esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
Our systematic review identified 21 papers employing AI approaches for NPH detection. Most studies focused on differentiating NPH from other neurodegenerative disorders, particularly Parkinson's disease and Alzheimer's disease. Traditional ML methods--primarily SVM, RF, and LogReg--were used in eleven studies, while deep learning approaches, predominantly DCNN (six papers), represented the emerging trend in this field. Table 5 summarizes the specific applications, approaches, advantages, and disadvantages of these AI methods. Traditional ML methods and deep learning approaches showed distinct strengths and limitations for NPH classification. Traditional ML algorithms (RF, LogReg, and SVM) achieved classification accuracies ranging from 70% to 96%, with [32] reporting the highest performance (96.3%) using LogReg for differentiating NPH from AD. These methods excel in high-dimensional feature spaces and provide probabilistic interpretations of classification results [33,38], offering valuable clinical insight. However, their performance depends heavily on careful feature selection and engineering, as demonstrated by [41], where accuracy decreased from 95.0% to 89.3% when additional features were included. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Weighted Importance (\%)** & **Studies Reporting** \\ \hline Evans ratio & 21.3 & 4 \\ \hline Frontal horn length & 15.7 & 3 \\ \hline Callosal angle & 14.2 & 4 \\ \hline DESH & 11.8 & 3 \\ \hline Ventricular volume & 10.5 & 5 \\ \hline White matter hyperintensities & 7.6 & 2 \\ \hline Gray matter volume & 5.4 & 4 \\ \hline Midbrain dimensions & 4.8 & 2 \\ \hline CSF flow parameters & 4.2 & 3 \\ \hline Other features & 4.5 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Weighted average feature importance across studies. In contrast, deep learning approaches demonstrated consistently higher performance metrics, with accuracies ranging from 90% to 99%. Reference [47] reported the highest accuracy (99.1%) using DCNN with Emperor Penguin Optimization, surpassing traditional ML methods by 3-5 percentage points on average. The key advantage of deep learning lies in automatic feature extraction without manual engineering [39], enabling the discovery of complex patterns that might be missed by traditional approaches. This was evident in [28] work, where Grad-CAM visualizations revealed that the model autonomously focused on diagnostically relevant regions without explicit instruction.
# Efficient Inorganic Stabilization Materials for Chromium and Arsenic Pollution in Water and Soil
## 3 Mechanism of Chromium and Arsenic Stabilization
### Precipitation Mechanism  
Stabilization materials reduce the mobility and bioavailability of chromium and arsenic by adjusting soil pH or generating anions such as sulfate, carbonate, hydroxide, and phosphate ions, which precipitate with chromium or arsenic ions. For high-concentration chromium and arsenic pollution, the generation of low-solubility precipitates through chemical reactions is an important way to achieve long-term stabilization. [PERSON] et al. [86] treated As(V) with the synergistic effect of lime, limestone, and clay minerals. The addition of lime causes clay minerals to produce calcium silicate hydrate (CSH) and calcium aluminum hydrate (CAH) as heavy metal stabilizers. The produced Ca\({}_{3}\)(AsO\({}_{4}\))\({}_{2}\).4H\({}_{2}\)O precipitation was revealed by XRD (Figure 10e), to prove the chemical fixation effect of the calcium-based system on As. The leaching rate of soil As stabilized with this material is only 7% (Figure 10f). [PERSON] et al. [87] impregnated sepiolite (SEP) in Fe(NO\({}_{3}\))\({}_{3}\) to form ferric nitrate-modified sepiolite (NIMS), and SEP was impregnated in KMnO\({}_{4}\) and FeSO\({}_{4}\) to generate iron-manganese modified sepiolite (FMS). In NIMS, FeOOH adsorbs and precipitates As(III) and As(V), with a strong adsorption capacity for As. NIMS and FMS reduced As in brown rice by 30% and 25%, respectively. [PERSON] et al. [88] prepared MnFe\({}_{2}\)O\({}_{4}\)@Fh-EDA by combining manganese ferrite nanoparticles (MnFe\({}_{2}\)O\({}_{4}\)) with ethylenediamine-functionalized ferrite (Fh-EDA) for Cr(VI) adsorption. Some Cr(VI) undergo redox reactions with Mn(II) to generate Cr(III)-Mn(III) precipitates. MnFe\({}_{2}\)O\({}_{4}\)@Fh-EDA has an adsorption capacity of 51.36 mg/g, reducing Cr(VI) concentration from 20.0 mg/L to below 0.5 mg/L. Fe\({}_{3}\)S\({}_{4}\) was successfully composited with MXenes\({}_{8}\)[89]. The highly toxic Cr(VI) was reduced to less toxic Cr(III) through redox reactions via Fe(II) and S(-II). Final Cr(III) being precipitated as Cr(III)-Fe(III) hydroxide.
