omega\) represents the difference between the maximum and the minimum frequency in the slow light region and \(\omega_{0}\) represents the center frequency. The reduction of \(v_{g}\) should be balanced with the bandwidth requirements in practical applications. The frequency bandwidth is considered as the frequency range corresponding to a \(\pm 10\%\) group index variation with respect to the obtained mean \(\widetilde{n}_{g}\) value [16; 39; 40].  
The group velocity is strongly dependent on the operation frequencies [41; 42], as quantified by the second-order derivative of the dispersion relation:  
\[\beta_{2}=\frac{\mathrm{d}^{2}k}{\mathrm{d}\omega^{2}}=\frac{1}{c}\frac{ \mathrm{d}n_{g}}{\mathrm{d}\omega}, \tag{2}\]\(\beta_{2}\) is the GVD parameter. Large \(\beta_{2}\) can lead to pulse distortion, and thereby degrades the performance of the slow light device. Taking the practical application requirements into consideration, the GVD parameter should below the order of \(10^{6}\left(\frac{a}{2\pi c^{2}}\right)\)[43; 44] (\(a\) is the lattice constant in PC systems).  
The propagation loss, mainly due to light scattering, is the most critical limitations to be solved in slow light systems. The scattering loss is mainly due to random fabrication imperfections or environmental changes. It increases quadratically with the reduced \(v_{g}\) as proposed theoretically in [19] and certified experimentally in [20]. If the propagation loss penalty is too high, the superiority of slow light might be compromised. To overcome this crucial limitation in slow light systems, we propose a backscattering-immune slow light waveguide as demonstrated in the followed sections.
## 2 Mathematical Models
### Three-Phase Relative Permeability Hysteresis Model
\[k_{rg}^{\text{\emph{hyst}}}=k\Big{(}1-\big{(}S_{L}+S_{gt}\big{)}^{\alpha} \Big{)}^{4}_{rgmin} \tag{87}\]  
where \(k_{rg}^{\text{\emph{hyst}}}\) is the gas phase hysteresis relative permeability, \(S_{L}\) is the liquid saturation, \(S_{gt}\) is the trapped gas saturation, \(\alpha=\frac{1}{2-D_{L}}\), where \(D_{L}\) is the linear fractal dimension of the porous medium and \(S_{L}\) is the total liquid saturation. When \(K_{\text{rg}}^{\text{hyst}}(S_{\text{g}})=K_{\text{rg}}\Big{(}S_{\text{gt}} \Big{)}\), similar results to [PERSON]'s are obtained, but by calculating the trapped gas saturation, hysteresis effects can be fully accounted for. The relative permeability of oil is also derived by considering the apparent saturation. As shown above, \(S_{\text{L}}\) becomes \(S_{L}+S_{gt}\), and \(S_{w}\) becomes \(S_{w}+(1-a)S_{gt}\). Then:  
\[k_{ro}=k_{ro}^{2 Ph}\Big{[}\big{(}S_{L}+S_{gt}\big{)}^{\beta}-\big{(}S_{w}+(1-a) S_{gt}+(S_{or})_{S_{gt}=0}\big{)}^{\beta}\Big{]} \tag{88}\]  
where \(\beta=(4~{}-~{}\text{DL})/(2~{}-~{}\text{DL})\), \(S_{gt}\) is the trapped gas saturation, \(k_{ro}^{2 Ph}\) is the oil relative permeability during water/oil imbibition tests, and \(a\) is the residual oil saturation reduction factor. When reaching \(S_{or}\), this expression ensures that \(k_{ro}\) equals zero. If a portion of the trapped gas contributes to a higher apparent saturation of water, its presence would influence the flow behavior of the wetting phase. In the following expression, it is assumed that the flowing \(S_{w}\) is deprived of a quantity proportional to the saturation of gas trapped with water:  
\[k_{ro}=\bar{S}_{of}^{1/2}\Bigg{[}\bigg{(}1-\bar{S}_{w}^{1/m}\bigg{)}^{m}-\bigg{(} 1-\bar{S}_{t}^{1/m}\bigg{)}^{m}\bigg{(}1-\frac{\overline{S}_{gro}}{1-\overline{ S}_{t}^{min}}\bigg{)}-\Big{(}1-\bar{S}_{t}^{1/m}\bigg{)}^{m}\frac{\overline{S}_{gro}}{1- \overline{S}_{t}^{min}}\bigg{]}^{2} \tag{80}\]0  
where R is the reduction factor for permeability. (4) [PERSON]. [PERSON] Model [43]  
[PERSON] and colleagues proposed a consistent procedure for calculating three-phase relative permeability (3 PRP) and capillary pressure (3 PCP), known as the Rescaled Interpolation Method (RIM). The method starts with the use of Baker saturation-weighted interpolation for three phases, but modifies the oil phase by rescaling saturation to be compatible with the general form of the residual saturation function \(S_{om}\big{(}S_{g},S_{w}\big{)}\). It is assumed that the hysteresis of each phase depends on whether the phase is increasing or decreasing, independent of the state of other phases.
