esa-sceva/satcom-chunk-collection huggingface dataset:
# Patient-Tailored Dementia Diagnosis with CNN-Based Brain MRI Classification
## 3 Results
### Proposed CDSS for Dementia Grad-CAM  
Grad-CAM, a class-discriminative localization technique designed to enhance the interpretability of CNN-based models' outputs, was employed in this investigation. The method produced class-specific feature maps, visualized as heatmaps overlaying the output images, highlighting the regions relevant to the prediction [43].  
The objective was to determine whether the highlighted areas aligned with the neurodegenerative patterns of brain atrophy associated with either MCI or AD as documented in the medical literature.  
It was observed that models with a deeper structure and, hence, more convolutional layers (with more filters within them) generally output more accurate predictions and better localized the brain atrophy patterns with the heatmaps, as shown in Figure 5.  
The highlighted areas often corresponded with key structural biomarkers of dementia. Specifically, Grad-CAM frequently emphasized the periventricular regions, suggestive of ventricular enlargement, and the medial temporal lobes, where hippocampal volume loss and cortical thinning are typically most pronounced in MCI and early AD. The heatmap visualizations closely reflected the known patterns of structural neurodegeneration, particularly in temporoparietal regions.  
Cortical thickness measures, particularly when computed using tools such as FreeSurfer or ANTs, offer sub-millimeter precision in detecting focal cortical atrophy [13]. In parallel, a change in the hippocampal volume remains one of AD's most well-established imaging biomarkers, though its interpretation typically requires readjustment for head size. While Grad-CAM was not explicitly integrated with the mentioned quantitative measures in this particular experimental approach, its potential to complement structural biomarkers is promising. Future research could explore how co-analyzing Grad-CAM visualizations with cortical thickness and volumetric hippocampal data may enhance interpretability and provide more nuanced insights into a CNN-based dementia diagnosis.  
Figure 5: Visualization of the Grad-CAM heatmaps overlaid on the sample outputs, highlighting specific brain areas responsible for the prediction: (**a**) Heatmaps generated by a shallow CNN-based model with a small number of convolutional layers and filters; (**b**) Heatmaps generated by a deep CNN-based model with a large number of convolutional layers and filters.  
As shown in Figure 6, the model output included the predicted dementia classification (type) paired with the confidence score and suggested optimal clinical management, further supporting clinical decision-making.
###### Abstract  
This paper proposes the design of a single radiator monopulse antenna with a substrate integrated waveguide (SIW) multi-mode feeding system to reduce the aperture size and simplify the feeding structure. The proposed antenna can effectively reduce the overall system size by using a single patch radiator, compared to conventional systems that require multiple radiators. In addition, to simplify the structure of the pattern comparator we use a SIW multi-mode feeding system with \(\text{TE}_{10}\) and \(\text{TE}_{20}\) modes. To verify the feasibility, the patch antenna with the SIW multi-mode feed is fabricated, and its antenna characteristics of reflection coefficients, mutual coupling, and radiation patterns are measured in a full anechoic chamber. The accuracy of the direction of arrival (DOA) estimation is further improved by applying the derived calibration factor to the resulting sum and difference ratio. The results demonstrate that the proposed antenna is suitable for compact and lightweight monopulse systems with the single radiator and the simple multi-mode feeding network.  
single radiator multi-ports; SIW feeding network; monopulse antenna; multi-mode antenna +
Footnote †: journal: Applied Sciences
## 5 Results
### Envelope-Based Fault Region Detection  
Figure 13 shows the performance of the created envelope-based fault region detection algorithm with recorded events from different relays and meters. In the test, we consider various event types (i.e., SLG, LL, LLG, and 3 LG) to indicate the proposed method precision. As shown in Figure 13, each type and phase type of the signal are assessed to locate the start/end points of the fault region. Also, the algorithm defines the event based on the user's defined threshold values. Subsequently, Figure 13a shows an example of the SLG type of signal recorded by the SEL735 (Feeder 2) meter. The result indicates that the proposed method can identify which phase has a fault and its start/end points. As shown in Figure 13b, the test is also applied for the LL type using the SEL351S recorded data. The algorithm still maintains its prediction performance. The algorithm's performance is also verified for LLG and 3 LG fault types, analyzing the recorder signals by SEL451 and SEL735 (Feeder 2), respectively.  
