esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Topology Optimization for the Synthesis of Compliant Mechanisms
### Design Sensitivity Analysis via Adjoint Methodology
#### 2.3.5 Sensitivity Analysis for Multiresolution Topology Optimization  
The sensitivity equations derived in the prior sections for the objective functions can be used analogously for multiresolution topology optimization. The only difference is that more design variables than finite-elements are used. This has an impact on the calculation of the terms \(\frac{\partial\underline{R}_{\underline{x}}}{\partial\underline{x}_{ \underline{x}}^{n}}\) (see Equation (37)), because \(\underline{\tilde{\xi}}\) is of higher dimension. The adjoint Equation (36) for the objective function sensitivities remains unchanged.  
In addition, the sensitivities of the volume constraint function in Equation (46) remain unchanged.  
The sensitivity functions of the stress constraint Equation (27) are changed if multiresolution topology optimization is used. Due to the use of the stress values \(\underline{\sigma}_{C,i,j}\) at each integration point (or subelement) \(j\), the p-norm function changes to Equation (30). This affects the partial derivative of the normalized p-norm which for the MTOP approach is written as  
\[\frac{\partial\varphi_{p}}{\partial\underline{x}_{l}}=c\left(\sum_{i}\sum_{j}V _{ij}\widetilde{\sigma}_{i,j}{}^{p}\right)^{\frac{1-p}{p}}V_{l}\widetilde{ \sigma}_{l}^{p-1}q\bar{x}_{l}^{q-1}\sigma_{M,l}. \tag{62}\]  
The partial derivative of the p-norm with respect to the element displacement vector \(\underline{u}\) previously introduced in Equation (51) must be adapted to the MTOP approach. Whereas the displacement vector \(\underline{u}\) is defined for the displacement elements and hence remains the same, the stress of each individual density subelement \(\underline{\sigma}_{C,i,j}\) must be considered. The sensitivity function for stress in MTOP is  
\[\frac{\partial\varphi_{p}}{\partial\underline{u}_{l}}=\sum_{j}c\left(\sum_{i} \sum_{j}V_{i,j}{}^{p}\right)^{\frac{1-p}{p}}V_{l,j}\widetilde{\sigma}_{l,j}^{ p-1}\bar{x}_{l,j}^{q}\frac{1}{\sigma_{M,l,j}}\underline{\sigma}_{C,l,j}^{ \intercal}\underline{\underline{A}}\frac{\partial\underline{\sigma}_{C,l,j }}{\partial\underline{u}_{l}}. \tag{63}\]
## 3 Method and Materials
### Methodology
#### 3.3.2 Tabular Data Processing with MLP and PCA  
The tabular data in our model are processed using a multilayer perceptron (MLP), which is particularly effective for handling structured data. An MLP is a class of feedforward artificial neural network that consists of at least three layers of nodes: an input layer, hidden layers, and an output layer. Each node, except for the input nodes, is a neuron that uses a nonlinear activation function. MLP utilizes a supervised learning technique called backpropagation for training, capturing complex relationships in data by adjusting weights and biases through gradient descent. In our model, the MLP is tasked with extracting and synthesizing features from the tabular metadata, providing a rich set of predictive signals that complement the visual cues from the images.
###### Abstract  
Poor braking performance on runways during winter is one of the most significant factors causing runway excursions. The International Civil Aviation Organization (ICAO) proposed a standardized method, named the Global Reporting Format (GRF), to establish runway surface conditions based on human observers. The GRF includes the description of most winter conditions. This work aimed at reproducing the GRF winter conditions in the laboratory, and to monitor the effect of the surface conditions, both without surface treatment and with surface treatment using liquid runway de-icing products (RDP) in both de-icing and anti-icing modes, using the British Pendulum Tester (BPT). The reproduction of the GRF winter conditions was possible in the laboratory since the BPT results correlated well with the GRF ratings. The worst conditions retained snow on ice. Wet conditions obtained with water and RDPs (potassium formate KFO, potassium acetate KAC and hybrid glycol-KAC HYB) behave similarly, indicating that RDPs alone on a runway for prevention are not any more dangerous than just water. The range from wet to dry, equivalent to a British Pendulum Number (BPN) from ~40 to ~50, was considered as good conditions on the runway. All the RDPs tested in the study improved the BPN from untreated conditions. In most of the conditions, the RDPs improved the BPN to values in the range of favorable conditions. KFO and KAC reacted almost in the same way for each condition. However, the HYB was better in conditions with ice. The obtained results demonstrated that simulated laboratory winter conditions could be used to determine the efficiency of RDPs in both de-icing and anti-icing modes.  