# Effect of Cation Influx on the Viability of Freeze-Dried _Lactobacillus brevis_ WiKim0069
###### Abstract  
Extension of the storage stability of freeze-dried lactic acid bacteria is important for industrialization. In this study, the effect of cation influx from soy powder, which contains high amounts of cations, as a cryoprotective agent on the viability of freeze-dried _Lactobacillus brevis_ WiKim0069 was tested. Compared to that in the absence of the soy powder, bacterial viability was significantly higher in the presence of soy powder. Approximately 4.7% of _L. brevis_ WiKim0069 survived in the absence of the protective agent, whereas 92.8% viability was observed in the presence of soy powder. However, when cations were removed from the soy powder by using ethylenediaminetetraacetic acid (EDTA) and a cationic resin filter, the viability of _L. brevis_ WiKim0069 decreased to 22.9-24.7%. When the soy powder was treated with ethylene glycol tetraacetic acid, the viability was higher than when it was pretreated with EDTA and a cationic resin filter, suggesting that Mg\({}^{2+}\) had a role in enhancing the viability of _L. brevis_ WiKim0069. Cold adaptation at 10 \({}^{\circ}\)C prior to freeze-drying had a positive effect on the storage stability of freeze-dried _L. brevis_ WiKim0069, with 60.6% viability after 56 days of storage. A decrease in the fluorescence polarization value indicated an increase in membrane fluidity, which regulates the activity of ion channels present in the cell membrane. Cold adaptation caused activation of the cation channels, resulting in increased intracellular influx of cations, i.e., Ca\({}^{2+}\) and Mg\({}^{2+}\). These results suggest that cold adaptation can be used to improve the storage stability of _L. brevis_ WiKim0069.  
Lactobacillus brevis WiKim0069; viability; storage stability; cation; membrane fluidity +
Footnote †: journal: Applied Physics Letters  
1R&D Division, World Institute of Kimchi, Gwangju 61755, Korea; [EMAIL_ADDRESS] (I.S.C.); [EMAIL_ADDRESS] (H.M.K.); [EMAIL_ADDRESS] (H.H.C.); [EMAIL_ADDRESS] (I.M.H.)  
Asian Pear Research Institute, Chonnam National University, Gwangju 61186, Korea; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +82-62-610-1728  
+ These authors contributed equally to this work.
