esa-sceva/satcom-chunk-collection huggingface dataset:
# Skin Cancer Classification Using Fine-Tuned Transfer Learning of DENSENET-121
## 3 Materials and Methods
### Model Flow Chart
#### 3.3.7 Fine-Tuned Inception v3  
Inception v3 explores methods for convolutional network scaling that prioritize computational effectiveness. The emphasis is on investigating methods like aggressive regularization and factorized convolutions to maximize the effective use of new computer resources. The goal is to balance enhanced quality and computational efficiency, particularly in applications like mobile vision and big data scenarios.  
By employing the proposed strategies, remarkable results are achieved in terms of error rates. For single-frame evaluation, the network achieves top-1 and top-5 error rates of 21.2% and 5.6%, respectively, while utilizing a computational cost of 5 billion multiply-adds per inference and maintaining a parameter count of fewer than 25 million. Additionally, leveraging ensemble learning and employing multicrop evaluation further enhances performance, resulting in impressive top-5 error rates of 3.5% on the validation set (3.6% on the test set) and a top-1 error rate of 17.3% on the validation set [34].  
It was then fine-tuned by adding one flatten layer and three dense layers. The first dense layer has 32 neurons with a LeakyReLU activation function, while the second dense layer has 16 neurons with a LeakyReLU activation function, and the output dense layer has one neuron with an activation function of sigmoid, as shown in Table 8. The pretrained Inception-v3 extracts features from the input image and transfers them to the layers to classify the skin lesion as either Benign or Malignant. The model parameters are shown in Table 8 below.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Layer** & **Output Shape** & **Parameters Trained** \\ \hline DenseNet-121 & (None, 7, 7, 1024) & 7,037,504 \\ Flatten & (None, 50,176) & 0 \\ Dense\_1 & (None, 32) & 1,605,664 \\ LeakyReLU\_1 & (None, 32) & 0 \\ Dense\_2 & (None, 16) & 528 \\ Dense\_3 & (None, 1) & 17 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Parameters of fine-tuned DenseNet-121.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Layer** & **Output Shape** & **Parameters Trained** \\ \hline Inception\_v3 & (None, 5, 5, 2048) & 21,802,784 \\ Flatten & (None, 51,200) & 0 \\ Dense\_1 & (None, 32) & 1,638,432 \\ LeakyReLU\_1 & (None, 32) & 0 \\ Dense\_2 & (None, 16) & 528 \\ LeakyReLU\_2 & (None, 16) & 0 \\ Dense\_3 & (None, 1) & 17 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Parameters of fine-tuned Inception-v3.  
The fine-tuned Inception-v3 model underwent training for 20 epochs using the Adam optimizer with a batch size of 64. The activation function employed for the output dense layer was sigmoid, and binary cross-entropy served as the designated loss function.
## 3 Angular Protection Methods
### Angular Protection with Distance Protection  
In our previous work, we proposed a coexistence scheme for distance protection and defined the exclusion and restriction zones. However, this solution does not consider the influence of angles on the coexistence of frequency bands. Therefore, we integrated the angle protection scheme proposed in this work based on the original distance protection scheme. Further, based on the exclusion and restriction zones, the significance of the angle protection scheme for the coexistence of frequency bands was analyzed. The following assumptions were made in this section:  
* The ES location is fixed, whereas the distance between the BS and ES is 15 km;
* The BS moves in a circle around the ES (i.e., the azimuth angle of the BS regarding the ES (\(\varphi\)));
* Both the BS-ES angle and BS-ES distance change with the BS (\(x,y\));
* The steering beam of the antenna array (\(\varphi_{s}\)) varies between \(-180^{\circ}\) and \(180^{\circ}\).  
Figure 10 illustrates the (a) angular protection and (b) flux gram of the combination of the exclusion and restriction zones. Both the BS-ES angle and BS-ES distance change with (\(x,y\)). Therefore, the beam shut-off angle \(\varphi_{off}\) also changes with the BS coordinates, as depicted in Figure 8. When calculating the result \(\varphi_{off}(x,y)\) of the beam shut-off angle \(\varphi_{off}\) and every BS location (\(x,y\)), the relationship between \(\varphi_{off}(x,y)\) and the beam shut-off range was analyzed. Only \(\varphi_{off}(x,y)\) is less than or equal to the beam shut-off range; thus, the BS can be deployed at the current location. Otherwise, it cannot be deployed. A predefined beam shut-off range exists (i.e., a threshold for the beam shut-off angle (\(\varphi_{off}\))).  
This threshold represents how much beam angle we are willing to sacrifice, whereas a higher value corresponds to a higher level of angular protection. The four predefined beam shut-off ranges (45\({}^{\circ}\), 90\({}^{\circ}\), 180\({}^{\circ}\) and 270\({}^{\circ}\)) were investigated to demonstrate the tradeoff between the BS coverage and the coexistence of ES protection. Although the angle of 270\({}^{\circ}\) was turned off, it was also included in the investigation to illustrate the upper limit of angular protection. More precisely, sacrificing 75% of coverage of the BS makes this range not worth deploying. The derivation of the restriction zone under different beam shut-off ranges leads to the same analysis conclusions.  
