esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Sensory Analysis of Portuguese Wild Hops  
Data from the CATA analysis was evaluated for the sensory characterization of the 15 Portuguese wild hops (Table 2). Resinous was the attribute perceived by most panellists. In almost all samples, except for PT01, the attribute was checked by at least half of the judges, being identified by at least 75% of assessors in PT03, PT05, PT06, PT07, PT09, PT10 and PT13. Regarding other attributes, it can be emphasized that 85% of assessors noted citrus fruits in PT14; non-citrus fruits were identified by 90% of the judges in PT14 and 75% in PT11. Moreover, 80% of panellists checked the floral characteristic for PT14 and spicy notes in PT05. [PERSON]'s Q test determined that the attributes citrus and non-citrus fruits, resinous, earth, floral and sulphurous differed significantly as a function of hop samples (Table 2). Regarding pleasant attributes, the highest number of checks was observed in PT14 for citrus, and in PT11, PT14, and PT15 for non-citrus fruits. Resinous was mostly perceived in PT05 and PT07 and floral in PT14. On the other hand, panellists detected unpleasant earthy and sulphurous attributes mostly in PT07 and PT08, respectively. The independence between the sum of attribute tables across panellists (Supplementary Table S1) was tested using the Chi-square distance, showing real differences between the hops in terms of their sensory profiles (\(p\) = 0.02). A dendrogram was constructed by Agglomerative Hierarchical Clustering, grouping the hops into two clusters by similarity (Figure 1). Group 1 (G1) included PT01, PT08, PT12, PT14 and PT15, and group 2 (G2) presented PT02-07, PT09-11 and PT13 hops. A Correspondence Analysis was plotted to visualize how the hops and attributes are relatively positioned. With 88.48% of explained total variance on the first two dimensions, it is possible to observe that samples of G1 were classified as sulphurous--mainly PT01, PT08 and PT12--also floral and both fruity attributes were predominant for PT14 and PT15. Hops included in G2 are more earthy
## 3 Results and Discussion  
### Chemical and Mineralogical Properties of Raw Materials  
A comparison of proximate and ultimate analyses of the raw materials studied is represented in Table 1. All samples were rich in volatiles. Cotton stems had the highest volatile matter content, fixed carbon and oxygen contents, and the lowest ash, nitrogen and sulfur concentrations. By comparison, the ash content of manure was high, whereas its carbon, hydrogen and oxygen contents were lower among the other materials. However, the amounts of sulfur and particularly nitrogen of manure and bio-solid samples were significant, revealing gaseous emissions during thermal treatment, but also increased capacity for plant nutrition when applied to soils for amelioration.  
Figure 1 illustrates the XRD spectra of the samples. The soil was of quarzitic nature, with small amounts of muscovite and chlorite minerals. The manure was rich in Ca, Mg, P and Fe phases, such as calcite, dolomite, whitlockite magnesian, hematite and rodolicoite. P was also incorporated in hydroxyapatite, whereas K was found in fairchildite. The bio-solid consisted mainly of whitlockite magnesian and considerable amounts of Ca and Fe bound in calcite, hematite and kennedyte. The cotton stems sample was dominated by K and Mg, in the forms of sylvite, arcanite and dolomite, respectively, and to a smaller extent by Ca identified in carbonates, phosphates and aluminosilicates.  
The concentrations of the major elements in manure and biochars, represented in Figure 2, are in accordance with the XRD analysis discussed above. Regarding nutrients, the higher quantities of Ca and Mg were measured in manure, the higher amounts of P and Fe were measured in the bio-solid, and those of K were measured in cotton stems biochar. These concentrations are comparable to those of similar materials reported by previous investigations [13; 18; 28]. However, it must be noted that the solubility of the minerals incorporating these nutrients governs their bio-availability in soils [18; 28].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Sample** & **Volatiles** & **Fixed Carbon** & **Ash** & **C** & **H** & **N** & **O** & **S** \\ \hline Manure (MAS) & 51.9 & 16.5 & 31.6 & 35.8 & 5.3 & 3.6 & 22.2 & 1.5 \\ Cotton stems (COS) & 73.4 & 18.3 & 8.3 & 46.8 & 6.5 & 1.6 & 36.7 & 0.2 \\ Bio-solid (BIS) & 65.6 & 16.6 & 17.8 & 43.0 & 6.6 & 7.9 & 22.9 & 1.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Proximate and ultimate analysis of raw materials (% dry).  
