esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
Substantial experiments have shown that the rock Kaiser effect can breakdown or disappear over the cyclic loading process [21; 22; 34]. The Felicity effect occurs when the Kaiser effect vanishes. The Felicity effect, which is also termed the counter-Kaiser effect, denotes that AEs start to appear noticeably when the applied stress is lower than its highest value of the stress history in the previous stage [21; 34]. The Felicity effect is quantifiable by the Felicity ratio (_FR_), which usually represents the degree of rock damage. As the _FR_ is decreased, the rock damage degree becomes higher. Given that the _FR_ is larger than 1.0, the Kaiser effect is pronounced and the rock damage degree is low or even no damage. When _FR_\(<\) 1.0, the Kaiser effect disappears, which indicates remarkable damage due to unstable crack expansion. The Felicity ratio (_FR_) is measured by the stress magnitude of the Kaiser effect (\(\sigma_{\rm AE}\)) and the highest stress in the previous loading stage (\(\sigma_{\rm m}\)):  
\[FR=\frac{\sigma_{\rm AE}}{\sigma_{\rm m}}. \tag{1}\]  
The _FR_ values of the red sandstone under 25 combinations of \(\sigma_{\rm A}\) and the loading strain rate are calculated using Equation (1). Table 2 and Figure 10 shows variations of _FR_ under different \(\sigma_{\rm A}\) and \(l_{\rm sr}\). In the following, we examine the effects of \(\sigma_{\rm A}\) and \(l_{\rm sr}\) on the appearance of both the Kaiser and the Felicity effects.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Felicity Ratio**} & \multicolumn{5}{c}{**Loading Strain Rate (\(l_{\rm sr}\), s\({}^{-1}\))**} \\ \cline{2-7}  & **(FR)** & \(\mathbf{1\times 10^{-5}}\) & \(\mathbf{5\times 10^{-5}}\) & \(\mathbf{1\times 10^{-4}}\) & \(\mathbf{5\times 10^{-4}}\) & \(\mathbf{1\times 10^{-3}}\) \\ \hline \multirow{5}{*}{**(MPa)**} & 0.5\(\sigma_{\rm C}\) & 1.08, 1.03, 1.07 & 1.14, 1.11, 1.13 & 1.21, 1.23, 1.19 & 1.29, 1.27, 1.25 & 1.32, 1.38, 1.35 \\  & 0.6\(\sigma_{\rm C}\) & 0.86, 0.88, 0.85 & 0.95, 0.90, 0.92 & 1.05, 1.00, 0.99 & 1.10, 1.15, 1.14 & 1.19, 1.18, 1.21 \\  & 0.7\(\sigma_{\rm C}\) & 0.55, 0.61, 0.53 & 0.83, 0.84, 0.82 & 0.87, 0.86, 0.86 & 0.91, 0.88, 0.91 & 0.95, 0.94, 0.93 \\  & 0.8\(\sigma_{\rm C}\) & 0.45, 0.46, 0.46 & 0.71, 0.69, 0.67 & 0.73, 0.74, 0.73 & 0.78, 0.77, 0.80 & 0.82, 0.80, 0.81 \\  & 0.9\(\sigma_{\rm C}\) & 0.37, 0.39, 0.36 & 0.60, 0.59, 0.57 & 0.64, 0.63, 0.63 & 0.67, 0.66, 0.67 & 0.70, 0.69, 0.70 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Felicity ratios under different stress levels and loading strain rates.
# A Modular Experimentation Methodology for 5G Deployments: The 5 GENESIS Approach
## 2 Background and Related Work
### Standardization on 5G KPI Testing and Validation
We observe that these aspects are key in order to completely define and take into account the network setup during a test, which includes the relationships between infrastructure, management and orchestration system, measurement probes, user traffic, and so on. As a matter of fact, it is hard to contextualize and compare the obtained results without a proper scenario definition; also, a clear definition of test preconditions is key for running reproducible tests and obtaining reliable results. For this reason, we have defined our testing methodology by taking [26] as a reference, and then extrapolating the provided guidelines towards covering a full list of 5G E2E KPIs. We also aim at a more general and flexible testing approach, tailored on open experimental testbeds. Hence, we propose a test case template that reuses and expands the one in [26] in terms of considered fields, while also addressing clear definitions for test sequence, preconditions, and E2E scenarios. When considering these aspects, a document by Next Generation Mobile Networks (NGMN) [27] introduces test case sequence and scenarios, with the latter based on the definitions provided in [28]. The goal is the evaluation of 5G NR performance, and the focus is on 3 GPP Release 15 (Rel-15), the first step of 5G NR standardization, which primarily considers eMBB and some aspects of URLLC use cases. For both, several KPIs are considered, e.g., latency, user throughput, and mobility, but also capacity, coverage, energy efficiency, and user experience, among others. The NGMN approach fuses together, in a monolithic definition of test cases, the testing procedures (e.g., test sequence), network configurations (e.g., number of exploited network resources), network conditions (e.g., number of users), and traffic profiles (e.g., used protocol and packet size). As detailed in the next sections, our testing approach is instead modular, i.e., the logical components forming an experiment (test cases, scenarios, and slices) are kept separated, ultimately enabling higher flexibility and adaptability, as well as lightweight extension towards more advanced testing. Table 1 summarizes the standards and documentation reviewed above.
