esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Agriculture Applications Using Deep Learning Technologies: A Survey
## 3 Deep Learning
### Convolutional Neural Network (CNN)
#### 3.1.1 Convolutional Layer  
The convolutional layer is the most basic and the most significant in a CNN. The resulting pixel matrix for the supplied picture or object is twisted or multiplied to build an activation map for the given image. The activation map's main advantage is that it stores all of the distinguishing qualities of a given image while limiting the amount of data that must be processed simultaneously. The data is combined in a features detector matrix, and various image variants are created by employing different feature detector levels. The convoluted model is also trained with backpropagation to achieve the lowest possible error in each layer. The depth and padding are determined by the error set with the fewest errors [11]. The convolutional layer is responsible for extracting visual features.
# An Overview of the Historical Retrofitting Interventions on Churches in Central Italy
## 4 Assessment of the Seismic Interventions  
In this Section, the seismic behavior of masonry churches is analyzed by subdividing them into macroelements, i.e., architectural portions with similar seismic responses. The evaluation of the macroelements and associated collapse mechanisms started with the Friuli earthquake of 1976 [15] and culminated with the definition of the A-DC survey form for churches in 2013 [48]. By exploiting these forms, the post-emergency activities carried out on cultural heritage buildings after the 2016 seismic sequence [83] enabled assessment of the earthquake-induced damage surveyed in the investigated sample of masonry churches in relation to the 28 possible collapse mechanisms defined by the Italian \"Guidelines for  
Figure 21: Example of interventions on the roof: (a) heavy concrete roof of Madonna del Carmine Church, Cortino (TE); (b) rigid beams inserted in the walls of the S. Martino Church, Norcia (PG).  
the evaluation and risk reduction of Cultural Heritage\" [84]. Indeed, the compilation of the form allowed, for each collapse mechanism, to rate the observed damage with a grade from 0 (no damage) to 5 (collapse), according to the damage criteria defined by [PERSON] [85] (introduced for buildings, but transposed for single macro elements in several subsequent research works [24]), and to infer therefrom an overall damage index (i\({}_{\text{d}}\)) [86], making possible a statistical evaluation of the most common damage mechanisms that occurred in various types of churches.  
The practical goal of the A-DC form is the rapid evaluation of the usability of the churches [87,88] as well as the definition of emergency measures and future costs of restoration; \"usable\" indicates a church without structural damage; \"usable with countermeasures\" refers to churches that need protection measures before being opened; \"partially usable\" refers to churches that are partially accessible only; \"temporarily unusable\" indicates churches whose survey is not complete, and further inspections are needed to take definite decisions; \"unusable for external risk\" indicates the presence of external unsafe elements that threaten the safety of the church; \"unusable\" refers to churches whose structural stability is compromised. Figure 22 shows the usability assessment outcome of the sample of churches presented in the introduction. As mentioned above, the sample consists of churches directly surveyed by the authors plus additional churches catalogued within the Da.do database.  
The form also proposes suitable safety measures, depending on the type of identified mechanism. As for Central Italy, propping was the most used temporary counter measure to inhibit the overturning of facades; very often, traditional propping systems were integrated with the use of steel cables or polyester bands, which proved suitable for counteracting the overturning of the cracked facades, in particular those ones detached by the orthogonal walls, without obstructing the streets around the buildings [89].
# Stochastic Natural Vibration Analyses of Free-Form Shells
## 4 Results and Discussion
### Shell Model 1: A Negative-Gaussian Free-Form Shell
#### 4.1.2 Stochastic and Sensitivity Analysis
The reason is that the increase (or decrease) of shell thickness leads to the increase (or decrease) of both stiffness and mass, which affects the frequencies in the opposite ways; in contrast, the increase (or decrease) of elastic modulus only affects the stiffness. Therefore, in most cases, the impact of the elastic modulus is larger than shell thickness. The difference between their significance depends on the geometrical shape to some extent. The stochastic and sensitivity analysis is repeated for the second and third natural frequency. Similar results are obtained.
## 3 Physical Layer
### Spectrum Sensing Techniques
#### 3.1.3 Energy Detection  
Both of the previously outline methods focus on the accurate and efficient sensing of certain predetermined target signals, built upon the assumption that related _a priori_ knowledge is in place. The energy detection is an alternative sensing technique which does not depend on such a precondition.  
