esa-sceva/satcom-chunk-collection huggingface dataset:
# Explainable Machine Learning Model for Chronic Kidney Disease Prediction
## 5 Discussion
Predictions made by AI in healthcare are extremely significant since, if not properly understood, they might cause wrong diagnoses, treatment suggestions, or potentially fatal outcomes [54; 55; 56]. Our research makes a noteworthy addition by incorporating XAI techniques to improve the transparency of the model. We utilized the LIME technique to offer clear and comprehensible insights into the predictions made by the model. This integration guarantees that the model not only forecasts CKD but also explains its predictions, addressing the crucial matter of transparency in ML-based medical diagnosis. The explanations produced by LIME aid physicians and patients in comprehending the determinants that impact the judgments made by the model, therefore enhancing trust and enabling more knowledge \begin{table}
\begin{tabular}{c c c} \hline \hline
**Authors** & **Method** & **Accuracy** \\ \hline [PERSON] et al (2024) [26] & Random Forest, Adaboost & 100\% \\ [PERSON] et al. (2024) [27] & TrioNet (Extra Tree + Random Forest + XgBoost) & 98.97\% \\ [PERSON] (2024) [28] & Light GBM & 99.75\% \\ [PERSON] et al. (2024) [29] & Bagging, Boosting, Stacking & 100\% \\ [PERSON] et al. (2023) [30] & Random Forest & 96\% \\ [PERSON] et al. (2023) [31] & Support Vector Machine & 99.33\% \\ [PERSON] et al. (2023) [32] & k-Nearest Neighbors & 100\% \\ [PERSON] et al. (2023) [33] & Light GBM & 98.30\% \\ [PERSON] et al. (2023) [34] & XgBoost & 98.30\% \\ [PERSON] et al. (2023) [35] & XgBoost & 98\% \\ [PERSON] et al. (2023) [36] & AdaBoost & 98.47\% \\ [PERSON] et al. (2023) [37] & Decision Tree & 98.60\% \\ [PERSON] et al. (2023) [53] & Logistic Regression & 98.5\% \\ \hline Our proposed model & Multi-layer Perceptron & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The comparison of our proposed model with other studies on the same CKD dataset. able medical choices. According to this study's results, implications for medical diagnostics ML model development are substantial. Our technique provides a trustworthy and reliable tool for CKD prediction by improving upon previous efforts in data imputation, feature selection, and model explainability.
# Flexible Wearable Heart Rate Monitoring System and Low-Power Design: A Review
## 2 Overview of Flexible Wearable Heart Rate Monitoring Systems
### Technical Demonstration of Flexible Wearable Heart Rate Monitoring Systems  
This subsection demonstrates complete flexible wearable heart rate monitoring systems based on ECG and PPG technologies through several research cases. Thanks to their unique body-conforming designs, these systems can stably monitor heart rates over long periods, breaking through the temporal and spatial limitations of traditional monitoring methods. They exhibit unparalleled advantages, whether in real-time heart rate tracking during daily activities or precise monitoring in special situations.  
[PERSON] et al. [57] developed a flexible wearable heart rate monitoring system based on ECG technology using dry conductive flexible textiles coated with nickel/copper (Ni/Cu). They employed headphones and the dry conductive flexible textiles as carriers to collect ECG signals measured from the mastoid area behind the ears as input signals. These ECG signals were preprocessed through a specially designed instrumentation amplifier circuit and an analog filter, then computationally processed by a 32-bit ARM microcontroller, and finally outputted and stored in an SD card.  
Meanwhile, Yoshida et al. [55] developed a flexible wearable heart rate monitoring system based on PPG technology by integrating flexible silver ink circuits through multi-layer screen printing, as illustrated in Figure 2c. The system employs the MAX30102 sensor chip (It is manufactured by Analog Devices, Inc., headquartered in Norwood, MA, USA) to acquire PPG signals as the input and is equipped with an external accelerometer (RX122,which is produced by Kionix, Inc., based in Ithaca, NY, USA) and an external oscillator. By combining specific algorithms, it preprocesses the input signals and compensates for motion artifacts. Subsequently, the sensor hub integrated circuit (MAX32664, it is manufactured by Analog Devices, Inc., Norwood, MA, USA) receives the signals, computes the heart rate value via [PERSON] algorithms, and finally outputs the processed PPG signals to a smartphone through a Bluetooth Low Energy (BLE) module.  