# Design and Analysis on Decoupling Techniques for MIMO Wireless Systems in 5G Applications
## 4 The Methods of Decoupling
### External Decoupling
#### 4.1.2 PDE Technique  
Parasitic decoupling elements as decoupling structures can be placed flexibly, such as above, below, or indirectly in contact with the antenna element. They help to induce reverse coupling, which prevents the currents flow to neighboring elements.  
A hybrid PDE structure composed of a circular patch and four L-shaped strips was printed on the upper surface of the four-port MIMO system in Figure 2[36]. Due to the surface current on the decoupling patches, not only the isolation was improved to 15.4 dB, but also the impedance bandwidth was widened. The C-shaped patch was added as PDE near the circular radiation element [37]. Compared with the original design without the decoupling method, the isolation was increased by 9 dB, and bandwidth was extended by 250 MHz in the compact MIMO with the element spacing of 0.32 wavelength. The quadelement ultra wide band (UWB) MIMO with PDE placed on the backside was designed [38]. In order to meet the wideband characteristic of the proposed antenna array, the parasitic element with different features was adopted to cover the whole band decoupling. More than 20 dB isolation can be obtained.  
Antenna decoupling surface (ADS) as a deformed PDE was firstly proposed in 2017 [39]. ADS is a thin substrate loaded with a series of small metal patches. The function of ADS is to cancel the coupling wave with the help of the reflecting wave from the ADS [40]. The hybrid decoupling scheme constituted of ADS and DGS was applied in the MIMO system with element spacing of only 1 mm [41]. Those metal reflectors consisted of two groups ADS. One was formed by window-shaped sheets as a main reflected element. The other was three cross-shaped sheets to fill the leak. The isolation improvement mainly depended on the distance above the patch and ADS and the results more than 20 dB in 1 GB bandwidth can realize.
# Explainable MRI-Based Ensemble Learnable Architecture for Alzheimer's Disease Detection
###### Abstract  
With the advancements in deep learning methods, AI systems now perform at the same or higher level than human intelligence in many complex real-world problems. The data and algorithmic opacity of deep learning models, however, make the task of comprehend the input data information, the model, and model's decisions quite challenging. This lack of transparency constitutes both a practical and an ethical issue. For the present study, it is a major drawback to the deployment of deep learning methods mandated with detecting patterns and prognosticating Alzheimer's disease. Many approaches presented in the AI and medical literature for overcoming this critical weakness are sometimes at the cost of sacrificing accuracy for interpretability. This study is an attempt at addressing this challenge and fostering transparency and reliability in AI-driven healthcare solutions. The study explores a few commonly used perturbation-based interpretability (LIME) and gradient-based interpretability (Saliency and Grad-CAM) approaches for visualizing and explaining the dataset, models, and decisions of MRI image-based Alzheimer's disease identification using the diagnostic and predictive strengths of an ensemble framework comprising Convolutional Neural Networks (CNNs) architectures (Custom multi-classifier CNN, VGG-19, ResNet, MobileNet, EfficientNet, DenseNet), and a Vision Transformer (ViT). The experimental results show the stacking ensemble achieving a remarkable accuracy of 98.0% while the hard voting ensemble reached 97.0%. The findings present a valuable contribution to the growing field of explainable artificial intelligence (XAI) in medical imaging, helping end users and researchers to gain deep understanding of the backstory behind medical image dataset and deep learning model's decisions.  
Alzheimer's disease detection; explainable AI; stacking ensemble; healthcare AI; Brain MRI; medical diagnostics; Grad-CAM; LIME; saliency map; early AD detection
# Optimization of Microalgae-Bacteria Consortium in the Treatment of Paper Pulp Wastewater
## 3 Results and Discussion
### Influence on Microalgae-Bacteria Inoculation Ratio on Organic Matter Removal  
The performance of organic matter removal was measured in terms of COD, for which average concentration in the paper pulp wastewater was 1010.5 \(\pm\) 63.30 mg/L. PBR1, PBR2, and PBR3 had the lowest initial inoculation ratio and showed a slower COD degradation, resulting in a progressive treatment performance during the monitoring days. After two days of operation, the COD removal efficiencies (RE) were 22.05%, 13.72%, and 35.74% for PBR1, PBR2, and PBR3, respectively. Given that bacteria and microalgae are involved in the decomposition of organic components, this could be due to a reduced concentration of microalgae and bacteria in the medium.  