Figure 12: The SEL-451 recording signal samples, including (a) currents, (b) voltages, and (c) digital signal plots for 3 LG fault test.
## 4 Case Study
### Results
#### 4.5.3 Revised Human-Only Decision  
The performance outcome of the hybrid workflow is shown in Table 5.  
The hybrid workflow inclusion/exclusion decisions agreed with the human-only process in 384 out of 390 articles.  
The hybrid workflow also improved the accuracy of the study selection process by overturning six decisions that were initially erroneous in the human-only SR. In the inclusion scenario, three (3/170 articles) papers were originally excluded by human reviewers, but the hybrid workflow identified that they did meet the inclusion criteria, which was incorrectly missed by the human-only SR. Upon further discussion and re-evaluation by the review team, it was agreed that these papers were indeed relevant and should have been included in the SR.  
In the exclusion scenario, there was a total of 220 exclusions in the hybrid method. There was 100% (114/114 articles) agreement with the articles that the human process excluded during the _title_ and _abstract_ screening in the human-only SR. For the rest, the human reviewers initially included several papers, but the hybrid workflow flagged three (3/106 articles) of these as not meeting the necessary criteria. The review team's deliberation confirmed that these papers were not appropriate for inclusion and should be excluded.  
Ultimately, for the LLM-assisted workflow, there was a total of 6/390, i.e., a 1.53% mismatch, compared with the fully human-performed SR. However, in each of the six cases, the proposed workflow improved the results.
## 2 Materials and Methods
### Microclimate and Eddy Covariance (EC) Measurements  
Net carbon exchange and microclimatic variables were continuously recorded using an Eddy Covariance (EC) tower. In Rainfied, the EC station was installed on 13 August 2020, 12 days post-planting (DPP), while in Irrigated, the EC station was installed on 3 February 2021 (12 DPP). The EC measurements for Rainfied went until the end of the canopy life cycle, 22 November 2020 (113 DPP). The EC measurements for Irrigated went until the end of the canopy life cycle, 9 June 2021 (138 DPP).  
The EC tower included an IRGASON with an open-path gas analyzer (EC 150, Campbell Scientific, Inc., Logan, UT, USA) and a 3D sonic anemometer (CSAT3A, Campbell Scientific, Inc., Logan, UT, USA), both are operated by a separated electronic module (EC100, Campbell Scientific, Inc., Logan, UT, USA). Raw data was recorded at a 10 Hz sampling frequency using a high-performance datalogger (CR1000X, Campbell Scientific, Inc., Logan, UT, USA). The tower height for both locations was calculated according to the equation \(hEC=Zd+4\) (\(hc-Zd\)) [20]; where, \(hEC=\) EC installation height, \(Zd=\) zero plane displacement (0.63 m) and \(hc=\) average height of the crop (0.9 m). The IRGASON was placed at a 1.7 m height. The IRGASON azimuth was 45\({}^{\circ}\) for Rainfed and 175\({}^{\circ}\) for Irrigated, corresponding to the prevailing wind directions recorded by the sonic anemometer, three weeks before starting evaluations.  
The following sensors were also installed: a Net Radiometer, to measure the incoming and outgoing short-wave and long-wave radiation (Rn), NR-LITE2 (Kipp & Zonen B.V., Delft, The Netherlands) at a height of 2 m, three sensors for the measurement of photosynthetically active radiation (PAR) (CS310, Apogee Instruments, Inc., Logan, UT, USA) positioned at a height of 0.5, 1, and 2.2 m; a pyranometer sensor (CS301, Apogee Instruments, Inc., Logan, UT, USA) at a height of 2 m that measures the total incident radiation; two temperature (Tair) and a relative humidity air sensor (HR) installed at a height of 1 and 2 m, respectively (HygroVUE(tm) 10, Campbell Scientific, Inc., Logan, UT, USA); two multiparameter smart sensors (CS655, Campbell Scientific, Inc., Logan, UT, USA) installed at a depth of 9 cm and 15 cm to monitor soil volumetric water content (SWC), bulk electrical conductivity, and soil temperature, and four type E thermocouples (TCAV-Averaging Soil Thermocouple Probe, Campbell Scientific, Inc., Logan, UT, USA). Soil heat flux density, G (W m\({}^{-2}\)), was obtained using the average value between the measurements of two HFP01 sensors (Hukesflux Thermal Sensors B.V., Delftechpark, Delft, The Netherlands), installed 88 cm apart at a depth of 8 cm. Climatic data were recorded every 5 min and averages were integrated on a half-hourly basis. Precipitation data were collected every day using a rain gauge connected to a datalogger (Oregon Scientific, Inc., Tualatin, OR USA) at the height of 2 m.
