esa-sceva/satcom-chunk-collection huggingface dataset:
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 8 Conclusions  
A highly adaptive reconfigurable receiver front-end (HARRF) has been presented for multi-band operation covering X-band (8-12 GHz) and K/Ka-band (23-28 GHz), integrating two three-stage LNAs and a Single-Pole Double-Throw (SPDT) switch in a 0.15 um InGaAs pHEMT process. The X-band LNA achieves 23-27 dB of gain, maintains a noise figure (NF) of approximately 1.7-2.0 dB, and retains input/output return losses better than \(-5\) dB with unconditional stability (\(K>1\)). The K/Ka-band LNA exhibits a gain of 23-27 dB and an NF of 2.3-2.6 dB while preserving stability over its band. The SPDT switch demonstrates low insertion loss (\(\sim\)3 dB) up to 50 GHz, isolation levels of 40-75 dB, and \(|S_{11}|,|S_{22}|\leq-10\) dB, ensuring minimal interference between X-band and K/Ka-band paths. Together, these components enable efficient frequency switching, high gain, and low noise over wide bands, reducing form factor and cost and making this solution viable for next-generation radar, satellite, and 5G mmWave transceivers.  
Conceptualization, M.U. and S.E. (Sunday Ekpo); methodology, M.U.; software, M.U. and F.E.; validation, M.U., S.E. (Sunday Ekpo), S.E. (Sunday Enahoro), R.U., Y.A.-Y. and F.E.; formal analysis, M.U.; investigation, M.U.; resources, M.U. and S.E. (Sunday Ekpo); data curation, M.U.; writing--original draft preparation, M.U.; writing--review and editing, M.U., S.E. (Sunday Ekpo), S.E. (Sunday Enahoro), R.U., Y.A.-Y. and F.E.; supervision, S.E. (Sunday Ekpo); project administration, S.E. (Sunday Ekpo). All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data are contained within the article.  
The authors declare no conflicts of interest.
## 1 Introduction
### Research Motivation  
Various research has been conducted on optimum component sizing using various optimization techniques to evaluate a cost-effective hybrid microgrid configuration such as PV/biodiesel/BESS using simulated annealing [37], Supercapacitors/BESS/WT/Fuel using Non-dominated Sorted Genetic Algorithm [6], diesel/PV/WT using multi-objective self-adaptive differential evolution algorithm [38], PV/WT/BESS using cuckoo search algorithm [39], MOPSO [40], GA-PSO and MOPSO [41], and more. However, it is observed from the research trends in the literature that in order to ascertain the maximum techno-economic benefits for any microgrid configuration and investment, the flexibility requirements of the system must be factored into its design, i.e., reliability based on adequate system flexibility provision must be prioritized alongside the planning and capacity sizing. Hence, in this study, a multi-objective optimal planning for an isolated microgrid that introduces the cost of flexibility management using ESS and DRP is investigated. The multi-objective design problem is formulated and solved using the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm in MATLAB environment.
# Cosmo ArduSiPM: An All-in-One Scintillation-Based Particle Detector for Earth and Space Application
## 2 The Cosmo ArduSiPM Architecture  
The Cosmo ArduSiPM (Figure 1) is fundamentally based on a similar architecture as its predecessor ArduSiPM [16] and uses pretty much the same Analog circuits. A significant amount of work has been carried out on package reduction aimed at significantly reducing both the size and power consumption of the electronics. The use of a powerful SAMV71 Microchip Technology SoC [17], also available in the radiation-resistant form, gives the possibility to use this device in a radiation-challenging environment such as space [18] or the CERN LHC (Large Hadron Collider) in Geneva accelerator. The Microchip SAMV71 with its ARM\({}^{\circledR}\) Cortex\({}^{\circledR}\)-M7 CPU core can run at up to 300 MHz, instead of 84 MHz of the SAM38 XE ARM\({}^{\circledR}\) Cortex\({}^{\circledR}\)-M3 CPU (Microchip Technology Inc., Chandler, AZ, USA) core of the original ArduSiPM. The new device revamps the potential of the ArduSiPM with high CPU(Central Processing Unit) processing speed, faster ADCs, faster communication interfaces, and smaller package size. The overall system comprises two main parts: hardware and firmware software.  