The concept of energy detection is straightforward: the receiver monitors the power level present in the frequency and the result is compared with a preset threshold to determine whether the channel is idle or busy [27]. Although energy detection might not be able to match the performance of the other two methods in terms of detecting specific signals [32], it provides a cost-effective option well suited to a much wider range of user scenarios for _IoT_. The growing number of wireless applications using the ISM band including WiFi and Bluetooth makes the part of spectral space increasingly crowded, thus resulting in a greater chance of interference. In this case, the focus of spectrum sensing shifts from target detection to channel quality estimation for determining the desirable frequencies to use. It is therefore no longer of great significance to distinguish particular sources of transmission power. The study in [26] demonstrates that energy detection is able to deliver satisfactory results with cooperative sensing and sometimes even outperforms feature detection-based approaches. Accordingly, energy detection canserve as an inexpensive yet effective spectrum-sensing technique that is the most commonly adopted approach for spectrum sensing [20, 25, 26, 33, 34].
## 3 Results
### RT Values
Particularly, in the BL orientation the ISQ values were 75.20 \(\pm\) 1.10 ISQ for Due Cone implants and 75.00 \(\pm\) 0.71 ISQ for Maestro implants in the 40 PCF density block, while 51.80 \(\pm\) 1.79 ISQ for Due Cone implants and 56.00 \(\pm\) 7.97 ISQ for Maestro implants in the 10 PCF density block. In the MD orientation, instead, the ISQ values were 74.80 \(\pm\) 1.92 ISQ for Due Cone implants and 74.40 \(\pm\) 0.89 ISQ for Maestro implants in the 40 PCF density block, while 40.60 \(\pm\) 3.29 ISQ for Due Cone implants and 49.00 \(\pm\) 11.51 ISQ for Maestro implants in the 10 PCF density block. Contrarily, no differences were found when comparing the highest values to those of the same implants inserted into 20 and 30 PCF density blocks. Specifically, in the BL orientation Due Cone implants expressed 69.60 \(\pm\) 2.30 ISQ and 71.00 \(\pm\) 0.71 ISQ, respectively, while Maestro implants were 68.60 \(\pm\) 2.07 ISQ and 69.20 \(\pm\) 4.97 ISQ, respectively. In the MD orientation Due Cone implants expressed 70.60 \(\pm\) 0.89 ISQ and 6.20 \(\pm\) 4.44 ISQ, respectively, while Maestro implants were 69.40 \(\pm\) 1.95 ISQ and 65.40 \(\pm\) 2.97 ISQ, respectively. The lowest RFA values were recorded in the lowest-density block of 10 PCF for both implants. For Due Cone implants they were 51.80 \(\pm\) 1.79 ISQ in the BL orientation and 40.60 \(\pm\) 3.29 ISQ in the MD orientation. Instead, for Maestro implants they were 56.00 \(\pm\) 8.00 ISQ in the BL orientation and 49.00 \(\pm\) 11.51 ISQ in the MD orientation. These values were significantly lower if compared to all the other blocks. Indeed, in the BL orientation, they expressed a \(p\) < 0.0001 if compared to 20 (69.60 \(\pm\) 2.30 ISQ), 30 (71.00 \(\pm\) 0.71 ISQ), and 40 (75.20 \(\pm\) 1.10 ISQ) PCF density blocks for Due Cone implants, while a \(p\) < 0.001 if compared to the 20 PCF density block (68.60 \(\pm\) 2.07 ISQ) and a \(p\) < 0.0001 compared to 30 (69.20 \(\pm\) 4.97 ISQ) and 40 (75.00 \(\pm\) 0.71 ISQ) PCF density blocks for Maestro implants. As regards the MD orientation, they expressed a \(p\) < 0.0001 if compared to 20 (70.60 \(\pm\) 0.89 ISQ), 30 (67.20 \(\pm\) 4.44 ISQ), and 40 (74.40 \(\pm\) 0.89 ISQ) PCF density blocks for Due Cone implants, while a \(p\) < 0.001 if compared to the 30 PCF density block (65.40 \(\pm\) 2.97 ISQ) and a \(p\) < 0.0001 compared to 20 (69.40 \(\pm\) 1.95 ISQ) and 40 (74.40 \(\pm\) 0.89 ISQ) PCF density blocks for Maestro implants (Figure 9). Table 3 provided Confidence Intervals and _p_-values for each intergroup comparison. Figure 9: Bar graphs reporting Resonance Frequency Analysis (RFA) values recorded in Bucco-Lingual (RFA-BL) and Mesio-Distal (RFA-MD) orientations. Values were expressed in the ISQ score and measured after the insertion of Due Cone and Maestro implants in all polyurethane foam densities.
