esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring the Potential of Sensing for Breast Cancer Detection
## 5 Biosensors for Breast Cancer Detection
### Quartz Crystal Microbalance Biosensor  
A crystal oscillator works on the principle of the inverse piezoelectric effect and is made up of piezoelectric material [110]. When an external voltage is applied with a specific frequency to a particular material, it produces mechanical deformation, and this material starts vibrating at the same applied frequency, which is defined as the inverse piezoelectric effect. Conversely, if we apply an external force to this piezoelectric material, it generates a voltage across the two terminals [110]. So, if we mechanically force them to vibrate at a specific frequency, they can develop an alternating current (AC) signal with the same frequency. This effect is known as the piezoelectric effect, and a material that shows this effect is known as a piezoelectric material. Different naturally occurring crystals like Rochelle salt, quartz, and tourmaline have these piezoelectric properties. Among these materials, Rochelle salt has the maximum piezoelectric activity, which means that for a given applied voltage, it generates the maximum vibration. But mechanically, this material is weak and can break very easily. Tourmaline has the most minor piezoelectric activity but is strong enough to resist breaking [110].  
In [111], a CD44-biosensor was designed by following the quartz crystal microbalance (QCM) approach for breast cancer detection under consideration of different films. The experimental results are as follows: (a) the LOD for MDA-MB-231 (M231) cells was 300 mL\({}^{-1}\) while the LOD for MCF-7 cells was 1000 mL\({}^{-1}\); (b) the expression level of CD44 on M231 cells was double that on MCF-7 cells; (c) the M231 cells were less stiff than the MCF-7 cells; (d) the metastatic potential of cancer cells was rapidly detected using this method. In [112], authors applied QCM sensors to detect the biomarker CA15-3 associated with breast cancer and observed the following: (a) the one-dimensional ZnO nanostructure was appropriate for biosensor application; (b) the QCM sensor showed good stability with a response time of 10 s; (c) QCM had high sensitivity (25.34 \(\pm\) 0.67 Hz/scale (1 U/mL)); (d) a linear (0.99) pattern associated with CA15.3 was observed for a range of 0.5 to 26 U/mL. In [113], the authors considered QCM for monitoring breast cancer marker CA15-3 and found that QCM sensors had high sensitivity (26. 303 \(\pm\) 1.139 Hz/scale, 1 U/mL) and good linearity (0.960 \(\pm\) 0.013) in the 0.5 U/mL and 100 U/mL concentration range of CA15-3.
# A Review of Fuzzy and Pattern-Based Approaches for Class Imbalance Problems
## 3 Pattern and Fuzzy Approaches for Imbalance Problems
### Discussion  
In the reviewed documents, the proposed methods with fuzzy pattern-based classifiers have shown better classifier results than other non-fuzzy approaches. Despite the favorable results, fuzzy pattern-based classifiers have not been studied more in detail. To the best of our knowledge, the only classifier based on fuzzy patterns is that proposed in [20]. Additionally, there are no fuzzy pattern-based classifiers specifically designed to deal with class imbalance problems and the approaches that are closer to this type of problem have not been studied enough.  
Fuzzy approaches have achieved those results for different reasons; one of them is that the results are similar to the language used by experts. This solution is due to the use of linguistic hedges (e.g., \"very\", \"less\", \"often\") that overcome the problems presented with the discretization of continuous features. Another characteristic is the flexibility the offers the fuzzification of the data. The fuzzification gives a wider window of opportunity in different classification steps (i.e., mining process, filtering process, variable window size).
