esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient AdaBoost Algorithm for Enhancing Skin Cancer Detection and Classification
## 4 Experimental Results
### Evaluation Metrics of the Algorithms and Classifiers That Were Applied in the Proposed Model
#### 4.2.2 Statistical Analysis: \(T\)-Test Results  
To assess the statistical significance of the differences between the performance of various classification algorithms, we conducted pairwise T-tests on the accuracy results obtained from 10-fold cross-validation.  
The results presented in Table 3, showing no statistically significant differences between the classification algorithms (DT, NN, RF, SVM) using EfficientNetB0-PCA on the ISIC dataset, can be interpreted as positive. This indicates that multiple algorithms are effective for this task, providing flexibility and robustness in model selection. Researchers and practitioners can choose any of these models based on other factors such as computational efficiency, ease of implementation, or specific application requirements, without worrying about significant performance discrepancies.  
The statistical analysis of classification algorithms for the DermIS dataset using EfficientNetB0-PCA, as shown in Table 4, reveals no statistically significant differences between the compared pairs of algorithms. The T-statistic and P-value for each comparison  
Figure 4: Visualization of the performance metrics for algorithms classifiers.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classification Algorithms** & **T-Statistic** & \(p\)**-Value** \\ \hline EfficientNetB0-PCA (DT vs. NN) & \(-0.466\) & \(0.647\) \\ EfficientNetB0-PCA (DT vs. RF) & \(-0.451\) & \(0.657\) \\ EfficientNetB0-PCA (DT vs. SVM) & \(-0.530\) & \(0.602\) \\ EfficientNetB0-PCA (NN vs. RF) & \(0.011\) & \(0.992\) \\ EfficientNetB0-PCA (NN vs. SVM) & \(-0.067\) & \(0.948\) \\ EfficientNetB0-PCA (RF vs. SVM) & \(-0.076\) & \(0.940\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical analysis of classification algorithms for ISIC dataset.  
(DT vs. NN, DT vs. RF, DT vs. SVM, NN vs. RF, NN vs. SVM, and RF vs. SVM) indicate that Decision Tree (DT), Neural Network (NN), Random Forest (RF), and SVM perform similarly on the DermIS dataset. This lack of significant differences suggests that these algorithms are equally effective for this task, allowing flexibility in choosing any of them based on other considerations such as computational efficiency or ease of implementation, without concern for substantial performance variations.
## 3 Results and Discussion
### Physicochemical Parameters
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Control** & **Da0.5\%** & **Da1\%** & **Da1.5\%** & **Da3.0\%** \\ \hline
**Day 0** & 5.56 \(\pm\) 0.00 a1 & 5.52 \(\pm\) 0.01 b1 & 5.52 \(\pm\) 0.01 b1 & 5.59 \(\pm\) 0.01 c1 & 5.60 \(\pm\) 0.01 c1 \\
**Day 7** & 5.29 \(\pm\) 0.01 a2 & 5.35 \(\pm\) 0.01 b2 & 5.34 \(\pm\) 0.00 b2 & 5.32 \(\pm\) 0.01 b2 & 5.36 \(\pm\) 0.00 c2 \\ \hline \hline \end{tabular} \({}^{\text{ac.}}\) Mean values in the same row (different treatment on same day) with different letters indicate significant difference. \({}^{1-2}\). Mean values in the same column (same treatment on different days) with different numbers indicate significant difference. (_p_ < 0.05; Tukey’s test). All parameter values were presented as mean \(\pm\) standard error.  