Figure 1: _Cont._
These advancements, especially through HBIM, underscore BIM's capacity to bridge traditional craftsmanship with modern analytical tools, fostering interdisciplinary collaboration among architects, engineers, and conservators while enhancing efficiency across project phases [21]. The successful application of BIM in preserving and analyzing complex vernacular structures underscores its potential to address the unique challenges of culturally significant architecture worldwide [22]. This potential is particularly pronounced in Vietnam, which has a rich and diverse architectural heritage shaped by its varied topography and historical cultural exchanges. The Vietnamese vernacular architecture (VVA), characterized by sustainable design solutions and locally sourced materials, reflects a deep synergy between human needs and environmental contexts [23; 24]. Iconic examples, from northern communal houses to Hoi An's heritage residences, embody regional identities that have persisted through external influences, including French colonial rule [25; 26]. However, BIM's application in VVA has been limited primarily to conservation and reconstruction, leaving its potential for new vernacular-inspired designs underexplored. For instance, studies on the Temple of Hung Kings in Ho Chi Minh City employed 3D laser scanning and HBIM to digitize and restore this historic monument, enhancing its cultural value through virtual tours and augmented reality [27, 28]. Similarly, HBIM workflows have modeled and conserved sites like the Complex of Hue Monuments, focusing on accurate reconstruction for operation and maintenance [29]. This focus on preserving existing structures leaves its potential for generating new vernacular-inspired designs underexplored. Specifically, there is a notable research gap regarding the use of BIM for automating the conceptual design process for such new constructions, particularly concerning the lack of automated workflows tailored for conceptual design and comprehensive 3D libraries that accurately represent VVA's non-standardized forms. This study investigates the integration of BIM into VVA, aiming to address existing limitations by proposing initial design concepts for new constructions inspired by vernacular forms. The research advances the field by developing high-resolution 3D models of architectural components alongside an automated system for assembling these elements into functional spatial configurations, thereby improving design applicability and operational efficiency. The methodological framework comprises four key phases: data collection, data analysis, 3D modeling of structural components, and the development of the automation system. The findings support engineers and architects in efficiently generating preliminary designs that harmonize vernacular aesthetics with contemporary construction standards. Ultimately, this approach promotes sustainable development within Vietnam's construction industry while safeguarding intangible cultural values. It introduces a novel paradigm for culturally responsive architectural innovation that bridges traditional heritage with modern-day functional demands.
## 3 Results and Discussion
### Effect of AC on PM  
Figure 4 shows the changes of PM mass concentration before and after the reactor at different powers and the PM removal efficiency by activated carbon. Since the diesel engine used in this paper used a natural air intake mode, the volume of the intake air did not change with the diesel engine power. However, as the fuel injection amount gradually increased with the engine power, the theoretical air volume for the fuel oil complete combustion increased. This will result in a gradual decrease in the O\({}_{2}\) concentration of the exhaust gas as the power increases, and an increase in the PM mass concentration due to incomplete combustion. When the power was lower than 6 kW, the PM concentration of the reactor inlet was lower than 250 mg/Nm\({}^{3}\), and with the further increase of power, the mass concentration of PM increased rapidly. When the power reached 15 kW, the mass concentration of PM reached 1282.2 mg/Nm\({}^{3}\), at which the removal efficiency of PM was 65.4%. The PM removal efficiencies of the AC reactor were above 60% over the entire power range, and the highest PM removal efficiency achieved at 9 kW reached 77.0%.  
Figure 3: Gas temperature and pressure drop changes.  