In summary, the flexible wearable heart rate monitoring systems described in this subsection, which are based on ECG and PPG technologies, are all composed of four modules: the input of raw signals, preprocessing and computational operations of these signals through internal system modules, and the final output of the processed signals.
## 2 Study Area and Data
### Research Data and Processing
#### 2.2.6 Landslide Inventory Data  
The landslide inventory data were provided by the Institute of Geomechanics, Chinese Academy of Geological Sciences, and were stored in vector format (shp files) [16]. This dataset primarily includes historical landslide location information and associated attributes. The analysis of historical landslide data in the study area enables rapid understanding of loess' landslide morphological characteristics, laying the groundwork for subsequent summarization and analysis of optical imagery features associated with potential landslide hazards.  
Figure 3: DEM Map of the study area.
# Research on an Index System for the Earthquake Disaster Resistance Capability of Highway Tunnels
## 2 Analysis of Seismic Damage Scenes in Highway Tunnels
### Analysis of the Influencing Factors of Earthquake Disasters in Portal Sections of Highway Tunnels
#### 3.4.2 Lining Structure Cracking  
Lining structure cracking is a mild damage to the tunnel structure passing through the faulted fracture zones, but the damage degree is still greater than that of the ordinary section of the tunnel. During the Wenchuan Earthquake, Youyi Tunnel passed through a narrow fault crushing belt with a width of 0.5 m. Although the lining did not collapse, a large number of penetrating cracks appeared in the lining structure of this section. Zipingpu Tunnel passed through a fault crushing belt with a width of 3 m, resulting in a clear crack and a certain trend of lining cracks, and no lining structure collapse occurred. Lining structure cracking is formed by the influence of the giant shear force caused by staggered faults, which is the initial stage of lining structure collapse.  
In the case of narrow crushing belts, there will be no fatal damage [28], such as lining structure collapse, but there will still be a large number of lining cracks. This is due to the strong shear force generated at the fault crushing belts, but the width is narrow. The fault movement during an earthquake is small, and the lining can be supported by its own  
Figure 6: Fault tree of seismic damage senses of the highway tunnel body section.  
strength without collapse, but the formation of a large number of cracks still makes the tunnel dangerous, so it can be divided into engineering geological factors. The strength of the lining structure here is low, and no seismic measures are set up for the fault crushing belts, which are tunnel structural factors.  
In summary, the causes of seismic damage to highway tunnels crossing fault crushing belts can be summarized, and engineering geological factors include fault crushing belts. Tunnel structural factors include supporting structures and seismic measures. The FTA model of the seismic damage of highway tunnels is established, as shown in Figure 7.
# Nanotechnology and Early Human Development
## 2 Transplacental Drug Delivery System  
The placenta is a fetomatermal organ consisting of two components: the fetal placenta, which develops from the same blastocyst that forms the fetus, and the maternal placenta, which develops from the maternal uterus. The placenta is an important metabolic organ that acts as a lung, liver, and kidney for the fetus; it regulates the passage of several metabolic products between the maternal and fetal bloodstream (Figure 1). Minimizing the exposure of the fetus to drugs and reducing adverse effects in the mother are the main challenges faced by researchers in this field.  
Nanoparticles cross the placental barrier by paracellular passage (particles under 25 nm) and transcellular passage, which is an active process requiring multiple steps [5]. The transportation of nanoparticles through the placenta depends on the physico-chemical characteristics of these substances (e.g., size, shape, charge, and degree of hydrophilicity) as well as the conjugation with other particles to maintain drugs in the maternal bloodstream (i.e., polyethylene glycol and placental chondro sulfate A binding protein) or allow transplacental passage [3; 5]. The ultimate goals of nanoparticle administration during pregnancy are of paramount importance for clinicians and include the treatment of pregnancy complications, the prevention of preterm birth and related complications, and the promotion of fetal growth and wellbeing. These factors have an impact even on adult health.  