Cosmo ArduSiPM hardware is a custom-designed printed circuit board (PCB) that incorporates a SiPM interface, an analog front-end, fast data acquisition, a processing system, and several communication interfaces. Photon detection using SiPM, along with its parameters like Time of Arrival (ToA), amplitude, and photon count, requires several signal conditioning stages. The system is made up of a power converter, as a suitable high-voltage DC supply for SiPM; a low-noise voltage amplifier, for better signal-to-noise ratio (SNR); a programmable threshold that controls the fast timing discriminator to cut off the narrow photon pulses and the noise; and a peak hold for ADC (Analog-to-Digital Converter) to read the pulse amplitude accurately. A comprehensive Cosmo ArduSiPM architecture is presented in Figure 2, briefly elucidating the interconnection between the parts.  
Figure 1: Picture of the Cosmo ArduSiPM board linked to the SiPM placed on a holder. Each board is composed of two independent channels.
## 7 Conclusion, Limitations, and Future Research Work
### Limitations  
The developed model uses AHP to shortlist qualitative factors and hierarchical fuzzy expert system techniques to assess organizational performance. There are some inherent limitations in this model, such as:  
* The critical success factors have been shortlisted from the literature review and the impact rating from participants. There is a possibility that a factor could be left out if the frequency of appearance is low.
* Furthermore, based on geographical conditions and socio-economic conditions, the model can only predict the ranking of organizations that fall in that geographic location.
* The total number of collected questionnaires is 130. The model accuracy can be improved by increasing the number of participant responses that are used to develop knowledge-based rules of the fuzzy expert system, as well as considering companies that are widespread across the range of rankings.
* The majority of the construction firms from which responses were received were not publicly listed. This limited the number of key financial performance factors, indicating factors that could be included in the model.
* The first layer structure is not one of the types of Hierarchical Structures in MATLAB, so due to technical constraints, only the second layer of the hierarchical fuzzy expert model could be developed in Matlab R2015. The first layer was manually calculated to compute the combined impact of sub-factors in the sub-model.
* The effect of choosing different methods of fuzzification and defuzzification methods and membership functions type.
* Furthermore, the model can only predict the category of the rank, not the exact rank. To be able to make the model more accurate, data over a wide range of rank need to be collected.
# Involvement of Vasoactive Intestinal Peptide Family Members in Diabetic Keratopathy
## 3 Expression of Neuropeptides in the Cornea  
Neuropeptides are signaling molecules of 3 to 100 amino acids that exert key roles in different physiological processes, such as reproduction, body weight regulation, pain, memory, sleep/wake cycles, long-lasting modulation of synaptic transmission, inflammation, tissue repair, and glucose metabolism [44; 45]. They exert their functions through the activation of G protein-coupled receptors (GPCRs), which are integral membrane glycoproteins containing seven transmembrane domains [46]. GPCRs are coupled with intracellular heterotrimic G proteins, which consist of three subunits, the \(\alpha\), \(\beta\), and \(\gamma\) subunits. Upon receptor activation, the G protein is activated and the \(\alpha\) subunit separates from the \(\beta\gamma\) dimer, and then both \(\alpha\) and \(\beta\gamma\) can modulate the activity of target effectors.  
G proteins are classified according to the activity of the G\(\alpha\) subunit as either Gs, Gi/o, or Gq/11 [45]:  
* Gs signaling is involved in adenylyl cyclase (AC) activity, which regulates intracellular adenosine 3',5'-cyclic monophosphate (cAMP). cAMP is an intracellular signal transmitter that, in turn, acts as a second messenger and activator of cAMP-dependent protein kinase A (PKA).
* Gi/o signaling is involved in the inhibition of AC activity, resulting in decreased intracellular cAMP production.
* Gq/11 signaling activates phospholipase C\(\beta\) (PLC\(\beta\)), which hydrolyzes phosphatidylinositol 4,5-bisphosphate (PIP2), releasing diacylglycerol (DAG) and 1,4,5-inositol trisphosphate (IP3). DAG activates protein kinase C (PKC), whereas IP3 diffuses to the endoplasmic reticulum (ER) and binds to IP3 receptors on ligand-gated calcium channels on the surface of ER leading to the release of calcium ions.  
Neuropeptides are largely synthesized and secreted in the central and peripheral nervous system, as well as in other organs and tissue, including the cornea. Corneal nerves produce various neuropeptides, displayed in Table 1, that play neuromodulatory functions in the healthy and diseased cornea.