Keywords:winter maintenance; runway de-icing products; landing and takeoff; snow; ice; British Pendulum Tester; Global Reporting Format +
Footnote †: journal: Applied Sciences
# Ultrafast Optical Heating Induced Polarization-Dependent Optical Switching in Gold Nanowires
## 2 Fabrication and Characterization of the Plasmonic Gold Nanowires  
A photoresist (PR) grating was first fabricated using interference lithography, where a He-Cd laser at 325 nm was used as the ultraviolet (UV) light source and S1805 photoresist (Rohm & Haas, Esslingen, Germany) was used as the recording medium. The PR grating with a period of about 410 nm was fabricated on glass substrate with a 200-nm layer of indium tin oxide (ITO), where the ITO layer also acts as a waveguide. Chemically synthesized gold nanoparticles [15; 16] with a diameter ranging from 5 to 10 nm, which were suspended in xylene with a concentration of 100 mg/mL, were then spin-coated onto the top surface of the grating. In the spin-coating process, a 30-\(\upmu\)L colloidal solution of gold was spin-coated at a speed of 2000 rpm. An annealing process at 280 \({}^{\circ}\)C enabled sublimation of the ligands on the surface of the colloidal gold nanoparticles and melting of the gold nanoparticles to be confined into the grating grooves. After being cooled down to room temperature, gold nanowires form in the grating grooves.  
Figure 1a shows the scanning electron microscopic (SEM) image of the gold nanowires. Clearly, each of the gold nanowires is not continuous and smooth along the grating lines; instead, it is composed of short segments or large gold particles with small gaps in between, which results from the annealing process. However, this provides us an opportunity to adjust the gaps between these gold nanostructures using optical heating process. According to Figure 1a, there is a large number of such gaps that are smaller than 20 nm, as indicated by the yellow arrows. Such small gaps are easily influenced by interaction between the gold nanowires and intense femtosecond laser pulses. Consequently, the localized surface plasmons resonant at these gaps may be modified by optical pulses. This is the basic mechanism for the polarization-dependent optical switching effects described in this work.  
Figure 1b,c shows the optical extinction spectra measured on the gold-nanowire grating with the incident angle changed from 0 to 16 degrees for transverse electric (TE) and transverse magnetic (TM) polarizations. The light was polarized parallel and perpendicular to the gold nanowires for TE and TM polarizations, respectively. As has been investigated extensively, waveguide resonance mode will be observed for TE polarization, which exhibits narrow-band peak features in optical extinction spectra [1; 15]. However, Fano coupling between plasmonic and waveguide resonance modes can be observed for TM polarization, as shown in Figure 1c, where enhanced transmission or narrow-band dips in the optical extinction spectrum can be observed. This Fano coupling feature provides amplification mechanisms for the optical switching process, as has been addressed in our previous publication [1; 14].
## 3 Results
### Vegetation Recovery in the Vegetation Damage Area
#### 3.3.3 The Relationship between Vegetation Recovery and Fault Zones  
There are two notable zones of vegetation recovery in the fault zones located in the study area (Figure 6): recovered fully type accounted for 54.09% in the western portion of the Wenchuan-Maoxian fracture where the proportion of recovered vegetation is the highest near the fault zones, and the district between the Beichuan-Yingxiu fracture and the Guan-Anxian fracture indicates the least recovered fully (23.41%).
## 5 Performance Evaluation
### Results and Discussion
#### 5.2.2 Impact of Different Intervals on BSM  
The different intervals with which the BSMs are generated also affect the performance of the multi-BSM. As more vehicles enter the connected environment, more BSMs are generated, and if the generation interval is kept very low then it could clutter the network performance. The control messages also need to be regulated so that they do not clog the network for other emergency messages that need to be disseminated in a timely manner. Therefore, the intervals of BSMs generation also play a vital role in the performance of the multi-BSM.  