## 3 Analysis and Design of Key Components
### High-Temperature and Ultrahigh-Pressure Simulation Test Cabin
#### 3.1.2 The Structural Form of the Cabin  
The maximum working pressure of the simulation test chamber designed in this paper is 140 MPa, and the maximum working temperature is 150 \({}^{\circ}\)C. Therefore, there are many design differences between this simulation test cabin and a cylinder under normal temperature and pressure, including the structural form, material, and wall thickness. At present, many research institutions have adopted various cylindrical structures for simulation test devices. The most commonly used structures are single-layer integral forged thick-walled cylinders, integral forged self-reinforcing thick-walled cylinders, double-layer and multilayer thick-walled cylinders, wire-wound cylinders, split block cylinders, and interlayer charging cylinders [30]. The bearing capacities for cylinders with different structural forms are not the same; the bearing capacity of a double-layer or multilayer cylinder is higher than that of a single-layer cylinder, and self-reinforced or prestressed structures carry higher loads than structures that are not self-reinforced or prestressed. Each cylinder structure has corresponding advantages and limiting factors. Therefore, in designing the structural form of the cylinder, all factors should be fully considered to choose a correct and reasonable structural form that meets usage requirements and is economical.  
Figure 5[39] shows the relationships between the ratio of the outer diameter to the inner diameter of cylinders with various structural forms and the pressures they bear. Based on Figure 5, the simulation test chamber in this paper was designed with a single-layer cylindrical structure to meet the usage requirements. The ranges and characteristics of various cylinder forms are listed in Table 1[39]. Based on a comparison of various types of cylinder structures and economic considerations for manufacturing, a single-layer integral forged thick-walled cylinder was chosen for the test cabin.  
Figure 4: Overall structure of the simulation test cabin. (**a**) 3D model. (**b**) Two-dimensional cross-section.
## 2 Related Work
### Summary of Relevant Works and Positioning of the Proposed Framework  
To contextualize and highlight the novelty of the proposed Edge-FLGuard+ framework, Table 1 provides a structured summary of representative recent studies in the domains of federated learning, IoT anomaly detection, and smart home cybersecurity. The table contrasts key characteristics such as architectural choices, privacy-preserving mechanisms, model efficiency, and 5G readiness across these works, clearly outlining how the present approach differentiates itself from the existing literature.  
While prior works by the authors introduced key components such as federated anomaly detection in smart cities [18], edge AI for smart home applications [19], and a preliminary federated framework (Edge-FLGuard) [17], these contributions tackled isolated aspects or broader domains. The current work, Edge-FLGuard+, builds upon and significantly extends this prior research by  
* Combining edge AI, federated learning, and 5G-specific optimizations into a single cohesive framework tailored for smart home environments;
* Introducing a lightweight GRU-based autoencoder architecture that offers improved detection performance with lower communication overhead;
* Conducting a more comprehensive evaluation, including ablation studies and privacy-preserving mechanisms, not present in earlier studies.  
As such, this work represents a novel and integrated advancement in the secure deployment of AI-driven anomaly detection in 5G-enabled smart home ecosystems.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Study** & **Domain** & **Model/FL** & **Devices** & **5G-Aware** & **Lightweight** & **Privacy-Preserving** & **Dataset(s)** \\ \hline [PERSON] et al. (2023) [1] & Smart home ML & CNN, RF & Local & — & Moderate & ✗ & Smart home \\ [PERSON] (2025) [13] & Edge IoT FL & Deep-belief + & FL & — & Moderate & ✓ & IoT time-series \\ [PERSON] et al. (2025) [5] & Edge IoT FL & Isolation Forest & Edge & — & ✓(<160 KB) & ✓ & Temperature sensors \\ [PERSON] (Edge-FLGuard, 2025) [17] & Smart IoT 5G FL & Autoencoder + & Edge + FL & ✓ & Moderate & ✓ & CIC-IDS \\ Proposed Edge-FLGuard+ & Smart home 5G FL & Lightweight & AE/GRU & Edge + FL & ✓ & ✓(<100 KB) & ✓(privacy-layer) & TON-IoT, CIC-IDS \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative overview of recent federated learning-based anomaly detection frameworks and the proposed Edge-FLGuard+, with emphasis on deployment domain, model architecture, resource-awareness, privacy mechanisms, and 5G applicability. The distinction between the authors’ earlier Edge-FLGuard and the new Edge-FLGuard+ highlights key improvements in architecture, privacy, and evaluation scope. Symbols: “✓” indicates that the feature is present or supported; “✗” indicates it is absent or not supported.