## 2 Materials and Methods
### Performance Comparison Criteria  
The performance of all models was evaluated using the mean absolute error (MAE), root-mean-square error (RMSE), Nash-Sutcliffe efficiency coefficient (NSE), coefficient of determination (\(R^{2}\)), and slope between measured and simulated \(\mathrm{ET_{a}}\) values, in accordance with the equations below. Meanwhile, the experimental model of actual evapotranspiration (\"GG\") was also used to compare with these machine learning models. The details of the GG model are described in the Supplementary Materials.  
\[\mathrm{MAE}=\left|\frac{\sum_{i=1}^{n}\left(\mathrm{P_{i}}-\mathrm{O_{i}} \right)}{n}\right|\]  
where n is the total number of observed data; \(\mathrm{P_{i}}\) is the predictive value of tea plantation evapotranspiration; and \(\mathrm{O_{i}}\) is the observed value.  
\[\mathrm{RMSE}=\sqrt{\frac{\sum_{i=1}^{n}\left(\mathrm{P_{i}}-\mathrm{O_{i}} \right)^{2}}{n}}\]  
\[\mathrm{R^{2}}=\frac{\sum_{i=1}^{n}\left(\mathrm{P_{i}}-\mathrm{O_{i}}\right)^ {2}}{\sum_{i=1}^{n}\left(\mathrm{O_{m}}-\mathrm{O_{i}}\right)^{2}}\]  
where \(\mathrm{O_{m}}\) is the mean of the observed values of tea plantation evapotranspiration.  
MAE and RMSE with smaller values and \(\mathrm{R^{2}}\) with higher values confirm better model performance.
# TrojanDetector: A Multi-Layer Hybrid Approach for Trojan Detection in Android Applications
## 4 Experimental Validation
### Experimental Results  
The outcome of the chosen binary classifiers is either True Positive Rate (TPR), False Reject Rate (FRR), False Positive Rate (FPR), or True Reject Rate (TRR). TPR is the total number of Trojan apps correctly classified as Trojans, FRR is the total number of Trojan apps incorrectly classified as benign, FPR is the total number of benign apps incorrectly classified as Trojans, and TRR is the total number of benign apps correctly classified as benign. Equal Error Rate (EER) describes situations where FPR and FRR are equal. We show the evaluation results regarding TPR, EER, and accuracy. We do not report FPR, FRR, and TRR because they can be easily derived as FRR = 1-TPR, and TRR = 1-FPR.  
Figure 2 summarises the results of the considered classifiers over the selected features. The DT classifier on chosen features (shown in Figure 3a) is the lowest accurate classifier with 82.25% accuracy, followed by RF with an accuracy of 86.68%. SVM is the most effective algorithm in this scenario. It attains an accuracy of 96.64%, followed by LR with 91.50% accuracy.  
Figure 3: Features importance graph for (**a**) DT, (**b**) RF, (**c**) LR, and (**d**) SVM classifier.  
Figure 2: Results of the chosen classifier on selected features.  
The results of the chosen classifier on selected features indicate that TrojanDetector could be effectively used to differentiate between Trojans and benign applications.
## 2 Methods
### Independence of the Mesh  
To analyze the effect of different grid resolutions upon computational results, three mesh resolution levels (Coarse, Medium and Fine) were established by varying the parameters of the mesh size in the encrypted zone (C). The RNG \(k\)-\(\varepsilon\) and IDDES models were both calculated using an identical group of mesh models (Figure 4), and the amounts of mesh units applicable to the RNG \(k\)-\(\varepsilon\) and IDDES models were 36 million (Coarse), 46 million (Medium), and 54 million (Fine), respectively. The amounts of mesh units for the LES models with different mesh resolutions were 59 million (Coarse), 71 million (Medium), and 81 million (Fine), respectively. All the models were solved by referring to the computational strategies presented in Sections 2.2 and 2.4.  
The lateral force of the sedan is used as a monitoring index, and Figure 5 presents the temporal variation in the lateral force coefficients for the sedan as obtained by the RNG \(k\)-\(\epsilon\), LES, and IDDES models. \"Coarse\", \"Medium\", and \"Fine\" are denoted by the abbreviations \"C\", \"M\", and \"F\", respectively. The results show that the predictions from the RNG \(k\)-\(\epsilon\) (M) model are largely in alignment with the RNG \(k\)-\(\epsilon\) (F) model, with a discrepancy in the variation amplitude of lateral force of less than 2.3%. The difference between the maximum fluctuation in lateral force obtained from the LES (M) and the corresponding value from the LES (F) model is controlled within 3.3%. The maximum value of the difference between the results obtained from the IDDES (M) model and the corresponding value from the IDDES (F) model is only 0.8%. Finally, the reasonable numbers of mesh units for RNG \(k\)-\(\epsilon\), LES, and IDDES models are identified as 46, 71, and 46 million, respectively.  