Surface-functionalized nanoparticles are candidates for the therapy of pregnancy complications as they could help prevent fetal transfer of specific substances and may enhance placental-specific drug delivery [6]. For example, prevention of premature birth, ectopic pregnancies, and fetal growth restriction have been studied in animal and in vitro models. However, human applications are still unexplored due to reasons including the risk of acute immune reactions (i.e., anaphylaxis); moreover, placenta is a species-specific mammalian organ and therefore observations from animal models cannot be automatically translated to humans.  
Another tool for the treatment of pregnancy complications and fetal conditions are short interfering ribonucleacide acids (siRNAs). These compounds may induce the silencing of a target gene through a protein complex and have high specificity for pathogenic targets. Some authors found that siRNAs loaded into liposome complexes were internalized by the placenta without being transferred to the fetus, enabling the possibility to selectively treat placental disorders [6]. Other authors developed and tested, in an animal model, a novel siRNA delivery system consisting of a lipid nanoparticle that protects the mRNA from clearance, allowing its distribution to the liver for the treatment of a severe inherited metabolic disorder: ornithine transcarbamylase deficiency [7].  
Figure 1: Schematic view of the placenta. Source: Anatomy & Physiology [4].
## 5 Understanding Decentralised Identity Systems
### Sovrin
#### 5.5.1 Technical Architecture  
The architecture of the Sovrin Network consists of four distinct layers:  
**The Ledger layer** maintains identity-related information such as DIDs, credential definitions, and revocation registers. Stewards verify transactions using a consensus process built on the RBFT algorithm. This ensures the data's confidentiality and integrity [65].  
**The Agent layer** facilitates secure, peer-to-peer connections. Edge and cloud agents manage the exchange of digital identities and credentials via the encrypted DIDComm protocol. This layer helps to maintain privacy and security for the communication of identity-related data between user, organization, and within the network [65; 66].  
**The Governance layer** is governed by the Sovrin Governance Framework (SGF). The SGF sets policies and standards for the system to be compliant with legal, security, and privacy requirements. The set guidelines are followed by all participants of the system, to maintain trust, autonomy, and security of the Sovrin ecosystem [65].
## 5 Numerical Results and Discussion
### Discussion  
Communication failure is a matter that cannot be ignored in data transmission. It has an important impact on the performance of the network. In order to minimize the negative impact of communication faults on network performance, a reasonable channel allocation mechanism is necessary.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Fixed Values** \\ \hline PU1 packets’ arrival rate \(\alpha_{11}\) & 0.3 \\ PU2 packets’ arrival rate \(\alpha_{12}\) & 0.2 \\ SU packets’ arrival rate \(\alpha_{2}\) & 0.3 \\ SU packets’ service rate \(\beta_{2}\) when the system state is normal & 0.5 \\ PU1 packets’ service rate \(\epsilon_{11}\) when the system is in a failure state & 0.4 \\ PU2 packets’ service rate \(\epsilon_{12}\) when the system is in a failure state & 0.3 \\ Communication repair rate \(r\) & 0.8 \\ The size \(k\) of the cache that holds SU packets & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameters with fixed values in the comparison experiment.  
Figure 4: Comparison of performance change trends of PU1 packets under the two mechanisms: (**a**) performance comparison in \(T_{P1}\); (**b**) performance comparison in \(B_{P1}\).  
In this section, we describe the performance trends of PU1 packets, PU2 packets, and SU packets in different network environments under the background of communication failure and repair based on the proposed channel allocation mechanism. Figures 1-3 could be summarized as follows. As the communication failure rate increased, the blocking rate of PU1 packets grew and the throughput decreased, and the blocking rates and loss rates of PU2 and SU packets increased while the throughput dropped. With the increase in repair rate, the above performance variation trends were opposite to those caused by the rise in the communication failure rate. At the same time, with the increase in PU1 or PU2 packets' arrival rate, the blocking rates and loss rates involved in the three types of packets (the loss rate not involving PU1 packets) increased. When the arrival rate of one type of packet increased, the throughput of the corresponding type of packet increased, while the throughput of the other types of packet decreased. The performances of these three types of packets were analyzed in detail and the possible reasons were explained, which helped us to better explore the impact of communication failure and repair on the system, as well as the difference in system performance in different network environments.  