To date, there has been no report that activated carbon can catalyze the removal of PM. The main reason for the PM removal in the experiment may be filtration and adsorption by the AC. When the exhaust gas flows through the gap of the granular activated carbon, its speed and direction changes greatly. The PM with a larger particle size gradually decreases and eventually deposits. The PM with smaller particle size will be adsorbed on the surface of the activated carbon. However, it will result in a gradual increase in the pressure drop of the reactor and will affect the denitration ability of the AC. In practical applications, it may be necessary to replace the AC in front of the reactor according to the change of the pressure drop, or to install an exhaust fan after the reactor. The method of diesel particulate filter (DPF) regeneration may be used. For example, activated carbon can be made into honeycomb and regenerated by backwashing.
# Optimizing Airfoil Aerodynamic Characteristics by Using Proposed CSA-KJ Method
## 1 Introduction
[PERSON] [14] utilized the PSOA without considering robustness to optimize the shape of the transonic airfoil; however, the results may be affected by data disturbance and noise in the model. Compared with GA and PSOA, Cuckoo Search Algorithm (CSA) not only has higher search efficiency [15] but also better convergence and global search ability [16]. Moreover, the accuracy, stability and parameter optimization of CSA are better than those of GA and PSOA [17; 18]. It should be noted that CSA has been widely utilized in airfoil optimization and has obtained good results [19; 20]. Therefore, CSA was applied to optimize the airfoil profile in order to identify the airfoil with better aerodynamic characteristics in this study. The Spalart allmaras (SA) turbulence model has been widely used in airfoil aerodynamic analysis [21; 22; 23; 24]. [PERSON], [PERSON], and [PERSON] [25] proposed a turbulence model to analyze the airfoil flow field based on the lattice Boltzmann method and compared it with the results of the SA turbulence model. [PERSON], [PERSON], and [PERSON] [26] applied the computational fluid dynamics FLOWer code to verify the \(k-\omega\) shear stress transport (SST) turbulence model. The corresponding lift and drag coefficients were obtained with the aid of the steady-state calculation on the applied airfoil. [PERSON] and [PERSON] [27] verified that the \(k-\omega\), SA and the transition SST model had high accuracy when the Reynolds number and the angle of attack were significant (i.e., above 10-12\({}^{\circ}\)). [PERSON], and [PERSON] [28] used the SA turbulence model to assess the accuracy of the unsteady aerodynamic characteristics of the SC1095 airfoil under free flow velocity/pitch oscillation coupling. From these studies, it can be concluded that the SA turbulence model has a high-level accuracy, so it can be effectively used to analyze airfoil aerodynamic characteristics. In summary, while the KJ theorem has made some progress in calculating airfoil lift, and CSA has a significant impact on airfoil optimization, few studies have combined the two in their optimization efforts. In view of this, the CSA-KJ aerodynamic optimization method is achieved in this work by taking KJ airfoil as the objective function and CSAas the iterative updating tool, applied to the NACA4412 airfoil. The airfoil aerodynamic characteristics are then calculated and analyzed using the SA turbulence model, providing an effective method for optimizing the aerodynamic performance of other airfoils.
## 4 Results
### Within-Case Analysis
#### 4.1.4 Case 4  
The interview was focused on the Italian warehouses of Polpenazze del Garda (BS), where the products of the industrial, food, and fast-moving customer goods sectors are mainly managed (for further information on the 3 PL and warehouse features, see Table 3).  
The main takeaways on the importance of the warehouse features and indicators are discussed below.  
1. Location. In the manager's view, this is the most important feature. Better locations lead to better prices and transportation fares. Furthermore, since the transportation is carried out through direct channels (they operate in the B2B market), they can exploit total potential fixed costs, thanks to their vehicles' optimal saturation (Full Truck Load). Therefore, the impact on transportation costs (rated as \"A\") is mainly determined by the total fares related to the location area. Moreover, the interviewee also rated the location as \"A\" in relation to the on-time delivery, as the closer the warehouse is to the network, the higher the service provided by the 3 PL will be.
2. External Areas. In this case, the interviewee highlighted how important the external area is for new e-commerce and parcelled transportation market trends. Moreover, he emphasized the role of the appearance of a logistics building; even though the external areas, in his opinion, are not directly crucial for the attainment of a good service level (indicators such as \"responsiveness\", \"delivery lead time\", and \"on-time delivery\" are rated as indifferent or \"I\", meaning that quality of external areas does not have any influence on them), the physical appearances and the status of the building are significant for the customer's opinion.