\end{table} 
Table 2: Changes in pH values of beef burgers with different levels of _Durvillaea antarctica_ meal during storage. authors have observed a similar phenomenon when cooking low-fat pork patties, where the inclusion of between 1.0% and 3.0% of Laminaria japonica meal reduced cooking loss. This most likely occurred because this brown seaweed contains dietary fibers such as alginate and laminarin, which have a high water-holding capacity and binding capacity [28]. On the other hand, the treatments with the lowest concentrations of \(D\). _antarctica_ (Da0.5% and Da1.0%) exhibited significantly lower WHC (\(p<0.05\)) compared to the control, which suggests a tendency to lose WHC tolerance in the beef burgers at these specific concentrations of seaweed. The concentration of seaweed may have altered the levels of minerals such as sodium and potassium (_Durvillaea antarctica_ mineral composition: Na: 4.11%; K: 1.59%; Fe: 305.5 \(\upmu\)g/g), which could alter the electrical balance of the medium and the isoelectric point of the proteins, significantly reducing water-holding capacity [15; 37; 40]. A similar phenomenon occurred in the cooking loss of beef burgers and sausages made with 3.0% sea tangle [41]. Cooking loss is an analysis that should be addressed shortly. However, the water-holding capacity of seaweed also depends on ambient temperature and species, and this aspect has only been studied recently [42]. Therefore, the WHC of beef burgers containing _Durvillaea antarctica_ could be compared with beef burgers made from other species available in our market under the same processing conditions.
###### Abstract  
A comfortable outdoor environment, like its indoor counterpart, can significantly enhance the quality of life and improve the physical and mental health of elderly populations. Urban spatial morphology is one of the key factors influencing outdoor environmental performance. To explore the interactions between urban spatial morphology and the outdoor environment for the elderly, this study utilized parametric tools to establish a performance-driven workflow based on a \"morphology generation-performance evaluation-morphology optimization\" framework. Using survey data from 340 elderly neighborhoods in Beijing, a parametric urban morphology generation model was constructed. The following three optimization objectives were set: maximizing the winter pedestrian Universal Thermal Climate Index (UTCI), minimizing the summer pedestrian UTCI, and maximizing sunlight hours. Multi-objective optimization was conducted using a genetic algorithm, generating a \"morphology-performance\" dataset. Subsequently, the XGBoost (eXtreme Gradient Boosting) and SHAP (Shapley Additive Explanations) explainable machine learning algorithms were applied to uncover the nonlinear relationships among variables. The results indicate that optimizing spatial morphology significantly enhances environmental performance. For the summer elderly UTCI, the contributing morphological indicators include the Shape Coefficient (SC), Standard Deviation of Building Area (SA), and Deviation of Building Volume (SV), while the inhibitory indicators include the average building height (AH), Average Building Volume (AV), Mean Building Area (MA), and floor-area ratio (FAR). For the winter elderly UTCI, the contributing indicators include the AH, Volume-Area Ratio (VAR), and FAR, while the inhibitory indicators include the SC and porosity (PO). The morphological indicators contributing to sunlight hours are not clearly identified in the model, but the inhibitory indicators for sunlight hours include the AH, MA, and FAR. This study identifies the morphological indicators influencing environmental performance and provides early-stage design strategies for age-friendly neighborhood layouts, reducing the cost of later-stage environmental performance optimization.  
ag-friendly neighborhood morphology; outdoor environmental performance; multi-objective optimization; XGBoost +
Footnote †: journal: Applied sciences  
Article
# Technical Interoperability for Machine Connectivity on the Shop Floor
## 6 Conclusions  
A generic solution to collect and send data from a grinding machine, implemented in a real-life industrial setting, has been described and discussed regarding technical interoperability, focusing on the system layout, communication standards, and open systems. The findings are tied to the key question of improving manufacturing performance by connecting the technical implementation to the Industry 4.0 maturity Index model. From the discussion, it can be derived that manufacturing enterprises need to internalize the knowledge of how IT systems can be utilized to connect their existing and new equipment, while not becoming overly reliant on technology providers. The example of IoT enabled IO-Link sensors also shows the fast development of available equipment, but also that this development will most likely align with existing efforts if current standards and state of the art solutions are followed.  
Figure 6: On the left: IO-Link gateway used for the described application (ifm AY1020). On the right: Internet of Things (IoT)-enabled IO-Link gateway (ifm AL1930).  
**Author Contributions:** Conceptualization, M.A.; Writing-Original Draft, M.A.; Writing-Review & Editing, M.A., J.S., U.E., D.M., and T.H.; Software, M.A., O.A., and T.H.; Supervision, A.F.-B.; Project Administration, J.S., U.E., M.F., M.A., M.B., and C.L.; Funding Acquisition, J.S., U.E., and M.F.  