# A Survey on 5G Coverage Improvement Techniques: Issues and Future Challenges
## 1 Introduction
### Coverage Enhancement Requirements  
All cellular technologies define their requirements before their initial releases. The 5G defined its requirements such as ultra-reliable low latency communication, high spectral efficiency, high data rates, massive mobility, and coverage enhancement [39; 40; 41; 42]. In 5G, a small cell site concept was proposed to improve the coverage but in rural areas large cell sites are still used because of low traffic rates. In suburban areas, based on the traffic density, the number of relay nodes deployed are increased to improve the coverage [43]. Therefore, based on the requirements the cell sites are deployed to improve the coverage, throughput, and spectral efficiency [44]. Mobility, handovers, and cell edge data rates also affect the coverage [45]. The authors in Ref. [46], proposed a method to reduce the data transfer rate at the cell edge and also to reduce the inter-cell interference and to improve the coverage. This section provides an overview of cellular network requirements and in the following sections the coverage enhancement techniques and their future challenges are explained.
# Evaluating Biosedimentation for Strength Improvement in Acidic Soil
## 3 Results and Discussions
### Macro-Mechanical Results
#### 3.2.4 Shear Strength  
The shear strength of the treated soil improved as a result of this technique, which is thought to be related to calcium carbonate precipitation (CaCO\({}_{3}\)). It was observed that the strength of the soil samples changed with the distance between the anode and the cathode, with maximum shear strengths of 28.14 and 14.50 kPa for D45 and D60, respectively, at the anode. Furthermore, as shown in Figure 10, the shear strength fell by as much as 36% for D45 and 23% for D60 from the maximum undrained shear strength as the treatment distance increased. This fluctuation in strength corresponded to the rate of calcium carbonate precipitation, with more CaCO\({}_{3}\) precipitation resulting in a higher shear strength. Both [PERSON] et al. (2018) and [PERSON] et al. (2019) agreed that the void ratio was lowered owing to the buildup of CaCO\({}_{3}\) inside it and that the water content was considerably reduced by dissipation outside the voids of the soil specimen, which assisted in the shear strength's enhancement.
# Smart Beamforming for Direct LEO Satellite Access of Future IoT
## 4 Resource Sharing Beamforming Access  
For ease exposition, the detection scheme will be described under the premise that users are time and frequency synchronized, i.e., \(e_{k}=\tau_{k}=0\), \(\forall k\). Hence, RBSA in its basic form is based on slotted Aloha. It is also important to mention that a peculiarity of slotted Aloha access schemes is that the terminal EIRP could be dictated by the aggregate throughput of the system rather than the terminal throughput [13]. This drawback, which is inherent to systems where the terminal has a limited time to transmit the packet with respect to the frame duration, may increase the cost of the device. Interestingly, unslotted random access schemes based on either frequency division multiple access (FDMA) or code division multiple access (CDMA) are more suitable for low-cost terminal solutions. In this section we will show that under time slot synchronization, the receiver is able to spatially separate users via smart beamforming. Thanks to the repetition scheme introduced in Section 3 we can provide spatial diversity multiple access as described next. First, we gather the samples as follows \(\mathbf{Y}_{m}=[\mathbf{y}[m(N+L_{R})],\cdots,\mathbf{y}[(m+1)(N+L_{R})-1]]\), for \(m=0,\cdots,N_{\mathrm{S}}-1\). Essentially, we group the samples to operate on a symbol basis. With this arrangement, we can get:  