# A Design Method for Gammachirp Filterbank for Loudness Compensation in Hearing Aids
## 5 Conclusions  
In this study, the gammachirp filterbank was applied to the multichannel compensation algorithm of digital hearing aids, and a new channel division method was proposed based on human hearing characteristics. The gammachirp filterbank was employed to decompose and synthesize the input signal, and personalized loudness compensation was performed on the speech signal in each subband, which could effectively supplement the speech energy loss in different frequency bands for hearing impaired patients. The new channel division method made it possible to flexibly adjust the subband filter bandwidth during the hearing aid fitting process, to complete an individual filterbank design, and to achieve signal reconstruction with fewer channels and to meet different audiogram matching requirements. Future studies that include extensive subjective experiments are warranted to optimize the performance of the proposed algorithm. The other possible research trend would be to focus on the data-driven channel division method and loudness compensation model.  
The authors contributed equally to this work. Conceptualization, R.G. and R.L.; methodology: R.G. and C.Z.; formal analysis, Q.W.; writing--original draft preparation, R.G.; writing--review and editing, R.L.; visualization, Q.W.; supervision, C.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key Research and Development Program of China under grant no. 2020 YFC2004003 and 2020 YFC2004002, and the National Natural Science Foundation of China under grant no. 62001215.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**SPL (dB)** & \begin{tabular}{c} **The Recognition** \\ **Rate of the Original** \\ **Speech (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **The Recognition Rate of** \\ **Compensation Signal** \\ **Based on Gammatone** \\ **Filterbank (\%)** \\ \end{tabular} &
\begin{tabular}{c} **The Recognition Rate of** \\ **Compensation Signal** \\ **Based on Gammatone** \\ **Filterbank (\%)** \\ \end{tabular} \\ \hline
10 & 0 & 0 & 0 \\
20 & 0 & 0 & 0 \\
30 & 0 & 4 & 16 \\
40 & 10 & 12 & 22 \\
50 & 32 & 38 & 46 \\
60 & 58 & 68 & 76 \\
70 & 68 & 80 & 88 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the speech recognition rate.
# Capturing Features and Performing Human Detection from Human Gaits Using RFID
## Materials and Methods
### System Design
#### 2.3.2 Signal Preprocessing
After the new data segment is obtained, the volatility of the data is judged; when the data fluctuation is greater than \(\sigma\), the data is beat, the current corresponding index value is recorded, and then the search continues backward; when other values are also greater than \(\sigma\), the index value is refreshed; if not, the judgment is over, and the current index value is recorded. For the current index value, it is necessary to further determine whether the index is generated by gait or environmental interference, so we set the action to be valid when the maximum fluctuation value in the intercepted signal is greater than \(1.5~{}\sigma\). Finally, the start and end indexes of the data were saved to complete the segmentation task. _Sliding window._ For the partitioned gait data, we need to perform gait detection and segmentation for each region. From Figure 8, we learn that the signal has obvious fluctuations when there are gait features, while the signal tends to a stable state when there are no gait features. In addition, each set of complete gait data contains four states: stillness-start-movement-end. Therefore, we detect gestures by using variance. We set a time window \(T\) of length \([\Delta t-T,\Delta t+T]\) within each region \(\Delta t\), where \(T=0.2\) s. Then we calculate the average standard deviation \(\sigma_{\Delta t}\) at \(\Delta t\), and choose the smallest \(\sigma_{\text{min}}\) as the variance threshold. The sliding window rule is as follows:  
\[\sigma_{\Delta t}=\sqrt{\frac{\sum_{i=\Delta t-T}^{\Delta t+T}\left(S(i)-S \right)^{2}}{n}} \tag{7}\]  
\[\text{Maximize}:~{}\Delta t_{end}-\Delta t_{start},\sigma_{t}\geq\sigma_{ \text{min}},~{}t\in[\Delta t_{end}-\Delta t_{start}] \tag{8}\]  
\[\Delta t_{end}-\Delta t_{start}\geq 0.7\,\text{s} \tag{9}\]  
In Equation (7), \(n\) represents the number of samples within \(\Delta t\) and \(S\) is the average value of samples. Meanwhile, in order to avoid other interference, we set the duration of each sliding window to \(0.7\) s. \([\Delta t_{end}-\Delta t_{start}]\) is the detected region. _Data augmentation._ Since the neural network needs a large amount of data, we use the data augmentation scheme to increase the diversity of data and the robustness of the system. Specifically, we refer to the data augmentation method in [37] and scale the original data along the timestamp by different multiples \(\beta\in\{0.6,0.8,1.2,1.4\}\). In addition, Gaussian noise is added to the original data and the scaled data to make the data have some errors, so as to better simulate the real situation.