# Finite Element Approaches to Model Electromechanical, Periodic Beams
## 1 Introduction  
Periodic structures can be defined as structures consisting of a series of repeating segments with the same physical properties and sizes. Theoretical investigations of such structures have usually been carried out by the assumption of infinite dimensions [1; 2; 3; 4; 5]. However, certain features of periodic structures may also manifest even in the case of structures of finite dimensions if they include a sufficient number of segments.  
One of specific features of periodic structures are band gaps in their frequency spectra. Band gaps define frequency ranges within which signals cannot propagate within these structures. The locations and the widths of these gaps in the frequency spectra are strongly dependent on the size of the unit cell and such material properties as modulus of elasticity [6; 7; 8]. These special features of periodic structures can be employed for very efficient vibration damping. On the other hand, active vibration damping methods include techniques that use piezoelectric materials. Structures with active piezoelectric elements enable one the conversion of mechanical vibrations to electrical vibrations and thus to control damping properties of the system. Therefore, only the balance between passive and active damping allows one to maximise the effectiveness of the damping process.  
Additionally, while modeling periodic structures, the influence of features resulting from the application of a particular numerical model, on the results of calculations, should be taken into account carefully. Almost every computational model of a discretised structure (finite element method (FEM) or time domain spectral finite element method (TDSFEM) models), has certain characteristics of a periodic structure. Therefore, it is worth to analyse if certain features of periodic structures may be utilised in a directed manner in order to make practical use of the unusual behaviour of suchstructures. This approach may potentially allow to reduce, or enhance, periodic properties of the computational model.  
In this paper, the authors propose to combine all the aspects mentioned above. They propose a special numerical model of the beam with active piezoelectric elements, by means of which the dynamic characteristics of the beam can be analysed and the width of band gaps can be controlled.
## 6 Discussion  
### Direct Associations  
This paper employed a systematic literature review and meta-analysis to investigate the determinants of adopting Industry 4.0 technologies in various sectors, enrich the knowledge of Industry 4.0 technology adoption, and reach accurate conclusions beneficial to different sectors. Three pairwise relationships (i.e., PU-BI, PEOU-BI, and SI-BI) were significant and positive. Specifically, this study involved 47 papers that investigated Industry 4.0 technology acceptance to examine the effect sizes of the correlations between PU, PEOU, and SI with BI. The findings of this study were robust and reliable, with more included papers than the work of [PERSON] and [PERSON] [25], who collected only 12 papers. H1 was supported, with PU showing the strongest effect on BI among the three constructs. This finding underscored that when users perceive Industry 4.0 technologies as useful, they are more likely to adopt them, a finding consistent with prior research [92]. H2 was also supported, indicating that ease of use remains a key driver of adoption. Technologies perceived as user-friendly are more readily accepted, highlighting the importance of usability in implementation strategies. H3 was supported as well, showing that SI significantly affected BI. This finding suggested that peer expectations, organizational norms, and leadership support all play a role in technology uptake. This study also explored the role of contextual moderators in explaining variations in effect sizes. Beyond confirming TAM assumptions, this study made a theoretical contribution by examining contextual moderators, such as sector, organization size, and region, to explain variability in effect sizes. This study highlighted the need to adapt TAM to specific Industry 4.0 contexts, offering a path for refining the model and extending its applicability in complex adoption environments.  
In the context of Industry 4.0, the impact of PU on BI is particularly significant, which is likely due to the industrial sector's high emphasis on the actual benefits and performance improvements brought by technology [130]. Compared to PEOU and SI, PU can more directly reflect the potential of Industry 4.0 technology in improving production efficiency, reducing costs, and optimizing processes, thus becoming a key driving factor for organizations to adopt Industry 4.0 technology decisions [153]. In addition, decision-making in industrial environments tends to be rational, emphasizing investment returns and value creation, which further strengthens the core role of PU in adopting intentions [6].