In two days, the synergistic process between microalgae and bacteria presented COD RE of 53.90%, 55.90%, and 64.29% for PBR4, PBR5, and PBR6, respectively, which had the greater inoculation ratios (Figure 3). Both organic matter oxidation by bacteria and its assimilation by microalgae had occurred simultaneously when these organisms were in symbiosis in a consortium, generating mutualistic transfer of CO\({}_{2}\) and DO [42]. In general, until the fifth day of operation, all PBRs performed well in terms of organic matter removal. Afterwards, there was a stability period in terms of carbonaceous material removal, with final COD concentrations in the effluent of PBRs below 390 mg/L. The final concentrations of COD were 356 mg/L, 273 mg/L, 279 mg/L, 200 mg/L, 175 mg/L, and 210 mg/L for PBR1, PBR2, PBR3, PBR4, PBR5, and PBR6, respectively, corresponding to RE of 67.59%, 75.80%, 75.21%, 83.03%, 85.50%, and 82.04%, respectively.  
Figure 2: Variation of pH in photobioreactors during operation.  
In the final of the 14-day operation period, the lowest COD effluent concentration was detected for the PBR5 (initial ratio of 1:5 microalgae-bacteria). This can be explained due to the higher rate of organic carbon oxidation by the bacteria compared with algae as stated by [PERSON] et al. [43]. [PERSON] et al. [17], working with _Scenesedesmus_ sp. and a paper pulp wastewater, with an initial dilution of 60%, obtained a COD RE of 89% after 28 days of operation, but without controlling the ratio of microalgae-bacteria, since bacteria only came from the wastewater.
## 2 Principle of Stress Wave-Based Pulse Position Modulation (PPM) Communication  
### Principle of Stress Wave-Based PPM Communication  
A stress wave is the propagation of acoustic waves in solid materials, such as metal, concrete, wood, rock, and among others [61]. Piezoceramic transducers are commonly used to induce the stress wave. Generally, there are three different modes of stress waves: (1) longitudinal waves, also called pressure wave (P-wave); (2) transverse waves, also called shear wave (S-wave); (3) Rayleigh waves, which are a type of surface wave. In Table 1, the velocities of stress wave in different metals are listed [62]. A stress wave traveling in different modes and/or in different metal materials can reach different speeds and distances. Such effects on stress wave propagation will be need to be considered when implementing stress wave communication on metallic pipelines.  
To achieve the data transmission via stress wave through the metal pipeline structures, PPM has been applied in the decoding and encoding process. PPM has been shown to be an effective  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Wave Mode** & \multicolumn{3}{c}{**Wave Velocities (m/s)**} \\ \hline  & **Steel** & **Copper** & **Iron** & **Aluminium** \\ Longitudinal, C & 5000 & 3650 & 3900 & 5000 \\ Transversal, C\({}_{T}\) & 3200 & 2250 & 2450 & 3050 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Velocities of stress wave in different metal materials.  
modulation format for transmitting digital information via acoustic waves because of its suitability for power-efficient channels, comparative simplicity in implementation, and reduced sensitivity to multipath propagation. In PPM, digital information is transmitted by dividing each data frame with duration \(T_{symbol}\) into M possible data slots with each of duration \(T_{slot}\), and timing a transmission pulse in one of these time slots. The mathematical definition of a PPM pulse stream is given in [63] as:  
\[x(t)=\sum_{n\ =\ -\infty}^{\infty}g\Big{(}t-nT_{symbol}-t_{n}\Big{)} \tag{1}\]  
where \(g(t)\) is the PPM pulse shape and \(t_{n}\) is the random data coded into PPM such that:  
\[0\ \leq\ t_{n}\ \leq(M-1)T_{slot} \tag{2}\]  
In PPM, data is modulated and transmitted with short pulses all of which have the same width and amplitude. The difference among the pulses is the delay between each pulse. A short duration represents digital 0, and a large duration represents digital 1. In this paper, we applied the following coding format, as shown in Figure 1. The bit '0' is represented by 5 pulses that are set at 1 ms, 3 ms, 7 ms, 11 ms and 14 ms with the frame period of 15 ms and the pulse width of 25 \(\upmu\)s. Correspondingly, the pulses standing for bit '1' are set at 1.5 ms, 3.5 ms, 7.5 ms, 11.5 ms, and 14.5 ms. After the MCU (microcontroller unit) receives the data from the PC, it will send the data in the format of \"55 DataAA\" to meet the synchronization requirements in demodulation.