# Blockchain for the Healthcare Supply Chain: A Systematic Literature Review
###### Abstract  
A supply chain (SC) is a network of interests, information, and materials involved in processes that produce value for customers. The implementation of blockchain technology in SC management in healthcare has had results. This review aims to summarize how blockchain technology has been used to address SC challenges in healthcare, specifically for drugs, medical devices (DMDs), and blood, organs, and tissues (BOTs). A systematic review was conducted by following the PRISMA guidelines and searching the PubMed and Proquest databases. English-language studies were included, while non-primary studies, as well as surveys, were excluded. After full-text assessment, 28 articles met the criteria for inclusion. Of these, 15 (54%) were classified as simulation studies, 12 (43%) were classified as theoretical, and only one was classified as a real case study. Most of the articles (n = 23, 82%) included the adoption of smart contracts. The findings of this systematic review indicated a significant but immature interest in the topic, with diverse ideas and methodologies, but without effective real-life applications.  
blockchain; distributed ledger; supply chain; healthcare; vaccines; medical device; solution; innovation +
Footnote †: journal: _Applied_  
understand and meet the needs of patients and providers. By better understanding and managing their SC, healthcare organizations can improve patient care and drive positive outcomes for both patients and providers [4; 5].  
An SCHM aims to improve clinical outcomes and ensure patients' and professionals' safety while controlling costs [4; 5]. Managing a SCHM implies higher costs for providers and heavier dependence on third parties as compared to other industries [6]. Furthermore, an SCHM presents unique challenges, as it is usually decentralized, lacking in standardized procedures (financial or contractual) among physicians, hospitals, and patients, and subjected to regulatory restraints [7].  
Policymakers, drug manufacturers, the pharmaceutical industry, and other institutions and structures, such as hospitals, clinics, and pharmacies, are usually the main stakeholders of SCHMs. On the one hand, an SC ensures the distribution of industry-derived products, such as drugs and medical devices (DMDs), but on the other hand, it must deal with patient-derived products, such as blood, organs, and tissues (BOT) [7]. A summary of an SCHM is represented in Figure 1.  
Emerging technology needs to be taken into account by clinicians, given that upcoming technologies are looming over their everyday practice. Getting acquainted with technologies will be imperative, as understanding how they work will ensure that patients receive more reliable care and more secure data processing, not to mention the medical-legal implications that will benefit from such applications.
## 6 Results
### Sensitivity Analysis  
RES is intermittent which can be affected by any variation of sizing, meteorological or economic data. The sensitivity analysis is a method that helps to select and/or to expect the optimal configuration of the hybrid system. The sensitivity analysis in this paper is implemented on the best scenario of the proposed, i.e., the PV/Biomass in the Aswan region. The selection of the sensitivity variables is based on the sizing of components in order to analyze the effect of sizing variation on four factors which are NPC, LCOE, LPSP and the Availability index.  