## 2 Detailed Model of MMC-Controlled Wind Turbine Generator  
Figure 1 shows the configuration of the MMC-controlled wind turbine generator in the detailed model. The wind turbine generator is a gearless system in which the wind turbine rotor directly drives the rotor shaft of the PMSG. The PMSG is a multipoles type of generator that operates at variable voltage and frequency. The electrical power produced by the generator is supplied to the grid system with a constant voltage and frequency through a back-to-back converter. The back-to-back converter is formed from two MMCs, namely a stator-side MMC and a grid-side MMC, which are linked by a DC link circuit. The three-phase AC voltage of the PMSG stator winding is rectified by the stator-side MMC, and the DC voltage of the DC link circuit is inverted to AC voltage by the grid-side MMC. The stator-side MMC is connected to the stator winding terminal of the PMSG, and the grid-side MMC is connected to the grid system through a step-up transformer (TR). On the stator-side MMC, \(I_{S}^{(abc)}\) and \(V_{S}^{(abc)}\) are, respectively, three-phase currents and voltages from the stator winding, and \(\omega_{r}\) is the rotational speed of the rotor shaft of the PMSG. On the grid-side MMC, \(I_{G}^{(abc)}\) and \(V_{G}^{(abc)}\) are, respectively, three-phase currents and  
voltages from the terminal of the grid-side MMC. Each of the MMC systems is equipped with the main MMC controller, inner MMC controller, and the Phase-Shifted Pulse Width Modulation (PS-PWM) circuit. The DC link circuit is configured by two capacitors (\(C_{dc}\)) arranged in series. The DC link circuit is also equipped with an over-voltage protection system controlled by a DC chopper. For more details, each part of the detailed model of the MMC-controlled wind turbine generator can be explained as follows.
## 5 Sensor Faults and Outlier Detection Models for IoTs
### Statistical Techniques  
Statistical-based techniques were the first algorithms used for sensor faults detection and outlier detection by many researchers. In this technique, the data from the sensors are modelled by employing a stochastic distribution. The data points from sensors can be identified as outliers or faults when the likelihood of the data instance that is generated by this model is very low. This technique uses previous sensor measurements to approximate and build a model of the accurate behaviour of a sensor. However, whenever the new measurement from the same sensor is registered, this data point is then compared to the model to check if the new data point statistically incompatible with the model. If the model is incompatible with new sensor reading, it is then marked as outlier or a faulty measurement.  
Figure 4: Sensor outlier sources in IoT.  
A statistical window based approach typically assists in decreasing the number of false positives of the faults and outliers. The low-high pass filter is an example of the basic statistical technique that classifies the sensor readings as faults or anomalies based on working out the average of past measurements and measuring how different the new readings are.  
A statistical technique based on the spatio-temporal data interdependence's of sensor data is proposed by [PERSON] et al. [30]. This technique mostly utilises two statistical tests, to locally detect outliers to make simple aggregation processes more reliable. Statistical models are relevant to quantitative data sets of real-value or at the very least is a quantitative data distribution that needs to be converted to a suitable numerical value for the numerical treatment. As the complexity and the amount of the sensor data increases (as it is common in the case of IoT), this model will take a longer time processing to convert the complex data.
# CA-CGNet: Component-Aware Capsule Graph Neural Network for Non-Rigid Shape Correspondence
## 4 Experiments and Evaluation
### Ablation Study
#### 4.4.2 Effectiveness of the Component-Aware Graph Routing  
In Section 3.3, component-aware graph routing is proposed to compute the primary capsule to transfer the input vector to the latent capsule. To test the effectiveness of the routing algorithm, we compare the proposed algorithm with dynamic routing. Table 5 shows the average geodesic error of different routing algorithms on the TOSCA dataset. The bold represents the minimum error in each column. As shown in Table 5, the average geodesic error of the dynamic routing algorithm is 0.1276, and the average geodesic error of our method without knowledge distillation is 0.1213. Our method improves the correspondence performance with an average error of 0.1187, which is 6.97% less than the dynamic routing and 2.14% less than our method without knowledge distillation. Our model achieves smaller average geodesic errors on different types of meshes, providing slightly better results on the TOSCA dataset. It illustrates that the routing strategy in this paper can capture more adequate pose information compared to dynamic routing, resulting in significant improvement in shape correspondence.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & **Dynamic** & \multicolumn{2}{c}{**Ours without**} \\  & **Routing** & **Knowledge Distillation** & **Ours** \\ \hline \hline  & 0.1153 & 0.1193 & 0.1186 \\ \hline  & 0.1295 & 0.1136 & 0.1129 \\ \hline  & 0.0644 & 0.0612 & 0.0598 \\ \hline  & 0.1058 & 0.0949 & 0.0941 \\ \hline  & 0.1824 & 0.1698 & 0.1605 \\ \hline  & 0.1625 & 0.1579 & 0.1572 \\ \hline  & 0.1137 & 0.1182 & 0.1174 \\ \hline  & 0.1471 & 0.1354 & 0.1287 \\ \hline \hline  & & & \\ \end{tabular}
\end{table}
Table 5: Comparison of average geodesic error (cm) on remeshed TOSCA dataset using different routing strategy.  