## 5 Discussion
### Addressing the Research Gaps and Future Directions
#### 5.4.1 Strategic Implications and Practical Applications  
The findings reveal various ways in which AI has been utilized in supply chain management. For example, AI is used in the blood supply chain for time-series-based demand forecasting to improve inventory management of blood products [7; 8]. Other AI methodologies, such as LSTM networks, are employed in the retail sector to predict consumer demand, enhance inventory levels, and meet customer expectations. Many applications also benefit from blockchain technology, which provides higher levels of trust and transparency. This trend has enabled many firms to gain new strategic advantages through post-implementation improvements.
## 4 Probabilistic Analyses
### Sensitivity Analyses
#### 4.6.2 Component Level
When considering the sensitivity factors with regard to the horizontal deflection in the column middle height, see Figure 10b, the tensile strength of the concrete \(X_{12}(f_{ct})\) and the compressive strength \(X_{11}(f_{c})\) both play a significant role as the horizontal displacement increases, however, the modulus of elasticity of the concrete \(X_{1}(E_{ci})\) and the concrete fracture energy \(X_{13}(G_{f})\) are initially more important. Figure 10d provides an insight into the scatters in the relationship between the axial force and the horizontal displacement in the middle height of the column made of C100/115 in a similar way as was reported in the previous section for the C45/55 column. A closer comparison between the sensitivity factor curves of the C45/55 and C100/115 columns shows clear differences. For example, \(X_{1}(E_{ci})\) becomes more important for the normal force analysis with higher strength, see Figures 9a and 10a. For the horizontal deflection sensitivity analysis, \(X_{1}(E_{ci})\) becomes less important and \(X_{13}(G_{f})\) becomes more important with increasing strength and load, see Figures 9b and 10b. Figure 10: Sensitivity analyses at the component/column level based on the probabilistic non-linear analysis of the considered column made of C100/115, see Section 4.4: (**a**) sensitivity of material parameters with respect to the normalized applied axial force; (**b**) sensitivity of material parameters with respect to the normalized horizontal displacement at middle height of the column; (**c**) basic variables of considered material models; (**d**) normalized horizontal displacement vs. normalized axial force response.
### Focus Moving Forward  
Advances in environmental satellite technology have created opportunities for new and exciting science, and this translates into increased needs for training, particularly as related to instrumentation and observation. In the US alone, there have been four relatively recent satellite launches (Suomi-NPP, GOES-16, JPSS-1, and GOES-17) that are increasing and improving the environmental satellite information available for a variety of applications. EUMETSAT launched the third polar-orbiter in their Metop series in 2018, and has also placed strong emphasis on both MSG satellite applications and the anticipated capabilities of the next-generation MTG satellites. COMET works collaboratively with these sponsors to define training priorities and needs. In the satellite meteorology community, these needs often stem from new satellites and the increased remote sensing capabilities and data flows that come with them. The resulting training offerings link directly to the WMO Guidelines and Skills for Operational Meteorologists [5], which focus on feature identification as well as on satellite interpretation for atmosphere, land, and ocean systems.  
COMET's more recent lessons work in tandem with existing foundational training materials on MetEd. With the changing technological landscape and increased emphasis on providing shorter, more interactive learning solutions that are both effective and engaging, we are continually attempting to balance the need for continued instruction in foundational science with the changing needs of the workforce. To address these workforce concerns, in recent years, COMET's offerings have become shorter and more narrowly focused, while also attempting to increase learner engagement. With our newest offerings, we have continued to keep our sights set on one of the primary goals of our training efforts: To provide measurable increases in performance and skill using environmental satellite products.  
Figure 2: Instrumentation and observation (which includes satellite topics) is the top training priority identified for WMO Regional Association (RA) IV (members include North America, Central America, Caribbean). Communication and customer interaction (key components of decision support services) is in the top five [4].
## 4 Design Methodology for SOA-Enabled HONoC
### Worst-Case Crosstalk Noise and SNR Analysis
#### 4.2.2 SNR Analysis of the Longest Path  
As the worst-case SNR of the longest path can vary depending on the dominant crosstalk affected by the SOA gain, the SNR values of three representative cases are analysed as follows.  
Figure 7: Non-SOA sub regions (\(REG\)) according to the SOA placement algorithm.  