\[\mathbf{Y}_{m}=\sum_{k=1}^{N_{\mathrm{U}}}\mathbf{h}_{k}\mathbf{\tilde{x}}_{k }^{m}+\mathbf{W}_{m}, \tag{14}\]  
where \(\mathbf{W}_{m}=[\mathbf{w}[m(N+L_{R})],\cdots,\mathbf{w}[(m+1)(N+L_{R})-1]]\). The system is modeled like a \(N_{\mathrm{R}}\times(N+L_{R})\) MIMO communication scheme. For simplicity we will omit the symbol index from here onwards. The data set can be split into the data and the redundant samples, yielding,  
\[\mathbf{Y}=[\mathbf{Y}_{d}\ \ \mathbf{Y}_{r}]=\sum_{k=1}^{N_{\mathrm{U}}} \mathbf{h}_{k}[\mathbf{x}_{k}\ \ \mathbf{x}_{k}\mathbf{C}_{p_{k}}]+[\mathbf{W}_{d}\ \ \mathbf{W}_{r}]. \tag{15}\]  
At the satellite, a beamvector \(\mathbf{b}_{i}\in\mathbb{C}^{N_{\mathrm{R}}\times 1}\) is designed such that it presents a maximum at the direction of arrival (DoA), where the signal with repetition pattern \(\mathbf{C}_{i}\) comes from. The constrained optimization problem is posed as:  
\[ \begin{array}{ll}\min_{\mathbf{b}_{i}}&\mathbb{E}\Big{\{}||\mathbf{b}_{i}^{ \mathrm{H}}(\mathbf{Y}_{d}\mathbf{C}_{i}-\mathbf{Y}_{r})||^{2}\Big{\}}\\ \text{s.t.}&\mathbb{E}\Big{\{}\mathbf{b}_{i}^{\mathrm{H}}\mathbf{Y}_{d} \mathbf{C}_{i}\mathbf{Y}_{r}^{\mathrm{H}}\mathbf{b}_{i}+\mathbf{b}_{i}^{ \mathrm{H}}\mathbf{Y}_{r}\mathbf{C}_{i}^{\mathrm{H}}\mathbf{Y}_{d}^{\mathrm{H }}\mathbf{b}_{i}\Big{\}}=\rho,\end{array}  \tag{16}\]  
where \(\rho\) is some constant different from zero. Figure 4 sketches the proposed design.
# Clustering-Based Energy-Efficient Self-Healing Strategy for WSNs under Jamming Attacks
## 4 Baseline Clustering Strategies under Jamming
### Power Domain
#### 4.1.2 Power Allocation (PA)  
Instead, this strategy assumes that the nodes can individually set their transmit power to reach their respective CH. Therefore, each node calculates the optimum transmit power that ensures the correct reception of the message and the CH, according to (3). Consequently, the nodes closer to the CH can use lower transmit powers and reduce their energy consumption. In contrast, the nodes far from the respective CHs demand higher transmit powers, and eventually, they drain their batteries quicker.
## 3 Materials and Methods
### Fishtail Probe Width (\(W_{\rm P}\)) Optimization  
The width of the fish-tail probe was varied in a step size of 0.4 mm symmetrically about the strip center line, as shown in Figure 7, and the corresponding effects of width variations on the IL and RL are plotted in Figure 8a,b, respectively, with a constant fish-tail depth of 2.5 mm. The effect of the variation of width on the IL, RL, and \(-10\) dB absolute bandwidth is tabulated in Table 5. It can be observed from the table that, as the width of the fish-tail increases, the resonance (29.75 GHz) is close to the design frequency of 30 GHz, with a minimum IL \(-0.36\) dB, RL \(-16.72\) dB, and \(-10\) dB absolute bandwidth 21.50 GHz being obtained. It can also be concluded from the tabular data that as the fish-tail probe width (\(W_{\rm P}\)) decreased, the insertion loss and return loss improved with a shift in the resonance frequency towards the Q-band and U-band upper-frequency limits. Therefore, the same transition could be easily and efficiently used for other than Ka-band microwave and millimeter wave applications, just by reducing the fish-tail probe width.  