3. Building. This feature is crucial since the manager thinks that good internal efficiency can compensate for external conformation issues, such as the number and density of loading docks. This is why the interviewee considers that the 3 PL's productivity, flexibility, and responsiveness are mainly affected by the building's features (i.e., layout and size). In order to have good internal efficiency, building factors are crucial (rated as \"O\"). Instead, the features are rated \"M\" for the operative cost. Moreover, the manager expressed again the idea that the firm operates in a B2B context, so their warehouses are directly observed and rated by other customer companies. Therefore, the care the building is treated with will reflect customers' perception of service quality. To quote the manager, \"Our warehouses must be as attractive as they should have to be to get many likes on social platforms\".
4. Internal Areas. According to the interviewee, this is the least important feature; it is generally rated as \"1\" for all the indicators. The interviewee generally saw the features related to equipment systems such as photovoltaic and fire-fighting systems as most relevant.
# Inversion of Rayleigh Wave Dispersion Curve Extracting from Ambient Noise Based on DNN Architecture
## 2 Materials and Methods
### Parameters Test
#### 2.4.3 The Number of Network Layers  
The series of layers between input and output is referred to as hidden layers. Having more than one hidden layer allows a neural network to approximate the nonlinear relationship between input and output data. However, having too many hidden layers may lead to overfitting, where the model performs poorly when faced with new data. Currently, there are no general rules available for selecting the number of layers in research. In this study, we employed seven network models with varying numbers of layers, ranging from three to nine, and calculated their corresponding errors. As shown in Figure 9, the optimal number of layers for training the theoretical synthetic dataset was six, with an average relative error of 8.9%. This indicates that the problem of Rayleigh wave dispersion curve inversion exhibits highly nonlinear characteristics, and shallow neural network models are ineffective in learning the features effectively.
# How Close Is Existing C/C++ Code to a Safe Subset?
## 2 Background
### Memory Safety Errors in C/C++  
C and C++ have long been plagued with memory safety errors due to the lack of an enforced type of safety, lack of bounds checking, and the use of manual memory management. These design choices make these languages more efficient in terms of performance, but they leave room for bugs that can be exploited as vulnerabilities.  
Spatial errors occur when a program writes data past the boundaries of an allocated memory buffer. This can lead to corruption of adjacent memory locations and potentially result in security vulnerabilities. Such errors are often exploited by attackers to execute arbitrary code or gain unauthorized access to a system. Similar errors can occur when casts between pointer types allow for a write outside of an allocated object or a mismatch between the types being written to and used. Figure 1 demonstrates a buffer overflow similar to a vulnerability found in the SSH1 protocol [17]. In this code, an array is allocated with a size that is dependent on user input. If the user inputs a large enough number, the calculation may wrap around from a large number back to zero, thus leading to an attempt to allocate a zero-sized array. The subsequent access to the zero-sized array will write to memory that has not been allocated, which is a spatial safety error. In the context of a larger code base, errors like this can become security vulnerabilities.  
List of Figures 1: Buffer overflow in C++ due to integer overflow. If the size calculation wraps around to 0, the array is allocated with size 0, thus leading to an overflow (even at index 0).  
Lifetime errors occur when a program accesses a memory location after the associated object has been deallocated or when the pointer points to an invalid memory address. Figure 2 demonstrates a lifetime error caused by storing the address of a stack-allocated variable into a global pointer. The global pointer has a longer lifetime than the stack-allocated variable, thereby allowing the pointer to refer to the memory after it has been reallocated for a new purpose. These errors can lead to crashes, data corruption, and unpredictable behavior in a program. The manual memory management in C and C++ means that developers must be meticulous in tracking and managing pointers, which can be error-prone and complex. A number of similar issues, including null and uninitialized pointer dereferences, fall into the same category of errors.  