(1) _Case_ 1: \(h=0\), \(G\leq\left|L_{avg}\right|\) \[SNR_{1}\approx\frac{L/K}{\left[\left(\left(GL\right)^{-1}+2 GL\right)\left(\sum_{ i=0}^{2m-3}\frac{1}{\left(GL\right)^{i}}\right)-\left(GL\right)^{-\left(m-2 \right)}+\left(GL\right)^{-\left(m-4\right)}\right]}\] (14a) (2) _Case_ 2: \(h=0\), \(G>\left|L_{avg}\right|\) \[SNR_{1}\approx\frac{L/K}{\left[\left(\left(GL\right)^{-1}+\left(GL\right)^{m-1 }\right)\left(\sum_{i=0}^{2m-3}\frac{1}{\left(GL\right)^{i}}\right)+\left( \left(GL\right)^{m-1}+1\right)\left(\sum_{i=0}^{m-2}\frac{1}{\left(GL\right)^ {2}}\right)\right]}\] (14b) (3) _Case_ 3: \(h=2m-1\) \[SNR_{3}\approx\frac{L/K}{\left[\left(L^{-1}+2L\right)\left(\sum_{i=0}^{2m-3} \frac{1}{L^{i}}\right)-L^{-\left(m-2\right)}+L^{-\left(m-4\right)}\right]}\] (14c)  
The case when \(h=0\) indicates that SOAs are placed on all waveguides, whereas there is no SOA when \(h=2m-1\). When \(h=0\), a _REG_ consists of only one router. As shown in Figure 8a of Case 1, the incoming crosstalk from the nearest router is dominant. In contrast, the crosstalk introduced from the farthest router causes the worst-case SNR in Case 2 as shown in Figure 8b.  
As shown in Equation (14a) and (14c), \(SNR_{1}\) and \(SNR_{3}\) have an identical form because of the same traffic pattern for the worst-case SNR in the network. If the \(GL\) of \(SNR_{1}\) is replaced by \(L_{1}\) and if the \(L\) of \(SNR_{3}\) is \(L_{3}\), then \(L_{1}\) is always less than \(L_{3}\). Therefore, \(SNR_{1}\) is always greater than \(SNR_{3}\) because the insertion loss of Case 1 is smaller than that of Case 3.  
In contrast, some crosstalk components have the chance to be amplified more times than the optical signal by SOAs as described in Case 2. In Equation (14b), \(\left(GL\right)^{m-1}\) represents the worst crosstalk noise due to the SOAs and \(\left(GL\right)^{i}\) indicates the ratio of the number of SOAs that the optical  
Figure 8: Worst-case SNR of the longest path in accordance with the SOA gain: (**a**) \(h=0\), \(G\leq\left|L_{avg}\right|\); and (**b**) \(h=0\), \(G>\left|L_{avg}\right|\).  
signal passes through to the number of the SOAs that the introduced crosstalk noise passes through. Therefore, if \(\left(GL\right)^{m-1}\) is larger than \(\left(GL\right)^{i}\), this implies that the number of SOAs that the crosstalk noise passes through is greater than the number of SOAs that the optical signal passes through. Consequently, \(SNR_{2}\) severely degrades and eventually can be smaller than \(SNR_{3}\) as the SOA gain grows.
## Acoustic Sensor Architecture
For chemical sensing, coating the sensor surface with a compound whose acoustic velocity varies upon exposure to a chemical species will also change boundary conditions and consequently reflect the echo time of flight. Since the Rayleigh wave exhibits an out of plane component radiating in fluids as a strongly coupled propagative wave, it is only compatible with sensing chemical compounds in the gas phase: measuring in the liquid phase requires a pure shear polarization of the acoustic wave, classically found in shear waves confined on the surface by a guiding layer in a Love mode configuration [33]. The sensor architecture investigated throughout this paper is made of black (pyroelectricity free) lithium niobate in a YXI/128\({}^{\circ}\) cut, propagating a Rayleigh wave. The sensing demonstration will focus on temperature and gas phase chemical detection: the aim of this paper is not so much to demonstrate the well-known sensing characteristics of these devices, but to emphasize the systems approach to wireless sensing, since the RADAR electronics must exhibit a \"much better\" stability than the variable measured to detect the quantity under investigation. The \"much better\" qualifier defines the detection limit for the measurement. Figure 1: Packaged (**a**) and sealed (**b**) 100-MHz delay line made of black lithium niobate. The four light lines on the left device are reflections from the interdigitated electrode transducer (second from top) and acoustic reflectors (top and the two lines at the bottom). The grating lines on the paper used as background are separated by 5 mm.