Figure 4: Mesh distribution around the sedan: (**a**) coarse; (**b**) medium; (**c**) fine.  
Figure 5: Comparison of the lateral force coefficients of the sedan in three turbulence modeling approaches: (**a**) RNG \(k\)-\(\epsilon\); (**b**) LES; (**c**) IDDES.
# Vision-Based Distance Measurement in Advanced Driving Assistance Systems
## 6 Experiments and Results
### Performance Comparison of Depth Estimation  
#### 6.2.1 Quantitative Comparison with the Other Four Methods  
In this subsection, we provide a comprehensive comparison of the proposed depth map estimation method with four other methods: [PERSON]'s method [20], which is a supervised depth estimation method; [PERSON]'s [34] and [PERSON]'s [23] methods, which are unsupervised estimation methods; [PERSON]'s [24] method, which is a semi-supervised method. We conducted the quantitative comparison experiments on the KITTI dataset. We used 200 images from the KITTI 2015 dataset as the test samples. In Table 1, we illustrate the comparative results of the proposed method with the other four methods on the KITTI dataset. We found that in terms of absolute relative error, the semi-supervised method presented in this paper is a huge improvement as compared with the supervised method of [PERSON] et al. and the unsupervised method of [PERSON] et al. The results obtained by our method are also significantly better than the results of [PERSON]'s method which uses left-right consistency. In addition, our method outperforms the [PERSON]'s method which is also a semi-supervised method. From the perspective of the root-mean-square error, the method in this paper is significantly improved compared to the other four methods. In terms of threshold accuracy, accuracies of more than 90% were achieved for the three thresholds of our method, all being higher than the other methods.
## 3 Experiments
### Baseline Systems and Experimental Details
#### 3.3.3 Evaluation Protocol  
After training, we extract 192-dimension speaker embeddings in test pairs and calculate the cosine similarity between embeddings of each pair, then adaptive s-norm [9] is performed with an imposter cohort size of 600. We measure the systems according to equal error rate (EER) and minimum of the normalized detection cost function (MinDCF). The EER represents the point at which the false acceptance rate and false rejection rate are equal. The MinDCF, which incorporates weighting for acceptance and rejection errors, is expressed as: \(C_{det}=C_{miss}\times P_{miss}\times P_{\text{tar}}+C_{fa}\times P_{fa} \times(1-P_{\text{tar}})\). In our research, we consider a prior target probability \(P_{target}\) of 0.01 and misses \(C_{miss}=\) false alarms \(C_{fa}=1\).
## 3 Improved Network for Component Detection  
This study proposes a research framework for the automatic detection of satellite parabolic antenna components based on Inverse Synthetic Aperture Radar (ISAR) and deep learning. This section mainly develops a parabolic antenna component detection model based on the modified YOLOv8 algorithm, according to the features of the parabolic antenna analyzed in the previous section under terahertz radar imaging. Therefore, this section mainly introduces the basic structural framework of the proposed network, the relevant evaluation indicators, and the other algorithms involved in the comparison.
## 1 Introduction
The similarity of two lights is evaluated with respect to certain characteristics such as symmetry condition, size, shape, and cross-correlation [6; 7; 12; 13]. A pairing method using optimization is proposed in [10; 11], which applies motion information from the tracking and spatial context to find the correct pairs. However, this method carries high computation cost for optimizing over the entire set of candidate light pairs. Light pairing faces certain difficulties: one vehicle light may be shared by more than one light pair if two vehicles are at the same distance from the host vehicle. Moreover, the pairing process may fail when various complex situations occur on the road, such as partial vehicle occlusion, blooming effects, or turning vehicles. To resolve the abovementioned problems, in this paper, we propose a vehicle detection and tracking system that perceives both preceding and oncoming vehicles on the road. The contributions of this work are three-fold: first, a machine-learning-based approach for detecting both headlights and taillights at night is proposed; second, a combination of motion information, correlation coefficient, and size of vehicle light is defined to pair the vehicle lights; finally, partial vehicle occlusion is solved by retrieving and analyzing tracking information from subsequent frames. The remainder of this paper is organized as follows. Section 2 describes the entire process of the system. It contains two main blocks: vehicle detection, which detects preceding and oncoming vehicles, and vehicle tracking with occlusion handling. Then, the experimental setup and evaluation results based on a real dataset are presented in Section 3. Finally, the conclusions are drawn in Section 4.