# Quality-of-Service-Linked Privileged Content-Caching Mechanism for Named Data Networks
## 2 NDN and QoS  
Research groups under the IETF have explored the issues of traffic flow classification [14], congestion control QoS services for ICNs [15], and resource management for content with different priorities [16]. Sections 2.1-2.3 provide the takeaways for the same issues.
## 2 Materials and Methods
### Texture Feature Extraction
#### 2.3.1 First-Order Statistical Descriptors  
Six texture features that are based on the first order statistics were extracted: pixel intensity mean, variance, standard deviation, skewness, kurtosis, and energy. These features depend on the single gray level of the pixel and the mathematical descriptions are defined in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature Name** & **Mathematical Description** \\ \hline Mean (\(m\)) & \(m=\sum_{x=1}^{M}\sum_{y=1}^{N}\frac{I(x,y)}{M\times N}\) \\ Standard deviation (\(\sigma\)) & \(\sigma=\sqrt{\frac{\sum_{x=1}^{M}\sum_{y=1}^{N}\{I(x,y)-m\}^{2}}{M\times N}}\) \\ Variance (\(\sigma^{2}\)) & \(\sigma^{2}=\frac{\sum_{x=1}^{M}\sum_{y=1}^{N}\{I(x,y)-m\}^{2}}{M\times N}\) \\ Skewness (\(S_{k}\)) & \(S_{k}=\frac{1}{M\times N}\frac{\sum_{x=1}^{M}\sum_{y=1}^{N}\{I(x,y)-m\}^{3}}{ \sigma^{3}}\) \\ Kurtosis (\(K_{t}\)) & \(K_{t}=\frac{1}{M\times N}\frac{\sum_{x=1}^{M}\sum_{y=1}^{N}\{I(x,y)-m\}^{4}}{ \sigma^{4}}\) \\ Energy\({}_{1}\) (\(E_{1}\)) & \(E_{1}=\sum_{x=1}^{M}\sum_{y=1}^{N}I(x,y)^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mathematical description of first-order statistical features.  
Figure 1: Representative patellar tendon images of one player displaying both the dominant and non-dominant side.  
Figure 2: Representative patellar tendon images showing the non-dominant side of two players, one with a normal tendon structure (**right panels**), and one player with an abnormal tendon structure (**left panels**). An abnormal tendon structure was taken as one that fell into the category of subclinical tendinopathy as arbitrarily defined in the study by [PERSON] et al. (2013). All scans reported in the figure were acquired with the knee at a 30\({}^{\circ}\) position. Long: longitudinal.
## 3 Results and Discussion  
### Oil Pressure Simulation Result  
Figure 6 shows the result of an analysis scenario for a model constructed by mixing AHC and DSC. The analysis scenario was implemented using the concept of system installation.  
Figure 4: Motion of heave and the AHC cylinder.  
Figure 5: Boundary condition of multi-body dynamics.  
For the heave, viz. the displacement excitation input, with the DSC system set to neutral, the vibration attenuation characteristics of the crown block were analyzed through PID control of the AHC system. When the DSC cylinders and piston accumulator reach the neutral position, excitation starts.  
Using the AHC and DSC mixed model, the performance was analyzed by grasping the displacement attenuation characteristics of the crown block for the test bed input. Good performance is achieved when the change in the displacement of the DSC cylinder is reduced by the PID control of the AHC system.  
In Figure 6a,b, it can be seen that the dead band of the servo valve is 20%, with the valve vibrating at a high frequency for control. An optimal servo valve is required to fit the control specification of the AHC. If the capacity of the servo valve is relatively large, the degree of control drops, and, if the dead band scope is wide, the controllability deteriorates. The valve should be selected to fit the capacity of the AHC considering the target performance. Thus the DSC system, AHC system and the controller must satisfy the target performance.
# Assessment of Different Stochastic Models for Inter-System Bias between GPS and BDS
## 4 Results and Discussion
### Accuracy Improvement during the Convergence Period  
During the convergence period, the accuracy of GPS/GLONASS combined PPP can be improved after considering the inter-frequency biases (IFB) [36]. So, similar analysis of accuracy improvement in GPS/BDS fusion PPP when considering ISB during the initialization phase is made in this study.  