**Funding:** This research was funded by the national Digital Industrial Pilot Programme operated by the Swedish Agency for Innovation Systems (VINNOVA) grant number [2015-06755].  
**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
## 5 Simulation-Based Performance Evaluation
### Comfort and Energy Analysis Under WLTC Conditions
Correspondingly, Figure 6d illustrates the dynamic adjustment of internal weights. The velocity tracking weight \(C_{1}\) increases sharply during critical phases to enhance responsiveness, while acceleration (\(C_{2}\)) and jerk (\(C_{3}\)) penalties are relaxed to permit decisive control actions. As the situation stabilizes, the weights gradually return to promote comfort and efficiency. In summary, DWMPC excels not only in static performance but also through a self-regulating, risk-aware control architecture. By continuously sensing traffic context and adjust \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Controller** & **Mean (m/s\({}^{2}\))** & **Std. Dev (m/s\({}^{2}\))** & **Min (m/s\({}^{2}\))** & **Max (m/s\({}^{2}\))** & **Jerk (m/s\({}^{3}\))** \\ \hline DWMPC & 0.40 & 1.12 & \(-\)3.84 & 2.68 & 2.81 \\ Fixed-MPC & 0.42 & 1.16 & \(-\)3.64 & 2.68 & 3.12 \\ PID & 0.77 & 2.10 & \(-\)9.68 & 2.78 & 4.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Acceleration and jerk statistics of the three controllers. ing control priorities in real time, it maintains a robust balance among energy economy, ride comfort, and safety responsiveness, surpassing the limitations of fixed-weight strategies. Compared with the recent fuzzy-MPC-based strategy [20], which demonstrated energy-saving capabilities, the proposed DWMPC achieves similar efficiency while further enhancing control coordination through dynamic mode switching and jerk-adaptive weight adjustment under complex transient scenarios.
## 2 Why the Electroencephalogram (EEG)?
### qEEG, Stress, and Immunity  
There is a strong link between qEEG oscillatory patterns and stress regulatory systems: the hypothalamic-pituitary-adrenocortical (HPA) axis and the sympathetic-adrenomedullary axis [168]. For example, qEEGs recorded from stressed students before an exam revealed a correlation between greater right hemisphere (RH) activation and higher cortisol levels [169]. This is supported by the following facts: (a) the administration of cortisol to healthy participants has been shown to increase RH frontal activation [170], and (b) greater right-sided activation (measured by resting qEEG) is associated with higher levels of basal cortisol compared to their left-activated counterparts [171]. Cortisol also seems to reduce neural interactions between different areas of the brain. Indeed, an inverse relationship between basal cortisol levels and neural interaction between the frontal and parietal cortex has been demonstrated using qEEG connectivity analysis [172].  
Considering the link between qEEG oscillatory patterns and stress regulatory systems, it is not surprising that the association between several factors of the immune system and qEEG activity has also been reported [173; 174; 175; 176; 177] (for a recent meta-analysis, see [178]). For example, higher levels of right-prefrontal qEEG activation (a) reliably predicted poorer immune response [176] and (b) are characterized by lower levels of natural killer cell activity [179]. These data support the hypothesis that individuals characterized by a more negative affective style have a weaker immune response and, therefore, may be at greater risk for illness than those with a more positive affective style. Additionally, RH activation is associated with hypercortisolemia, which contributes to the deterioration of immune system functioning and puts depressed patients at a greater risk of developing other illnesses, accounting for depression's high comorbidity with other diseases [180].
# 185-215 GHz CMOS Frequency Doubler with a Single Row Staggered Distribution Layout Design
## 3 Measurement  
Figure 10 shows a microphotograph of the presented amplifier-doubler chain. According to Figure 10, the total area of the chip, including the pads, was 795 \(\times\) 1000 \(\upmu\)m\({}^{2}\). Measurements of the chip were performed on the wafer. The input port was probed using a Cascade Infinity WR 8 waveguide probe. The input signal generated by the commercial frequency multiplier module was injected into the chip, and the output power was measured by a VDI Erickson PM5 power meter with a waveguide taper. Two different measurement setups were used in the measurement since 220 GHz was the boundary of the WR3 and WR5 waveguides which needed to be probed with Cascade Infinity WR3 and WR5 waveguide probes, respectively.  