```
int*p=nullptr; voidfoo(){ intx=5;... p=&x; } voidbar(){ inty=0;... *p=10; }
```  
Figure 2: Use-after-free errors in C++ due to assigning a stack-allocated address into a global pointer.  
recommended types of manual or automated testing\". In response to this executive order, both the NSA and NIST released guidance that, in part, recommended the use of memory-safe languages.
## Appendix A  
### Calculations for Matching at Different Harmonics for an Inductor Converted to an Equivalent Circuit  
The following two cases are possible:  
1. The parallel resonator is tuned to the 2 nd harmonic, and the net impedance is shorted at the 3 rd harmonic. In this condition, the design equations are as follows: \[1-4\omega_{0}^{2}L_{p2}^{L}C_{p2}^{L}=0\] (1) \[Z(3\omega_{0})=3j\omega_{0}L_{a2}+\frac{3j\omega_{0}L_{p2}^{L}}{1-9\omega_{0} ^{2}L_{p2}^{L}C_{p2}^{L}}=0\] (2) \[j\omega L_{0}=j\omega L_{a2}+\frac{j\omega_{0}L_{p2}^{L}}{1-\omega_{0}^{2}L_ {p2}^{L}C_{p2}^{L}}\] (3) Substituting \(\omega_{0}\) from Equation (1) into Equation (2) and after simplification, a relationship between \(L_{a}\) and \(L_{p2}^{L}\) is obtained. \[L_{a2}-\frac{4}{5}L_{p2}^{L}=0\] (4) Now, using (1) in (3), we have the following: \[L_{0}=L_{a2}+\frac{4}{3}L_{p2}^{L}\] (5) The parameters are given by Equations (45)-(47).
2. The parallel resonator is tuned to the 3 rd harmonic, and the net impedance is shorted at the 2 nd harmonic. Under that condition, the design equations are as follows: \[1-9\omega_{0}^{2}L_{p3}^{L}C_{p3}^{L}=0\] (6) \[2j\omega_{0}L_{a3}+\frac{2j\omega_{0}L_{p3}^{L}}{1-4\omega_{0}^{2}L_{p3}^{L} C_{p3}^{L}}=0\] (7) \[j\omega L_{0}=j\omega L_{a3}+\frac{j\omega_{0}L_{p3}^{L}}{1-\omega_{0}^{2}L_ {p3}^{L}C_{p3}^{L}}\] (8) Using Equations (6) and (7), a relation can be derived between \(L_{a}\) and \(L_{p3}^{L}\). \[L_{a3}+\frac{9}{5}L_{p3}^{L}=0\] (9) Equation (9) implies that one of the parameters, \(L_{a}\) and \(L_{p3}^{L}\), must be negative. Hence, this condition is not possible.
# Management Platforms and Protocols for Internet of Things: A Survey
## 7 IoT Device Management Protocols
### COntrained Networks and Devices MANagement (COMAN)
#### 7.1.2 Oma-DM--Open Mobile Alliance Device Management  
OMA-DM provides the management information for connecting devices with the DM tree model [70; 71; 72] and remotely managing connected devices through the OMA-DM management protocol [73]. It provides efficient methods to manage connected \"things\" in network environments using: (i) configuration maintenance and management, (ii) configuration of user preferences, (iii) fault detection, query and reporting, (iv) non-application software download, (v) provisioning, and (vi) software management.  
The OMA Device Management is divided into DM Server and DM Client devices [74]. Th standard format for communication messages and data transports uses the XML format for the following technologies: physical layers lines or wireless networks (GSM, IrDA, Ethernet or Bluetooth) and transport layers over Wireless Session Protocol (WSP)/WAP [75], HTTP [76], OBEX [77] or similar transports, as shown in Figure 17.  
OMA-DM performs data exchange and Device Management with XML data through a DM server/client communication [78]. The OMA-DM consists of two phases: (i) a configuration phase, after authentication enables the exchange of device information through the user commands (Add, Alert, Copy, Get, and others) sent to the DM Client; (ii) the management performs the request/response messages (Status, Generic Alert, and Results) between DM server/client.  
_Pros: Standard communication model._  
_Cons: No supports the heterogeneity networks and security._  
Figure 16: Illustration of the CoAP architecture.  
Figure 17: OMA-DM standard management architecture.