Tables 7 and 8 show the statistics of the positioning accuracy during the convergence period. It can be seen from the tables that no matter which product is applied, if ISB is neglected, the accuracy in N, E, and U components is lower than the schemes with considering the ISB during 8 min and 16 min  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Convergence**} & \multicolumn{3}{c}{**Convergence**} & \multicolumn{3}{c}{**Accuracy**} & \multicolumn{3}{c}{**Accuracy**} \\  & \multicolumn{3}{c}{**Time (95\%)/min**} & \multicolumn{3}{c}{**Time (68\%)/min**} & \multicolumn{3}{c}{**(95\%)/cm**} & \multicolumn{3}{c}{**(68\%)/cm**} \\ \cline{2-13}  & **N** & **E** & **U** & **N** & **E** & **U** & **N** & **E** & **U** & **N** & **E** & **U** \\ \hline OFF & 7.5 & 17.5 & 17.0 & 6.5 & 15.5 & 13.0 & 4.9 & 6.1 & 7.6 & 2.0 & 2.7 & 3.2 \\ PW & 7.0 & 14.0 & 14.0 & 5.0 & 12.5 & 12.0 & 4.8 & 6.6 & 7.6 & 2.0 & 2.9 & 3.2 \\ RW & 7.0 & 21.0 & 14.5 & 5.5 & 17.5 & 12.5 & 5.0 & 7.0 & 7.8 & 2.1 & 3.3 & 3.3 \\ AD & 7.0 & 14.0 & 14.0 & 5.0 & 12.5 & 12.0 & 4.8 & 6.2 & 7.4 & 2.0 & 2.7 & 3.1 \\ WN & 7.0 & 23.5 & 14.5 & 5.5 & 19.0 & 12.5 & 5.2 & 6.8 & 8.0 & 2.2 & 3.3 & 3.3 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Convergence time and positioning accuracy of GPS/BDS combined kinematic PPP with the WUM products in five different schemes.  
Figure 10: GPS/BDS combined kinematic PPP convergence performance in five different schemes using _WUM_ precise products.  
convergence period both at 95% and 68% quantiles. Therefore, we can conclude that considering ISB also has a great improvement on the PPP positioning accuracy during the convergence period. With GBM precise products, the average improved rate can be 40.1%, 35.4%, and 41.0% in N, E, and U components, respectively. In addition, for the _WUM_ products, this rate can be 25.4%, 22.3%, and 33.4% in three components, respectively.
## 3 Important Factors in Efficacy of Amphiphiles  
### Role of Lignin  
L Lignin is known to reduce the efficiency of enzymatic hydrolysis for several reasons. (i) Lignin creates a shield for cellulose against of the physical, microbial or chemical degradation and restricts the significant swelling of the cell wall, thereby restricting accessibility of cellulose to the enzyme. This necessitates the escalation in enzyme utilization or extension in reaction times to achieve high conversions [1,75,76]. (ii) Lignin unproductively adsorbs a large fraction of the cellulase, making it unavailable for enzymatic hydrolysis of cellulose [77]. Evidence from flow through systems strongly suggests that the remnant lignin in solution during pretreatment with acid or water/steam reacted to form compounds with limited solubility that precipitated back on the surface of biomass [58,59]. As can be understood, elimination of lignin or alleviation of its negative impact on enzymatic reaction can be greatly beneficial in improvement of hydrolysis. Unfortunately, the pretreatment techniques developed thus far are not quite successful at sufficiently and economically eliminating the lignin. In addition, pretreatment techniques such as organosolve pretreatment tend to be too expensive to make the ethanol production from cellulosic biomass justifiable, therefore techniques that can isolate the lignin can be of great value for the biomass conversion process.  
Some studies have shown that amphiphiles such as surfactants (e.g., Tween 20, Tween 80, Triton-100, SDS, DoTAB) and protein-based biopolymers such as casein and BSA are more effective at improving the enzymatic hydrolysis yield of lignin containing substrate than pure cellulose [1,27,36,42], while other studies demonstrated that these additives (enzyme stabilizers) are as effective on pure cellulose as they are on lignocellulosic materials [12,26]. It was observed that although the cellulase was adsorbed to pure cellulose significantly [29,36], the concentration of cellulase started to increase in solution after 1 h [29], meaning that the adsorbed cellulose started to desorb from substrate after the hydrolysis reaction was completed. Although it is believed that lignin is the primary cause of enzyme deactivation, the negative impact of crystalline cellulose might also be present [29], as it was shown that the improvement in enzymatic hydrolysis yield was increased as the concentration of crystalline cellulose was decreased.