Figure 14: Visualization of geodesic error of several methods on original FAUST dataset.  
In order to fully capture the features of the potential space, inter-layer distillation and output-layer distillation are used to transfer the model's knowledge. To verify the effectiveness of the knowledge distillation strategy, we train the CA-GCNet and the CA-GCNet without a knowledge distillation strategy separately to acquire the network accuracy, as shown in Figure 15. The accuracy curves illustrate that the prediction accuracy of our proposed CA-CGNet is higher than that of the CA-CGNet without the knowledge distillation strategy, which indicates that the student network can learn more detailed features from the teacher network.
## 4 Discussion  
Digital and complex medical data are available from various medical specialities. For patients with tumours, for example, there are molecular data, and radiological and pathological image data [10; 29]. The analysis of these vast data from one field--or, better, combined--leads to an opportunity to find next-generation, data-driven biomarkers [29]. In pathology, Pathomics is the subdiscipline dedicated to mapping image data to clinical information such as nodal status. In other words, new image-based biomarkers are being sought in Pathomics. In this context, the segmentation of histological images (e.g., in tumour and stroma) is an early and major step in many projects [1; 4]. For supervised segmentation approaches, the scarcity of large, properly annotated datasets is a common obstacle [3; 7; 30]. Not only is annotation tedious per se, but in many cases, the number of available images is limited. For example, managing thymoma, a rare disease, significantly limits the number of cases to be included [31]. Data augmentation techniques alone have usually not solved the problem of small numbers [7; 10]. In addition to supervised training approaches, there are weakly supervised and unsupervised approaches that can help to overcome this constraint. However, mapping the results produced by unsupervised approaches to meaningful labels is a non-trivial task. Against this background, we tested, in the complex setting of the distinction between pulmonary (solid-growing) ADC and (non-keratinising) SqCC [32], whether a segmentation model could (i) be trained in an unsupervised approach and (ii) modified and retrained in a supervised setting to produce meaningful histological labels such as 'ADC' or 'SqCC'. In the best case, these labels should handle the aforementioned non-trivial distinction between solid-growing ADC and non-keratinising SqCC. (iii) We compared the the segmentation results to standard segmentation models for the same datasets.  
(Ad i), we show that unsupervised image segmentation techniques or training frameworks as described by [PERSON] et al. can be used for the unsupervised segmentation of histological images (Figure 3A) [15; 16]. Here, only aspects such as the ratio of filter size to object size in the image need to be considered.  
(Ad ii), we demonstrate that these models can be extended by another block, which can, after a second supervised training, remap (by a linear combination) the produced labels to meaningful labels such as 'ADC' or 'SqCC' (Figure 3B). Our new contribution is that a simple linear combination of the different labels previously recognised based on unsupervised training is applied to predict difficult labels such as 'ADC'.  
(Ad iii), finally, we compare the results to conventional training approaches and demonstrate that our approach of remapping the labels is not superior to conventional supervised learning. It is indeed inferior and there are only limited settings where it can be useful.