\begin{table}
\begin{tabular}{p{71.1 pt} p{71.1 pt} p{71.1 pt} p{71.1 pt} p{71.1 pt}} \hline \hline
**Depth of the Fish-Tail** & **Min. IL** & **RL** & **Resonance** & \(-\)**10 dB Absolute BW** (\(\mathbf{f_{L}}\)**-\(\mathbf{f_{H}}\)) (GHz)** \\ \hline
1.9 & \(-0.50\) & \(-11.59\) & \(43.07\) & (38.30–44.96) \\
2.1 & \(-0.46\) & \(-36.72\) & \(25.04\) & (21.78–44.70) \\
2.3 & \(-0.43\) & \(-25.98\) & \(25.94\) & (21.84–44.80) \\
2.5 & \(-0.36\) & \(-16.72\) & \(29.75\) & (23.52–45.0) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Effect of fish-tail depth variations with a fish-tail probe width \(W_{\rm P}\) = 3.8 mm.  
\begin{table}
\begin{tabular}{p{71.1 pt} p{71.1 pt} p{71.1 pt} p{71.1 pt} p{71.1 pt}} \hline \hline
**Fish-Tail Probe** & **Min. IL** & **RL** & **Resonance** & \(-\)**10 dB** \\
**Width, W\({}_{\rm P}\) (mm)** & **IL** & **(dB)** & **(dB)** & **Frequency** & **Absolute BW** (\(\mathbf{f_{L}}\)–\(\mathbf{f_{H}}\)) (GHz)** \\ \hline
2.6 & \(-0.25\) & \(-19.76\) & \(43.56\) & (25.60–45.0) \\
3.0 & \(-0.29\) & \(-18.94\) & \(43.11\) & (24.09–45.0) \\
3.4 & \(-0.35\) & \(-15.79\) & \(27.96\) & (23.13–45.0) \\
3.8 & \(-0.36\) & \(-16.72\) & \(29.75\) & (23.52–45.0) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Effect of fish-tail width variations with a fish-tail depth = 0.4 mm.  
Figure 7: Fish-tail width variation with a step size of 0.4 mm.
# On Analyzing Beamforming Implementation in O-RAN 5G
## 1 Introduction
Section 2 presents the related work and the O-RAN architecture concepts, interface specifications, and units; Section 3 focuses on splitting options and describes Split Option 7; Section 4 elaborates our O-RAN beamforming approach, presents the beamforming challenges, and suggests methods focusing on one of the beamforming deployments (channel-information-based beamforming); Section 5 explained the system models and simulation results; and lastly, Section 6 overviews the paper conclusions.
# Evaluation of Agricultural Soil-Improving Zeolite for Improving Irrigation Water Quality
## 3 Results and Discussion
### Adsorption Kinetics
#### 3.4.1 Contact Time  
The relationship between contact time and the sorption of B and Na was analyzed according to the conditions identified by the CCRD. The results (Figure 2) showed differential  
Figure 1: 3D Response surface plots for sodium (**A**) and boron (**B**) removal, considered individually.  
behavior between the two studied species. For B, sorption and/or immobilization occurred slowly and steadily during the first 720 min, after which the relative speed increased until 1200 min when equilibrium and/or saturation was reached, remaining constant with a 90% removal. For Na, repetitive cycles of sorption/desorption with variable temporal durations were observed, but with a decreasing trend. Additionally, it was possible to identify the desorption of larger amounts of Na than those initially contained in the water (240 min), possibly due to the release of the inherent Na from the structural and compositional zeolite. The maximum removal percentage of Na did not exceed 35% in any of the experiments.