# Robust Optimization for Household Load Scheduling with Uncertain Parameters
## 2 Mathematical Model  
### Uncertain Parameters  
In household load scheduling, the outdoor temperature and hot water demand for the next day are representative uncertain parameters that are closely affected by the randomness in the environment and human behaviors and cannot be predicted precisely in practice. Thus, the deviation from the forecast value will lead to the ineffectiveness of the original optimal schedules especially for the loads associated with the uncertain parameters.  
To formulate the uncertainties, it is assumed that the uncertain parameters vary in given intervals (i.e., the uncertainty sets) of which the boundaries are set around the predicted values. In practice, the uncertain parameters may take any value in the uncertainty set. Note that no complex probability distribution is assumed within the uncertain parameters.  
According to the analysis in [13] and [22], the actual outdoor temperature will fluctuate around the forecast values. Thus, the uncertainty set for the outdoor temperature can be described as follows:  
\[\bar{\theta}_{out,i}\in[\overline{\theta}_{out,i}-\bar{\theta}_{out,i}, \overline{\theta}_{out,i}+\bar{\theta}_{out,i}] \tag{1}\]  
where \(\bar{\theta}_{out,i}\) represents the outdoor temperature (which is uncertain during the scheduling stage) at the \(i\)th time step of the next day. \(\overline{\theta}_{out,i}\) and \(\bar{\theta}_{out,i}\) are the forecast value and the maximum deviation value of the outdoor temperature considered at the \(i\)th time step of the next day. Generally, the forecast values are obtained through the weather forecast issued by the local meteorological department. The maximum deviation considered can be confidently summarized by using sufficient historical data. If no sufficient data is available, it can be set as a certain percentage of the forecast value as an approximate estimation.  
According to [23], the uncertainty of hot water demand comes from random human behavior, such as an extra shower that will lead to extra usage of hot water. Therefore, the uncertainty set for the hot water demand can be described as follows:  
\[\bar{d}_{i}\in[\overline{d}_{i},\overline{d}_{i}+\hat{d}_{i}] \tag{2}\]  
where \(\hat{d}_{i}\) represents the uncertain hot water demand at the \(i\)th time step of the next day; \(\overline{d}_{i}\) and \(\hat{d}_{i}\) are the forecast value and maximum extra usage of the hot water demand at the \(i\)th time step of the next day.  
To respond to flexible electricity prices, the household load scheduling problem is formulated into a mathematical programming problem, of which the objective is to minimize the next-day electricity bill for a customer and the constraints are the operational and comfort limits of the appliances. The solution of the programming problem are the operational schedules for every appliances. The formulation of the problem is detailed below.
# Lithological Mapping Research Based on Feature Selection Model of Relief-RF
## 3 Material and Methods
### Feature Selection and Lithological Mapping Method
It has the ability to define non-linear decision boundaries in high-dimensional variable space by solving a quadratic optimisation problem [60]. This method is embedded in ENVI 5.3 software. The optimal procedure aims to maximise the margin between the closest training data and the hyperplane, therefore minimising the confusion between classes. In practice, SVMs use a subset of training data (called a support vector) to partition the feature space without assumptions on the statistical distribution of the training data. When referring to classification based on remote sensing data using SVMs, it does not usually mean a linear SVM but a nonlinear separable SVM, which requires a nonlinear kernel function to map the classified data set to a higher dimensional feature space for linear hyperplane [17]. The usual kernel types are linear, polynomial, radial basis function and sigmoidal. In the current work, the radial basis function kernel was yielded. The radial basis function is the most widely used kernel type and is versatile when the penalty parameter is at 100 [61]. Hence, a penalty parameter of 100 and gamma in kernel function of 0.05 were used in this study. In this study, the SVM was utilised as a comparison model with Relief-RF for lithological mapping based on ENVI 5.3 software. For the purpose of this research, ArcGIS 10.2, ENVI 5.3 and Matlab 2021 were used. These three types of software were mainly used for all the calculations, spatial analysis and feature extraction, such as lithological map modification by visual interpretation and field surveys, training and test dataset selection and lithological mapping research, etc. The confusion matrix was used to compare different datasets for lithological mapping, and the producer accuracy, user accuracy, average accuracy (the average of user and producer accuracy), overall accuracy and Kappa coefficient were determined.