In addition, we compared the communication failure and repair mechanism with classified PUs proposed in this paper with that with unclassified PUs. Figure 4 demonstrates that the proposed mechanism improved the throughput and reduced the blocking rate of PU1 packets, which proved the effectiveness of the proposed mechanism for real-time services.
## 6 Results and Discussions  
The proposed PODESCA and SBDMT have been applied on the 69-bus, 85-bus, and 118-bus RDSs. As mentioned before, BFSA was used to perform the load flow. For every system, the OPSC problem is solved by minimizing the system's total annual cost given in (2) considering the constraints given from (3) to (7).  
PODESCA is first applied without reducing the search space, i.e., the search space of the problem includes all the system's buses (excluding the slack bus), and the obtained results are then compared with those from several methods in the literature. After that, the proposed algorithm is reapplied but this time with reducing the search space by the SBDMT, where the results are thus compared without and with reducing the search space by the SBDMT. Furthermore, for every system, three different load levels are considered; light-, medium-, and full-load, with values of 50%, 75%, and 100% of the base-case, respectively. Thus, a comprehensive planning schedule is provided by deciding the locations and sizes of both fixed and switched SCs.  
According to the system's scale, PODESCA's parameters are set using a step-wise variation of stochastic parameters so that the best performance is achieved. For each system, 50 independent runs are executed to obtain the best solutions. Matlab-R2016a is used for coding and running the programs on a PC with an Intel Core processor (TM) i7, 3.2 GHz speed and 8 GB RAM.
# Real-Time Text-to-Cypher Query Generation with Large Language Models for Graph Databases
## 3 Related Work
### Graph-Based Query Languages
In their work, they create both a seq2 seq model extended with an attention mechanism and a transformer-based model to create Cypher queries. The advantage of these models is that the natural language input does not have to be pre-processed and can be converted directly into CQL statements without further auxiliary structures. They showed that the transformer based model is slightly more accurate in generating Cypher queries, while being almost 10 times more computationally efficient. With regard to the use of LLMs to generate Cypher queries, [PERSON] et al. [60] are the only paper found in this literature search to have taken such an approach. In their system, a named entity recognition is first performed on the user question in which the entities from the question are mapped to nodes in the graph. The results of this and the original question are then incorporated into several prompts which are used by ChatGPT to generate the CQL statement. In addition, they also tested different prompt strategies, as well as an improvement of the query by ChatGPT in case of error/empty result. However, the ChatGPT version used was not mentioned in the paper.
## 5 Discussion  
In our previous work, the research team pioneered a cross-domain interference diagnosis approach based on knowledge transfer, which, to the best of our knowledge, marked the first application of domain generalization in the field of interference diagnosis. The related outcomes have been widely implemented in localized service platforms and 5G positioning terminals.  
Building upon that foundation, the present study further investigates the mechanisms of interference suppression, analyzes the limitations of unified suppression strategies, and proposes a classification-driven differentiated interference suppression method. To validate its effectiveness, we compare the proposed approach with five representative baseline methods: AFF, TDPB, FDPB, KLT, and WPT. These baselines cover a broad range of commonly used GNSS anti-jamming strategies, including time-domain, frequency-domain, transform-domain, and subspace-based techniques. Experimental results demonstrate that our approach achieves low-complexity, real-time, and cost-effective interference mitigation, with significant performance improvements over that of classical methods such as KLT and WPT.  
Looking forward, our goal is to further advance this suppression method toward hardware-level implementation and conduct real-world testing in complex urban environments. In addition, we note that the proposed method operates at the baseband signal processing level and maintains standard-compliant output formats (e.g., C/N\({}_{0}\), PVT), ensuring compatibility with existing GNSS receiver standards such as RTCA DO-229. It also preserves signal integrity under unknown interference, which supports downstream applications like precise timing and robust navigation.