Figure 6a shows the relationship between the increasing vehicle density with average transmission delay regarding the different intervals. It can be seen that the average transmission delay is kept at the lowest with the increase in the vehicle density when the generation interval is kept at 50 ms.  
Figure 6b shows the relationship between the packet delivery ratio with increasing vehicle density regarding different intervals. As more vehicles are entering the network, more BSMs are being generated, which in turn affects the packet delivery ratio; therefore, a suitable interval should be adoptedto improve the packet delivery ratio. As seen in Figure 6b, the packet delivery ratio is at its lowest when the BSM interval is kept at 15 ms and performs better at 50 ms.  
Figure 6c describes the comparison of the vehicle's velocity to the packet delivery ratio regarding different BSM intervals. The velocity of the vehicles is inversely proportional to the packet delivery ratio. The ephemeral effect is observed in vehicles with high velocities and the packet delivery ratio performs better when the BSM intervals are kept at 50 ms. The packet delivery ratio decreases with a decrease in the intervals as more BSMs are generated frequently, which can cause congestion in the network.  
Figure 7 shows the average routing delay alongside the number of packets that are being sent through the network. It is being observed that the most routing delay is incurred in the vouch algorithm, with the Grover and multi-BSM close to each other.  
Figure 5: The energy consumption of the algorithms at the RSU.
# Benefits of Advanced Air Mobility for Society and Environment: A Case Study of Ohio
## 4 Benefit Factors
### BF-6: Cost Savings in Agriculture
#### 4.6.3 Cost Savings in Livestock Monitoring  
Drones for livestock monitoring are picking up steam in many nations. Drones are employed to keep an eye on the herd, requiring less workforce and allowing the farmers to follow the animals better than ever before [75]. Farmers have also realized that using this technology will save them cash, time, and energy. A case study was conducted to monitor the impact of sUAS on farmland of \(l_{c}=980\) cattle and ewes [76]. In that study, it was found that the duty of inspecting the hill sheep was decreased from 40 min to 10 min, and, eventually, it saved \(ST_{1}=26\) man-hours per year. For checking some other cattle and sheep grazing on grassland, the time lowered from 1.5 h to 30 min, which saved a total of \(ST_{1}=104\) man-hours per year. Traditionally, workers used to monitor those animals either walking on foot or driving quad or truck, but drones saved a lot of labor time. The average farm labor in Ohio (\(FL\)) is on average $13.93 per hour [77]. According to USDA, the animal, requiring large farmland to graze, are cattle, sheep, and lamb. These animals were on our consideration during the study as focusing on larger farmland could be more effective than the other animals' farmland in terms of cost savings. Using Equation (42), we calculated \(E\), which indicates the estimated labor cost savings per year per animal:  
\[E=\frac{(ST_{1}+ST_{2})\times FL}{l_{c}} \tag{42}\]  
Using the forecasted number of animal in a year Livestock Count (\(L\)) (see Table 1), we obtained the annual estimated Value of Labor Cost Savings (**VLCS**) using Equation (43):  
\[VLCS_{i}=E\times L_{i}\times F_{ag}\qquad i\in\{2022,2023,...,2032(year)\} \tag{43}\]
# Advanced Brain Tumor Segmentation Using SAM2-UNet
## 2 Related Work
### U-Net++  
UNet++ is an advanced version of the classical UNet architecture, which is designed to reduce the semantic gap between the encoder and decoder feature maps through nested, dense skip pathways. This architecture enhances the ability of the model to capture fine-grained details and is effective for CT scans and polyp segmentation in colonoscopy videos. Yet, this model relies on annotated training data, which can be a limitation in scenarios with limited labeled datasets.