## 2 Materials and Methods
### Meteorological Data
The process was therefore as follows. In the first iteration, nearest point determination based on latitude and longitude took place, i.e., the closest geographic location was found. The minimum Euclidean distance for  
Figure 2: Structure of meteorological data and an example of visualization of these data as a plot of wind direction (wd) and wind speed (ws) relative to geographic latitude (lat), longitude (lon), and altitude (alt). latitude and longitude between the flight trajectory point (\(lat_{P_{p}}\) and \(lon_{P_{p}}\)) and all points in the meteorological dataset (\(lat_{Q}\) and \(lon_{Q}\)) was calculated as  
\[d_{horizontal}=\underset{q\in Q}{\arg\min}\sqrt{(lat_{P_{p}}-lat_{Q})^{2}+(lon_{ P_{p}}-lon_{Q})^{2}}. \tag{3}\]  
The indices \(k\) of the closest data points in the dataset from the perspective of geographic location, determined using the computed \(d_{horizontal}\), were pinpointed. These indices signify the closest data points in terms of their horizontal locations from the original dataset and also correspond to specific altitudes. Hence, utilizing \(k\), one can choose the vector of altitudes associated with these nearest data points, where horizontal positions are given by latitude and longitude. From this resultant vector \(A\), it is feasible to determine the closest point regarding altitude by employing an analogous method as outlined in Equations (2) or (3). Therefore, it is possible to determine the closest geographic location and then assign it the nearest corresponding altitude. If we refer to the nearest altitude point as \(l\), then the desired meteorological data regarding wind speed and direction will be located at position \(Q_{k_{l}}\). The above outlines an approach for the selection of meteorological information for a specific flight point. Following this, the headwind or tailwind, defined as the wind component aligned with or against each point (\(P_{p}\)) along the flight trajectory, was computed. Tailwind/headwind was considered a crucial parameter for further modeling as the latter may have a significant impact on duration of a flight. By adding the processed meteorological data from the velocity dataset (Figure 1) to the data matrix, an input dataset of merged data is created, which becomes the subject of further processing. The fundamental abstraction of this input dataset is illustrated in Figure 3, highlighting the nature of the time series data, with each flight represented by multiple waypoints characterized by dynamic parameters such as latitude, longitude, and time over waypoint timestamps. Additionally, constant terms, such as take-off and scheduled in-block times, are associated with all waypoints of a given flight, which is an important factor that must be considered in further processing.
## 3 Method
### Implementation  
The proposed sensor was implemented using the following processes. First, sensor housings A and B were fabricated from stainless steel (SUS304) by high-precision metal cutting. A guide channel with a V-notch was fabricated in the center of housing A, to precisely align the carbide pin and the UMFOPS. The cutout from both sides of housing A were fabricated to increase the sensitivity. The carbide pin was custom-ordered with a diameter of 120 \(\upmu\)m (WH20, United, Kanagawa, Japan). The assembly process was performed manually, as shown in Figure 3. The most important part of this process is the heating and cooling (shown in Figure 3 (2) and (3)). The heating process rigidly fixes the carbide pin and UMFOPS together with low-temperature sealing glass. This process is required not only for the low-temperature sealing glass to adhere, but also so that the cooled sensor structure compresses the carbide pin and UMFOPS together, enabling both pushing and pulling forces to be measured. All procedures were conducted manually using a microscope and the positioning stage shown in Figure 4.  
Figure 3: Sensor assembly: (1) The carbide pin and UMFOPS are aligned in the channel in sensor housing A so that their tips are in contact. (2) The buffers are filled with low-temperature sealing glass and heated to 300 \({}^{\circ}\)C. (3) The sensor is cooled to 25 \({}^{\circ}\)C. Pre-compression is achieved by the thermal deformation of the sensor structure during cooling. (4) The sensor is covered and sealed by sensor housing B.  
Figure 2: Structure of the proposed force sensor: (**a**) overview and (**b**) images of the developed sensor.