## 3 Test Damage Form and Characteristics
### Damage Mechanism of Specim SF-2  
The damage form of specimen SF-2 is shown in Figure 13. A small number of broken pieces of the AAC panel came off when the interlayer displacement angle was 1/75. Micro cracks were produced at the corner of panel No. 5. When the interlayer displacement  
Figure 11: Equipment used for the test: (**a**) Displacement meter; (**b**) data collector; (**c**) crack observer.  
Figure 12: Images depicting the results of the loading test used for specimen SF-1: (**a**) splicing of the AAC wall panel due to mortar cracking; (**b**) mortar falling off from the spliced AAC wall panel; (**c**) cracks in bolt holes; (**d**) AAC wall panels damaged at the corners; (**e**) oblique cracks in the middle of the AAC wall panel; (**f**) overall view of the wall panel frame after the test.  
angle was 1/50, the mortar of the joints between panels No. 1 and No. 2 was broken, and cracks appeared at the corners of these panels. When the interlayer displacement angle was 1/40, cracks appeared at the upper bolt holes of panels No. 2 and No. 3, and the lower node of the second panel was loose due to apparent transverse relative displacement. At a displacement loading of 90 mm, the mortar between panels No. 2 and No. 3, as well as between No. 3 and No. 4, fractured one after another; consequently, cracks continued to extend. At a displacement angle of 1/25, all the mortars between the joints fell off. When the interlayer displacement angle was 1/20, the bearing capacity dropped below 85% of the ultimate load, and the test ended with the connector undeformed.  
For specimens SF-1 and SF-2, damage occurred first at the corners of the wall panels and mortars of the joints. Since specimen SF-1 used rigid connection nodes, the wall panel could not release the effect of harmful displacement. Therefore, specimen SF-1 was damaged more than specimen SF-2. With increasing displacement loading, cracks appeared between the panels and around the bolt holes in both specimens SF-1 and SF-2, and the cracks extended continuously with the increase in loading. Since the bolt holes of the NEDC used in specimen SF-2 reduce the crush damage caused to the wall panel by sliding when subjected to horizontal in-plane loading, the damage caused to the AAC wall panel of specimen SF-2 was less than that of specimen SF-1.
## 5 Conclusions and Perspectives  
The buckling behavior of planar and uniformly compressed grid beams has been analyzed in this paper. A Timoshenko beam model, embedded in a 2D space, and accounting for the geometric effect of compression of longitudinal fibers, as well as for the micro-warping of the transverse fibers, has been formulated in the framework of a homogenization procedure. Accordingly, elastic and geometric corrective factors have been introduced in the constitutive coefficients, analytically derived through a suitable identification procedure. The buckling analysis of the coarse model, governed by a boundary value problem admitting an exact solution, has been illustrated. The effectiveness of the presented Timoshenko beam model has been discussed on sample grid beams, by comparing the results of exact buckling analyses with benchmark solutions obtained through finite-element discretization of the correspondent bi-dimensional frames.  
The following conclusions have been drawn.  
1. An excellent agreement between the critical loads and modes of the equivalent beam model, with respect to finite-element analyses has been found, providing a sufficient number of cell is considered. This finding is independent from the boundary conditions.
2. The geometric effect of the prestress has been introduced both in the equilibrium and constitutive equations of the coarse model. The second aspect, accounting for micro-effects, is shown to be crucial to correctly describe the buckling behavior of the here analyzed micro-structured beams.
3. The corrective factors provide a significant correction of the elastic operator, revealing their essential contribution in the proper modeling of planar grid beams.  
Figure 6: Critical mode in the case study II, when \(n=20\), and for different boundary conditions: (a) H-R; (b) C-F; (c) C-R; (d) C-S. Continuous green curve: Timoshenko model; blue dots: FE solution.  
Finally, the main perspectives of this work are: (i) the extension of the presented model to a 3D space, also including for the presence of micro-structure's imperfections, which may lead to a flexural-torsional buckling behavior; (ii) the development of buckling experimental tests on 3D-printed grid beams.  
Conceptualization, M.F. and F.D.; methodology, M.F. and F.D.; software, M.F. and F.D.; validation, M.F. and F.D.; data curation, M.F. and F.D.; writing--original draft preparation, M.F. and F.D.; writing--review and editing, M.F. and F.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.