## 4 Effect of Design Parameters on Strength
### Effect of TA/C Ratio on Compressive Strength and Split Tensile Strength  
In order to achieve proper packing density, the ratio of TA/C plays a key role. The particle size distribution of aggregates defines the formation of major voids in the system. Hence, the TA/C ratio plays a vital role in defining the fresh and hardened properties of the system. It is observed from Figure 6 that an increase in the TA/C ratio results in a decrease in compressive strength. This is because there is lesser binding content with an increase in TA content, which result in more pores. Most of the researchers with irrespective compressive strength of SCC opted for a TA/C ratio between 3 and 7. The literature with a TA/C ratio greater than 9 had a compressive strength in the range from 30 to 40 MPa.  
Similar to compressive strength behavior, with an increase in TA/C ratio there is a decrease in split tensile strength that is observed. However, the optimum ratio for TA/C ratio with respect to split tensile strength is observed from 3 to 5. Irrespective of SCC grade and with respect to compressive strength, the split tensile strength decreases with an increase in the TA/C ratio. There is no such sudden reduction in split tensile strength such as compressive strength when there is an increase in the TA/C ratio (Figure 7).  
Figure 5: Effect of the W/B ratio on various split tensile strength of SCC concrete with recycled aggregate according to the literature.  
Figure 6: Effect of TA/C ratio on various compressive strengths of SCC concrete with recycled aggregate according to the literature.
## 4 Review of the Routing Protocols
### Link Quality-Based Routing Protocols Comparison
The routing protocol for low-power and lossy networks (RPL) is a popular routing protocol used for IoT-based WBASNs, and it became standard in 2011 by IETF. RPL works with IPv6 to make a complete IoT-based network. At the MAC and PHY layers, RPL uses IEEE 802.15.4. Node status information is used by RPL to decide on the next hop. The node status information includes residual energy, memory and link quality. RPL is recommended for healthcare applications by IETF and ZigBee [168]. For WBASNs/IoTs, the proposed routing protocols have mostly evolved through ad hoc on-demand distance vectors [169]. The link-aware category depends on the link state information for routing. These protocols use parameters such as end-to-end delay, RSSI, PRR and power, which affect the performance of data delivery. Routing by energy and link quality (REL) is an IoT-based routing protocol that was developed for use with WSNs for patient monitoring systems. The REL considers issues such as energy utilisation, reliability and latency. These low-power radios are sensitive to the interference generated by nearby devices. REL uses the link quality metrics, i.e., LQI and RSSI, to overcome the sensitivity issues; this is helpful in providing reliability. In REL, the next hop is selected based on a set of matrices, including link quality, residual energy, hop count and load balancing. The link-quality-based lexical routing (LABILE) protocol proposes a routing algorithm that uses the lexical structure with link quality information [170]. LAIBLE uses the LQI value to provide link reliability. LABILE categorises the computed value of link quality as \"good\" or \"bad\" according to predefined threshold values. The LABILE protocol ignores the energy efficiency metric while selecting the next hop.
## 3 Methodology
### Feature Selection
For each feature selection technique, the optimal number of features was determined through a systematic process that involved testing various feature sets, starting from the top 100 features and incrementing by 100 up to the top 500 features. This range was chosen to address the challenges associated with high-dimensional EEG data, which can complicate data analysis and increase computational demands. The selection of the top 100 features was found to be optimal, balancing classification performance with computational efficiency. Furthermore, this systematic evaluation allowed for a comprehensive assessment of model \begin{table}
\begin{tabular}{c c c} \hline \hline
**Filter** & **Wrapper** & **Embedded** \\ \hline Mutual Information (MI) & Forward Feature Selection (FFS) & Elastic Net-Based \\ \hline \multirow{3}{*}{Chi-square} & Support Vector Machine-based & \\  & Recursive Feature Elimination & \\  & (SVM-RFE) & \\ \hline Minimal-Redundancy- & & \\ Maximal-Relevance (mRMR) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Feature selection methods. performance across different feature configurations, reinforcing the effectiveness of the chosen feature set.