The results of the measurement are shown in Figure 11. According to Figure 11, the presented amplifier-doubler chain reached its peak output power of 7.9 dBm at 200 GHz, and the 3 dB bandwidth was 30 GHz (from 185 GHz to 215 GHz).  
Figure 10: The micrograph of the presented amplifier–doubler chain.  
Figure 9: The load pull diagram with a drive power of 11.5 dBm.  
Table 1 shows the performance of this frequency doubler compared to state-of-the-art frequency doublers. According to Table 1, the presented amplifier-doubler chain provided the highest peak output power of the reported frequency doublers when fabricated in CMOS technology. A frequency doubler fabricated in SiGe technology obtained a larger peak output power than the presented frequency doubler; however, the chip area of a frequency doubler fabricated in SiGe technology was 2.5 times that of the presented frequency doubler, and the DC power consumption of the frequency doubler fabricated in SiGe technology was more than three times as high as that the presented in the frequency doubler.
## 3 Results  
### Bioinformatics and CD Analysis of LEAPs  
The amino acid sequences of LEAP-1 and LEAP-2 from _Dissostichus mawsoni_ and _Lycodichthys dearborni_ were aligned with several other known and predicted LEAPs. As shown in Figure 1, the multiple sequence alignment of mature LEAP sequences from teleost fishes and mammals revealed a highly conserved pattern of eight and four cysteine residues. The alignment also showed the presence of Arginine and lysine amino acids, which are typically associated with high antibacterial activity [28]. The presence of ATCUN motif in the peptides increases the chances of interaction with DNA, thereby promoting antibacterial activity [7]. The ATCUN motif (QSH) was found to be present in nLEAP-1, which could lead to increased interaction with DNA, and consequently, a more potent antibacterial activity. The CD analysis of LEAPs in 30% TFE showed a negative band at about 200 nm, characteristic of \(\beta\)-turns loops and distorted \(\beta\)-sheets from 5 to 35 \({}^{\circ}\)C (Figure 2). The analysis of CD spectra data with BeStSel software (Beta structure selection) revealed the typical characteristics of \(\beta\)-sheet conformation, including antiparallel strands (antiparallel 1, 2, and 3) and beta turns, among others at different temperatures. Incubation of the nLEAP-2 and eLEAP-2 at different temperatures from 5 to 35 \({}^{\circ}\)C showed a change in the CD spectrum at low temperatures, indicating a modification of the peptide structure.  
Figure 1: Multiple sequence alignment of Liver-Expressed Antimicrobial Peptides (LEAPs) from human (AAH20612.1, AAH70919.1); _Dissostichus mawsoni_ (ABY84829.1, ABY84838.1); _Lycodichthys dearborni_ (ABY84842.1, ABY84845.1); _Notolthenia augustlata_ (ABY84832.1, ABY84835.1); _Notolthenia coriecps_ (XP\(\_\)010791080.1, XP\(\_\)010772611.1); _Pogonoltryne scotti_ (ABY84821.1, ABY84840.1); and _Epinephrineus coioides_ (AEK87109.1, ADN06867.1). (**A**) The multiple sequence alignment of LEAP-1 shows highly conserved cysteine region and presence of basic amino acids arginine and lysine residues. The nLEAP-1 (Antarctic notothenioid liver-expressed antimicrobial peptide-1) and eLEAP-1 (Antarctic eelout liver-expressed antimicrobial peptide-1) contain highly conserved arginine and lysine residues. eLEAP-1 contain ATCUN motif (Amino terminal Cu(II)- and Ni(II)-binding motif) which is also present in human LEAP-1 (Human liver-expressed antimicrobial peptide-1). (**B**) nLEAP-2 (Antarctic notothenioid liver-expressed antimicrobial peptide-2) shows highly conserved cysteine residues with conserved arginine and lysine amino acids and eLEAP-2 (Antarctic eelout liver-expressed antimicrobial peptide-2) also shows conserved cysteine residues but contain low arginine and lysine amino acids as compared to other peptides. (Cysteine residues (C): highlighted with yellow colour; Arginine residues (R): highlighted with pink colour and lysine residues (K): highlighted with green colour).