## 2 Piezoceramic Transducer and Monitoring Principle  
### Piezoceramic Transducer  
Piezoelectric materials, such as piezoceramics, have characteristics of actuating and sensing in a wide frequency range, which makes them suitable for the SHM field [58; 59; 60; 61]. The piezoelectric effect includes the direct and converse piezoelectric effect [62; 63]. Owing to these characteristics, the piezoceramic materials can be used as a sensor and an actuator [64; 65; 66]. The piezoelectric material has a strong commercial availability and piezoelectric effect; therefore, it is highly applied in this research. Furthermore, we used PZT disks as transducers so as to enable the TR method to monitor steel bar corrosion.
# The Potential of AI-Driven Assistants in Scaled Agile Software Development
## 6 Discussion
Our analysis, supported by empirical studies, underscores that the integration of AI across a diverse spectrum of domains within SADMs has the potential to result in positive outcomes in terms of organizational performance and potential. **RQ3: _Which aspects of SADMs do AI-driven assistants improve?_**  
Expanding upon the insights derived from the SLR findings, as presented in Section 4, our subsequent step involved categorizing the identified assistants, aimed at supporting organizations and companies in addressing specific challenges. In this subsection, we classified these assistants into the overarching category of _Usage Domains_, encompassing areas such as human assistance, risk prediction, problem solving, and various others. Upon closer examination, we noticed an interesting finding; some assistants cross several sections within the category. This underscores the remarkable adaptability and applicability of AI-driven assistants, illustrating their capacity to address a wider range of challenges. Furthermore, we performed a categorization of the most common challenges based on different configurations of SAFe, which is presented in Section 5. This approach allows organizations to pinpoint challenges corresponding to the specific SAFe configuration they have implemented. Consequently, our categorization offers a different perspective, enabling organizations to deploy AI-driven solutions strategically and manage a wide range of challenges across their organizational structures effectively.
## 1 Introduction
Recently, many handling techniques, such as Response Surface Methodology [14], the Genetic Algorithm [14], Particle Swarm Optimization (PSO) [15; 16; 17; 18], and Neural Networks [15; 19; 20] have developed to optimize machining parameters that are time consuming and require a coding system to resolve multi-criteria decision-making (MCDM) problems. Grey relational analysis (GRA) was conducted to optimize the multi-input factors for performing the best response of output factors [21; 22; 23]. GRA is a section in the grey system first deduced [24; 25], which is an advantageous technique to deal with poor-quality, deficient, and dim data [26] that is nominated for grey data. The GRA was further improved with the extension of fuzzy logic theory into the system with more quantities of variables to achieve a more advantageous response output [27; 28; 29]. The technique for order of preference by similarity to an ideal solution (TOPSIS) method is a simple and efficient way to solve processes using MCDM [30; 31]. In general, it is not easy to produce the ultimate solution to a problem in real-time. Thus, TOPSIS method is available with two different classes of the ultimate decision based on the strongest or weakest response of the option correlated with various measures. The classify-based approach [32] is studied to choose the strongest and weakest response, as short as possible, from and to the ultimate decision. The TOPSIS method expects the probable input data for resolving multiple criteria problems. The weight ratio determination helps to give exact correlated output responses in real-time problems [33; 34]. The benefits of TOPSIS come from it being simple and understandable, and applying deftly computation techniques [35]. Previous studies usually only assessed the weight and percentage contribution of the input factors. However, the weight of the output factors plays a very important role in attribute group decision-making. Determining and measuring the weight of decision-making is an interesting study field. The goal of this study was to propose a novel approach to determine the optimal parameter of the CFRP drilling process, as shown in Figure 1, in which an extended TOPSIS technique, based on the grey coefficient with the contribution of the entropy weights, is presented. The remaining part of this study was presented as follows: Section 2 gives the material and method. The design of the experiment included the selection of the material characteristic, the specification of the drilling tool, the design of the parameter process, and measurement process. The grey relational coefficient embed to the technique for order of preference by similarity to an ideal solution (Grey-TOPSIS) algorithm was also described. Then, the analysis and discussion was shown in Section 3. The proposed method of applying the data results from the experiment was performed and the optimal results are analyzed and discussed. In Section 4, a confirmation test was suggested to validate the benefits of the proposed method. Section 5 presents the conclusion.