Figure 11 shows the effect of variation in the sizing of PV and biomass units on the NPC. The PV sizing is highly impacted by the total cost of the hybrid PV/Biomass system, which means that in the case of reducing the area of PV units the NPC is reduced too. On the other hand, if the area covered by PV modules is increased, the NPC increases too. The variation in the sizing of biomass unit is increased throughout the interval \(-20\) to \(20\) slowly and it has no noticeable impact on the NPC anyway. Figure 12 shows the effect of variation of PV and biomass sizing on the LCOE. The NPC and the LCOE are linked with a linear equation which means that they have the same shape. The LCOE reached \(0.08\) $/kWh when the area of the PV system is reduced by \(20\%\). Figure 13 shows the impact of variation in the sizing of PV and biomass systems on the LPSP. The impact of PV size is very important for the LPSP, because when the size of the PV system is increased the LPSP is enhanced, mainly in the \(-20\%\) to \(0\%\) interval. When the PV size is changed in the interval of \(0\%\) to \(+20\%\), the LPSP is increased to \(2\%\) while when the PV size is changed to \(-20\%\), the change in LPSP equals \(16.4\%\) which is a very bad sign for system building. The Biomass system does not affect the value of the LPSP and the transition between \(-4\%\) to \(0\%\) is explained as the obtained sizing of the system is optimum. Figure 14 shows the impact of the variation of PV and Biomass sizing on the availability index. The availability index enhanced exponentially with the increase in the PV sizing. In the interval between \(-20\%\) and \(0\), availability progresses quickly, while after zero, the availability begins to be stabilized and it is clearly shown in the interval between \(+12\%\) and \(+20\%\).  
Figure 11: Sensitivity analysis application for net present cost (NPC).  
Figure 12: Sensitivity analysis application for Levelized Cost of Energy (LCOE).  
Figure 13: Sensitivity analysis application for Loss of Power Supply Probability (LPSP).  
The PV system through this analysis is demonstrated as a very important element for having a high hybrid systems criterion. However, the Biomass system helps the PV to satisfy the constraints and its variation does not have a serious impact on the performance of the hybrid system.
## 3 System Model
### Network Model  
The proposed work assumes the random deployment of N-sensing nodes that, once deployed, are stationary. The deployed nodes are homogeneous in nature, with identical initial energy and hardware. The basic working principle is divided into rounds as in [37], where each round comprises local data gathering by the sensing nodes, which is forwarded to the cluster head of the particular clusters. On receiving the data, the cluster head aggregates the received data, removes the redundancies and transmits these data to the base station (BS). In between the successive rounds, the nodes are assumed to switch off their radios in order to conserve their batteries. The following assumptions have been considered for this purpose:  
**Assumption 1**.: _All the sensing nodes, once deployed, and the base station are stationary._  
**Assumption 2a**.: _All the sensing nodes start with the same initial energy (in the case of BS positioned at the centre of the deployment)._  
**Assumption 2b**.: _The deployment is equipped with advanced nodes in incremental fashion with increasing distance from the BS as per Lemma 2 (in the case of BS positioned outside the deployment area)._  
**Assumption 3**.: _The BS controls the clustering process and has high computational power and is also not energy-constrained._  
**Assumption 4**.: _The BS is within the direct communication range of all the nodes and thus the elected CHs (which communicate directly with the BS)._  
**Assumption 5**.: _The sensing nodes sense the region at a fixed rate and then communicate the sensed data to the respective CHs._  
**Assumption 6**.: _The sensors can adjust the transmission power as the distance to the target changes._  
**Assumption 7**.: _The sensors are homogeneous in nature and energy-constrained._  
The above-listed assumptions are taken into consideration in order to study the performance of the proposed scheme in line with the other popular schemes mentioned in the paper and highlight the improvements of the proposed scheme in terms of the common parameters.
# An Improved Multi-Objective Grey Wolf Optimizer for Aerodynamic Optimization of Axial Cooling Fans
## 1 Introduction
Despite these improvements, most existing MOGWO variants focus on structural or parameter-level adjustments, lacking a deeper theoretical redesign based on quantum computation principles. Moreover, few approaches explicitly exploit the Bloch coordinates of qubits to enhance exploration-exploitation dynamics in GWO-based multi-objective search. To address this gap, this study introduces an improved multi-objective grey wolf optimizer (IMOGWO) and evaluates its performance using benchmark functions and a real engineering application. The IMOGWO utilizes the Bloch coordinates of qubits to ensure a high-quality initial population. By integrating the concepts of Manta Ray Foraging and associative learning, information exchange between populations and the search capability of the IMOGWO are enhanced. These innovative strategies are incorporated into the original GWO algorithm to achieve better convergence and a more diverse set of high-quality solutions for multi-objective optimization. Detailed improvements are presented in Section 2. In Section 3.1, the practical effects of the IMOGWO are demonstrated through experimental validation using five benchmark functions. The results are compared with three well-known multi-objective algorithms: NSGA II, MOGWO, and MOMVO, showcasing the IMOGWO's superior coverage and convergence. In Section 3.2, the IMOGWO is applied to a multi-objective process to optimize the aerodynamic performance of an axial cooling fan. Finally, the main conclusions are drawn in Section 4.