## 3 Results and Discussion
### Adsorption Isotherm  
In order to investigate the distribution of FCF between the aqueous solution and ionic liquid-modified magnetic polymer at a specific temperature during the adsorption process, the effect of the adsorption isotherms of a magnetic polymer on FCF at three temperatures was studied in this paper. The results are shown in Figure 10. The adsorption isotherm data in this study were fitted by the Langmuir and Freundlich isotherm models. The Langmuir model assumes that the surface of the adsorbent is uniform, the adsorption performance is the same everywhere, and the adsorption process is monolayer adsorption. After a certain adsorption site is occupied, no further adsorption will occur on this site. The Freundlich model is an empirical formula used to describe the adsorption on heterogeneous surfaces. The [PERSON] and Freundlich model formulas are:  
\[\text{Langmuir isotherm: Q}_{\text{e}}=\text{Q}_{\text{m}}\text{K}_{ \text{L}}\text{C}_{\text{e}}/(1+\text{K}_{\text{L}}\text{C}_{\text{e}}) \tag{5}\]  
\[\text{Freundlich isotherm: Q}_{\text{e}}=\text{K}_{\text{F}}\text{C}_{ \text{e}}^{1/\text{n}} \tag{6}\]  
In the formula, \(\text{Q}_{\text{e}}\) (mg/g) and \(\text{C}_{\text{e}}\) are the equilibrium adsorption capacity and equilibrium concentration of the adsorbent in the equilibrium state, respectively. \(\text{Q}_{\text{m}}\) (mg/g) is the maximum theoretical adsorption capacity. \(\text{K}_{\text{L}}\) and \(\text{K}_{\text{F}}\) are equilibrium constants related to adsorption capacity in Langmuir and Freundlich models, respectively, and the constants are related to adsorption performance; n characterizes the binding strength of the Freundlich model adsorbent-adsorbate interaction.  
It can be seen from the correlation coefficients in the Table 3 that the adsorption isotherms of magnetic polymers to FCF are more in line with the Freundlich equation, indicating that the adsorption of \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)@GMA@IM to FCF exhibits a multi-layer adsorption [25]; therefore, the adsorption process may have different adsorption effects, such as hydrophobic, \(\pi\)-\(\pi\) interactions and cation-\(\pi\) interactions, etc. From the simulated parameters, the n value of \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)@GMA@IM for FCF adsorption is greater than 1, which is a favorable adsorption, and the n value increases with the increase in temperature, which is consistent with the change trend of \(\text{K}_{\text{L}}\) and \(\text{K}_{\text{F}}\)[26; 27]. However, the maximum adsorption amount does not change much with the increase in temperature, which may be because chemisorption is less affected by temperature.  
Figure 10: Langmuir adsorption isotherm (**a**) and Freundlich adsorption isotherm (**b**) of \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)@GMA@IM.
## 4 Results and Discussion
### Model Fitting
#### 4.3.3 Silica Purity Grade (SPG)  
The reagents influencing SPG the most were QT and SS. High doses of QT have an undesired effect, achieving high values of silica concentration in the froth. This was due to the fact that QT acts as a depressant of carbonates and fluorite when high doses are used (Figures 6 and 7). PS has a behavior similar to QT in terms of SPG but with less notoriety due to its lower energy as a carbonate depressor [15]. Otherwise high doses of SS reach a minimum, around 8% of silica concentrate in the froth phase, due to the effect of SS as a silica depressant [36].  
Figure 5: 3D surface graphs of FPG showing the effect of PS, QT, SS, OA, and WD.  
Figure 6: 3D surface graphs of SPG showing the effect of PS, QT, SS, OA, and WD.  
Low doses of OA showed the lowest value obtained of concentration of silica 7.6%. This is due to the fact that OA also acts as a silica collector with less affinity than fluorite. Varying the dosage of WD showed no effects.  
Comparing what it is shown in graphs of Figure 6 and \(p\)-value of Table 6, it can be seen that QT is the only factor that make a significant effect on SPG (\(p\)-value under 0.05). Also, two factor interactions make a statistical significance on SPG which are QT-OA and OA-SS.
# Advancements and Prospects of Electronic Nose in Various Applications: A Comprehensive Review
## 2 E-Nose System
### The Electronic Nose's Brain: Algorithms and Visualization  
A crucial element within the realm of electronic noses is their cognitive core, often referred to as the \"brain\". This section delves into the intricate processes of visualization methods and algorithms, elucidating their significance in achieving selectivity and discernment in sensor array responses.  