# A Wideband Metal-Only Patch Antenna for CubeSat
## 2 Antenna Configuration and Simulation Results
### Parametric Analysis
For example, increasing L\({}_{1}\) from 10.6 mm to 30.6 mm leads to an increase in \(\mid\)S\({}_{11}\)\(\mid\) from \(-33.8\) dB to \(-26\) dB. The optimal length is when L\({}_{1}\) = 15.6 mm, where it provides a small reflection coefficient of \(-33.8\) dB at 2.45 GHz. As shown in Figure 7, the variation of L\({}_{3}\) has an influence on the reflection coefficient. When L\({}_{3}\) is changed from 20.8 mm (optimal value) to 10.8 mm, the reflection coefficient dramatically increased from \(-33\) dB to \(-16\) dB. This means more power will be reflected back instead of being radiated into space.
# Integration of AI in Self-Powered IoT Sensor Systems
## 8 Discussion
One of these challenges is the duty cycling of AI processing. This processing refers to running algorithms exclusively at times when the sensor has enough stored energy to perform such operations. To accomplish such a task, synchronization between data collection and energy availability is necessary, as well as for the actual execution of inference in cases where the model is not resident in the cloud. Another issue concerns the trade-off between inference accuracy and energy consumption. This refers to the fact that complex AI models provide results with very good evaluation metrics, but they cannot operate efficiently under fluctuating energy conditions. Another issue concerns the temporal inconsistency of data caused by intermittent power sources, as real-time sequential interpretation is performed. These challenges require solutions such as intelligent buffering, adaptive model selection, anticipatory prediction of available energy, energy consumption streamlining and optimization, or AI models tolerant to missing data. These represent research directions that researchers should consider as benchmarks in this field. The open issues and future research directions aimed at ensuring the maturation of the field primarily concern the lack of standardization of the interfaces between self-powered sensors, AI modules, and IoT networks. The real-time implementation of AI algorithms on intermittently powered nodes requires studies addressing buffering, data loss, latency, and real-time implications. The study of AI models under variable energy conditions requires new paradigms, such as continuous learning or incremental learning, directly on self-powered IoT nodes. These paradigms will improve the adaptability of systems to environmental variations and sensor wear without the need for complete retraining. In this context, future research should address the integration of these directions into the proposed architectures.
## 3 Results and Discussion
### Recommendations for Governmental Support  
The economic feasibility analyses conducted in this study indicate that water reclamation projects for industrial water supply are infeasible without accounting for environmental benefits. Given that pollution discharge fees do not exist in Thailand or in many developing countries, environmental benefits currently do not directly benefit the industrial sector. Their incentives for pursuing water reclamation projects are likely to be limited. Environmental benefits often benefit the general public. As a result, the government should establish supportive policies to ensure the success of such water reclamation efforts.  
Governmental support for water reclamation projects can be in the form of funding support for investment in water reclamation projects [48; 49], the addition of adders to the prices of reclaimed water, or tax reductions for businesses that invest in water reclamation projects [50]. These measures can be chosen in a variety of ways, depending on the government and local context. Economic feasibility analyses, such as those undertaken in this study, may also aid in determining the appropriate level of support. For instance, funding support for water reclamation projects could be assessed using the difference between the NPVs with and without environmental benefits. The amount of the adders added to reclaimed water prices can be estimated by comparing the minimum selling prices of reclaimed water with and without consideration of the environmental benefits. Tax savings should be proportional to the environmental benefits created by such projects. Notably, giving low-interest loans did not appear to make water reclamation projects economically feasible, as the IRRs were negative for all flow rates when environmental benefits were excluded.  
Although water reclamation projects for the irrigation of plants and green areas were found to be economically feasible, even without considering environmental benefits, governmental support is still considered beneficial because it can increase the incentives for investing in such projects. Governmental support for water reclamation projects for the irrigation of plants and green areas could take the form of tax reductions or low-interest loans.  
The government can also play an important role in initiating technology adoption and in developing a regulatory framework, indicators, and monitoring procedures to ensure transparency and to guarantee health and safety, ultimately leading to public acceptance of water reclamation.