## 2 Materials and Methods
### Performance Measures  
The performance measures for classification as well as segmentation for HLS are overall- and average- accuracies, including Kappa statistics. We have calculated the Overall Accuracy (OA), Average Accuracy (AA) [28], and Kappa (\(\kappa\)) statistics [48] using the following relationships as given by \(\mathrm{OA}=\frac{\sum_{i=1}^{N}y_{i}}{N}\), where \(\mathrm{y_{i}}\) represents true predicted instances and \(\mathrm{N}\) is the total number of actual instances in the experiment. Similarly, \(\mathrm{A}=\frac{\sum_{i=1}^{N}\frac{y_{i}}{L_{i}}}{L}\), where \(y_{i}\) represents true predicted instances of the ith class, \(\mathrm{L_{i}}\) is the number of actual instances of ith class while \(\mathrm{L}\) indicates the total number of classes in the experiment, and \(\kappa=\frac{\mathrm{P}(y)-\mathrm{P}(y)}{-\mathrm{P}(y)}\), where \(\mathrm{P}(x)\) represents the actual observed agreement and \(\mathrm{P}(y)\) represents the chance agreement. This measure is used to test inter-rater reliability. It shows the reliability of the data collected in the experimentation having the right representation of measured variables [49].
## 3 Concept and Architecture of DigiHoriRobot
### Functional Capabilities and Digital Execution Cycle  
The initial deployment of DigiHortiRobot focused on demonstrating the system's ability to autonomously execute core horticultural tasks, specifically seeding and harvesting, by leveraging predictive simulation, real-time data integration, and bidirectional synchronization between the Physical and Virtual Layers. These capabilities are supported by a unified architecture that combines multimodal perception, semantic mapping, and AI-based decision support.  
Rather than functioning as a passive monitoring system, DigiHortiRobot transforms collected data, such as plant imagery and environmental sensor streams, into structured digital representations that drive action planning and validation. These entities are synchronized with the virtual map of the greenhouse environment, allowing a simulation-based assessment of interventions prior to execution. The virtual layer models not only geometric layouts and plant attributes, but also encodes operational constraints and expected outcomes for planned actions.  
This architecture enables the system to autonomously propose and evaluate alternative intervention strategies. Farmers can specify desired crop attributes (e.g., ripeness index or harvest diameter), and the system will simulate candidate operations, rank outcomes using predictive metrics, and suggest optimal plans. Once validated by the user, therobotic platform executes the selected strategy. Execution results are logged and used to incrementally update the decision models, ensuring adaptive behavior over time.
# Punching Shear Stress in Post-Tensioned Transfer Plate of Multi-Story Buildings
## 4 Analytical Model
### Modeling of the Post-Tensioned Transfer Plate  
Classical shell element refers to a thin-shell element that follows [PERSON]'s plate theory, which neglects transverse shear deformation. However, for thick members with span-to-depth ratio of 10:1 to 5:1 or less, the effects of shear deformation tend to increase [24].  
Since the span-to-depth ratio of the transfer plate ranges from 5:1 to 3:1, it is considered a thick element and highly affected by shear deformation. So, the use of [PERSON]'s plate theory is not appropriate. Whereas the Mindlin-Reissner plate theory allows for a constant strain and shear stress in the transverse direction. Therefore, the transfer plate was modeled using the thick-shell element and meshed into 0.2 m size.  
To reflect stress concentration due to arch action at the shear wall above the transfer plate, 3 stories of the shear wall have meshed to the same size as the transfer plate.  
The post-tensioning force is considered using prestressing tendon element in modeling. The tendon element in ETABS is modeled as equivalent load that acts upon the embedded element, which is the transfer plate element in this study, based on the vertical and horizontal profile of the modeled tendon. The equivalent load acts on the mesh node of the transfer plate. This method is commonly used in the linear design procedure of prestressed concrete. The properties of the prestressing tendon are summarized in Table 2. The tendon is assumed to be a bonded 7-strand wire. To address prestress losses, anchorage slip was assumed to be 6 mm, and wobble coefficient (\(K\)) and curvature coefficient (\(\mu_{p}\)) presented in ACI 318-08 [25] was applied.