## 4 Simulation and Measurements Results  
The bare version, i.e., facing the ambient environment, dual-band nested patch array was designed using electromagnetic full-wave simulations, using CST Studio Suite 2023 [20]. Upon fabrication, measurements were carried out in a shielded anechoic chamber with \(\pm 1\) dB measurement uncertainty across the frequency ranges of interest; a snapshot of the measurement setup is depicted in Figure 6a. The array was mounted to a plastic holder and bolted to the rotating metallic positioner, covered with absorbing material to avoid unwanted artefacts in the measured radiation pattern. The array was then embedded into the rear spoiler and re-measured as shown in Figure 6b. The nested array was designed and integrated such that it remains hidden in the rear spoiler.
# Investigation of Data Quality Assurance across IoT Protocol Stack for V2I Interactions
## 1 Introduction
### Traceability  
Traceability refers to accurately documenting and tracking the provenance of data from source to destination [40]. It involves capturing and maintaining a comprehensive audit that identifies data sources, including the entities that have accessed or modified the data and the timestamps of these activities. Integrating traceability in a system will offer better security management, safety management, better response to crises, and overall strengthen the performance and coordination of the system [41]. The traceability dimension will benefit V2I use case examples like Pseudonym resolution [42,43].
# The Impacts of Human Activities on Ecosystems within China's Nature Reserves
## 2 Methods and Materials
### The Land Cover Change Datasets  
LCC datasets were collected for the years of 2000, 2005, 2010, and 2015 with resolution of 100 m from Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences ([[http://www.resdc.cn](http://www.resdc.cn)]([http://www.resdc.cn](http://www.resdc.cn))). These datasets were interpreted by human-machine interactions based on rectified satellite images using an ArcGIS platform, according to methods from [PERSON] et al. [23]. Land cover was classified into 6 primary types, consisting of grassland, forest, cropland, wetland, desert, and built area. The 6 types were further subdivided into 25 subtypes.
# A Compact Sub-GHz Wide Tunable Antenna Design for IoT Applications
## 3 Results of Simulation and Measurements
### Reflection Coefficient Curve  
The reflection curves characterize the proposed antenna design. The simulated and measured results of the reflection curves are shown in Figure 5. Figure 5a shows the proposed antenna design simulated reflection coefficient curves (\(S_{11}\)). The resulted curves are for the design with varactor diode capacitance values from 0.84-5.08 pF and the corresponding reverse bias voltage values ranges from 15 to 0 V. A smooth variation in the resonating bands was observed from 753-1040 MHz with a \(-10\) dB bandwidth of 17 MHz. The wide frequency sweeping with narrowband operations is the proposed antenna design's key characteristic. The proposed slot radiating structure can easily cover several IoT bands in the sub-GHz range.  
The proposed antenna's measured S-parameters are shown in Figure 5b. It is clear from the figure that the measured results are in good agreement with the simulation results. In the event of any small variation between the two results, it can be compensated for as continuous frequency sweeps have been obtained for both. This demonstrates the main advantage of using varactor diode capacitive loading for these types of operations.  
The slight variations in both simulated and measured \(S_{11}\) are mainly due to the substrate properties, fabrication tolerances, and varactor diode modeling flexibility in HFSS [44; 45]. The HFSS does not consider diode losses and packaging effects. Also, manual soldering of the SMA connector might affect the values of input impedance \(Z_{in}\). The additional harmonics are the higher-order resonances of the slot antenna. In an ideal simulating software environment, higher-order modes are poorly matched with the input  
Figure 5: Reflection coefficient curves of the antenna (**a**) Simulated \(S_{11}\) (**b**) Measured \(S_{11}\).  
impedance \(Z_{in}\), and hence are visible. However, the measured \(Z_{in}\) is only matched with its fundamental mode of operation within the given frequency bands, and the poorly matched bands did not appear for the measured values. Hence, no harmonics are observed for the measured \(S_{11}\) results.