## 4 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{3}{c}{**MRAS**} & \multicolumn{3}{c}{**SMO**} \\ \hline Speed & \(0.08\omega_{\mathrm{ref}}\) & \(0.25\omega_{\mathrm{ref}}\) & \(0.4\omega_{\mathrm{ref}}\) & \(0.08\omega_{\mathrm{ref}}\) & \(0.25\omega_{\mathrm{ref}}\) & \(0.4\omega_{\mathrm{ref}}\) \\ \hline Signal loss & 21 ms & 11 ms & 23 ms & 28 ms & 34 ms & 23 ms \\ \hline Signal & \multirow{2}{*}{29 ms} & \multirow{2}{*}{13 ms} & \multirow{2}{*}{8 ms} & \multirow{2}{*}{23 ms} & \multirow{2}{*}{13 ms} & \multirow{2}{*}{8 ms} \\ interference & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 4: Classification times obtained in motor load conditions. Classification times for both types of classifiers are at a similar level. Classification times are, in most cases, less than 30 ms. It can also be inferred from the table that the detection time decreases with increasing speed. Example Transients showing the time of occurrence and classification of failures are shown in Figure 17.  
Figure 16: Speed, SMO detector, and neural classifier transients with motor load \(0.15\mathrm{T}_{\mathrm{N}}\) during signal interference (**a**) and cyclic signal loss (**b**).
## 3 Results and Analyses
### Illumination Analysis
#### 3.1.2 Summer Illumination Analysis  
The summer test times were 28, 29, and 31 July 2015. The weather of the three dates was thundershower, thundershower to cloudy, and cloudy and sunny (short shower), respectively. The light intensity of each indoor and outdoor collection site is shown in Figure 4. The light intensities during 9:00 and 16:00 under different weather conditions in summer were averaged for the comparative analysis of the light intensity with that of the outdoor site (Table 2).  
According to Figure 4 and Table 2, for the summer thundershower weather, the average light intensity of the outdoor site was 34.04 kLux, and the average light intensities of different indoor sites were between 8.30 and 19.39 kLux; for the thundershower to cloudy weather, the average light intensity of the outdoor site was 51.43 kLux, and the average light intensities of different indoor sites were between 8.47 and 26.16 kLux; during the cloudy and sunny weather, the average light intensity of the outdoor site was 60.31 kLux, and the average light intensities of different indoor sites were between 13.50 and 28.00 kLux. These results showed that weather conditions have an obvious impact on indoor light intensity. The average light intensities of different sizes were listed in descending order: outdoor > E > C > B > D > A.  
According to the comparison of the average summer indoor light intensity of the best site (E) and the poorest site (A) for daylighting, the differences in the three days of the two sites were 11.09, 17.69, and 14.50 kLux, respectively, and the average daily light intensity of site A was 58.87% of site C (81.1% in winter). This means that the distribution of the summer light intensity in the photovoltaic greenhouse was not even, mainly due to the larger direct light than scattered light in summer.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Outdoor** & **Site A** & **Site B** & **Site C** & **Site D** & **Site E** &
\begin{tabular}{c} **Compound** \\ **Average** \\ \end{tabular} \\ \hline Average light intensity & 27.23 & 6.48 & 6.95 & 7.99 & 6.69 & 7.29 & 7.02 \\ Light transmittance & & 23.79\% & 25.53\% & 29.35\% & 24.56\% & 26.77\% & 25.77\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average light intensity and light transmittance of the photovoltaic greenhouse in winter.  
Figure 4: Changes of the light intensities in the photovoltaic greenhouse under different weather conditions in summer: (**a**) thunder shower; (**b**) thunderstorm to cloudy; (**c**) sunny interval.  
The weighted calculations of the roof transmittance area and the photovoltaic area in proportion to their areas showed that the compound average transmittance rates were 36.82% (thundershower), 27.57% (thundershower to cloudy), and 27.57% (cloudy and sunny), and the mean value of the three weather conditions was 32.35%.