The essence of an electronic nose's efficacy hinges on the processing and synthesis of responses from an array of sensors, which generally include signal filtering, switching, and feature extraction. The manipulation of data matrices derived from these sensors is pivotal for optimal performance.  
Once the signal has undergone preprocessing, it undergoes another round of analysis through pattern recognition. Through training, the electronic nose system grasps the traits of the odor under examination and predicts attributes of samples with unknown concentrations using a predefined model based on characteristic relationships. Commonly employed pattern recognition algorithms in electronic nose detection systems include principal component analysis (PCA) [78], linear discriminant analysis (LDA) [79], partial least squares regression (PLSR) [80], support vector machines (SVMs) [81], and artificial neural networks [82].
## 3 A Knowledge-Based Approach for the Investigation of Collaborative Workplaces
### Knowledge Acquisition  
The _Knowledge Acquisition_ aims to acquire the necessary knowledge, establish a set of relations and rule, and generate usable datasets. The HRC field is analyzed from three points of view: (i) the scientific literature, (ii) the regulatory framework and (iii) the industrial context.  
The overview provided in Section 2 allows for identifying the main areas of interest to deal with in the context of HRC workplace designing, according to the scientific literature: task allocation, safety, ergonomics, human-robot interaction, robot control, and so forth.  
In the same manner, the study and the critical analysis of the regulatory framework leads to the identification of the main requirements for ensuring safety and risk reduction within an HRC workplace. However, a detailed discussion on standards is redundant  
Figure 1: Depiction of the main phases of the Knowledge-Based Approach.  
considering that other works [46; 47; 48] already did it. It is sufficient to recall that the standards regarding robotics are organized into three types as follows:  
* Type A standards: basic safety standards and requirements that apply to machinery.
* B1 standards concern specific safety aspects.
- B2 standards concern safeguard measures, interlocking devices, and optical or pressure sensors.
* Type C standards: safety requirements for specific machinery.  
Among the latter type, it is worth mentioning the ISO/TS 15066:2016, which is the most recent standard in collaborative robotics and concerns the interaction between human and robot presenting four collaborative operations. Furthermore, it is a technical specification that describes in detail the allowed collaborative operations and specifies their requirements. It reinforces the indications given in ISO 10218-1/2 (Type C), that are the main standards concerning robots and robotic devices.  
From the industrial point of view, analyzing [49; 50], the following key points emerge:  
* When grouping the applications of collaborative robots by sector, about three quarters of them belong to electrical engineering and automotive sectors. There are fewer applications in the plant engineering and mechanical fields.
* Focusing on the category of application, the majority of applications concern assembling or material handling.
* Applications where a worker really collaborates in strict contact with the cobot are not very common. In most of the cases, humans and robots coexist sharing the same workspace only occasionally. Furthermore, cobots are usually placed behind a physical fence and used in the same way as classic industrial robots.  
Given the above, moving in this dense network of relationship and non-homogeneous information without a guide represents a grueling challenge. The acquired knowledge needs to be stored as rules, constraints, semantic representation, and facts to be easily manipulated. The following section shows how the large amounts of acquired information are managed and organized in a structured framework, thus enabling its exploitation in the context of the workplace layout design.
## 1 Introduction  
For numerous years, wireless communication systems and radar sensing systems have advanced independently, albeit within constrained connections. They share common terms, such as signal processing and system structure [1]. The coexistence of communication and radar systems has been a subject of extensive research, focusing on developing effective interference management techniques to enable the efficient operation of two independently installed systems. Radar and communication systems transmit distinct signals, even when physically located in proximity [1].  
An intriguing prospect involves exploring whether a single transmitted signal can serve both communication and radar sensing purposes to mitigate the interference in coexisting systems arising from transmitting two separate signals. The concept of extracting information from received radio signals, beyond the conventional signal demodulation associated with communication data, is termed radio sensing. Utilizing radio signals, radio sensing can be achieved by measuring sensing parameters and physical feature parameters [1].