Figure 9: Beam shut-off angle vs. BS-ES angle (ES antenna elevation angle = \(-180^{\circ}\) to \(180^{\circ}\), \(d\) = 15 km, 4 \(\times\) 4 antenna array).
# Effects of Aerosols on Gross Primary Production from Ecosystems to the Globe
## 4 Discussion
### Limitations and Future Needs  
There are still some uncertainties in our quantitative study of the influence of aerosol on GPP. First, the current flux tower sites measure less diffuse PAR when conducting carbon and water flux observations [29; 36]. Although most process-based models can now differentiate between sunlit and shaded leaves [21; 36; 70], field measurements are still necessary in order to parameterize the effects of diffuse PAR on LUE and photosynthesis. However, most of the diffuse radiation data used nowadays are mainly derived from experience or numerical models [67; 71]. Therefore, the lack of field-based diffuse PAR measurements can lead to uncertainty in simulated effects of scattered radiation on GPP by numerical models.  
Second, the indirect effects of aerosols on meteorological changes (i.e., precipitation) can also affect GPP. [PERSON] et al. [63] found that aerosol-induced drought strongly reduced NPP, which may be due to aerosol-induced changes in evaporation and precipitation [22; 72]. In addition, the indirect effects of aerosols can also change the size and distribution of cloud droplets and cloud albedo [32; 73] and increase the depth and number of clouds. Clouds can also disturb the scattered radiation, surface temperature and precipitation and thus have complex effects on the terrestrial carbon cycle. Because the relationship between aerosols and clouds is so complicated, current research cannot completely separate them [74; 75]. To resolve these meteorological feedbacks and accompanying mechanisms, future efforts are needed to fully couple the terrestrial biosphere, atmosphere chemistry and climate in earth system models [63].  
Third, the input data also affect the simulation of the impact of aerosols on GPP. The effects of spatial resolution and uncertainty of remote sensing data on carbon fluxes cannot be ignored [76; 77]. Some uncertainties in the simulation of the AOD-LUE model may be caused by the scale mismatch between the input data sets. The spatial resolution of the AOD data used in this article was 0.5\({}^{\circ}\)\(\times\) 0.625\({}^{\circ}\), while the spatial resolution of other simulated flux tower datasets was generally less than 0.03\({}^{\circ}\)\(\times\) 0.03\({}^{\circ}\). This kind of uncertainty in GPP simulation caused by data scale mismatch is inevitable [1; 53]. For example, researchers [PERSON] et al. [35] found that spatial PAR data explained 57% of the PAR variation detected at EC tower sites [78; 79]. To achieve a global level of spatial resolution for GPP modeling on a large scale, the GPP model research center still must improve the quality of remote sensing data.
## 2 Economy-Quality Bi-Objective Optimization Model
### Objectives  
#### 2.2.1 Objective 1: To Save Economic Cost  
EC of ME-VPP comprises two parts: energy-purchasing cost \(\Omega_{\rm P}\) and environmental governance cost \(\Omega_{\rm E}\).  
\[\textit{Minimize}\ F_{1}=\Omega_{\rm P}+\Omega_{\rm E}, \tag{1}\]  
\(\bullet\) Energy-purchasing Cost \(\Omega_{\rm P}\)  
\(\Omega_{\rm P}\) includes cost of fuel consumption of CCHPs and cost of electricity purchased from main grid in case of undersupply.  
\[\Omega_{\rm P}=\sum_{t=1}^{T}\left\{\sum_{i=1}^{N_{\rm c}}\left[f_{\rm c}(P_{ \rm c}^{i}(t))\cdot\lambda_{\rm c}\right]+P_{\rm g}(t)\cdot\lambda_{\rm g}(t) \right\}, \tag{2}\]  
Assume the gas (fuel of CCHPs) price \(\lambda_{\rm c}\) is constant, \(\lambda_{\rm g}\) is the electricity price purchasing from the main grid that varies with time and \(f_{\rm c}\) is the quadratic function that calculates the cost of fuel consumption of CCHPs.  
\[f_{\rm c}(P_{\rm c}^{i})=\alpha_{2}^{i}\cdot\left(P_{\rm c}^{i}\right)^{2}+ \alpha_{1}^{i}\cdot P_{\rm c}^{i}+\alpha_{0}^{i}, \tag{3}\]  
where \(\alpha_{2},\alpha_{1},\alpha_{0}\) are all constant fuel cost factor of each CCHP.  
\(\bullet\) Environmental Governance Cost \(\Omega_{\rm E}\)  
\(\Omega_{\rm E}\) reflects the cost of governing pollutant emissions of ME-VPP, which consists of three parts: carbon dioxide (CO\({}_{2}\)), sulfur dioxide (SO\({}_{2}\)) and nitrogen oxides (NO\({}_{\rm X}\)).  
\[\Omega_{\rm E}=\sum_{t=1}^{T}\left\{\sum_{i=1}^{N_{\rm c}}\left[P_{\rm c}^{i}( t)\cdot\mu_{\rm c}\right]+\sum_{j=1}^{N_{\rm s}}\left[P_{\rm s}^{j}(t)\cdot\mu_{ \rm s}\right]+\sum_{k=1}^{N_{\rm g}}\left[P_{\rm g}^{k}(t)\cdot\mu_{\rm g} \right]\right\}, \tag{4}\]  
where \(\mu_{\rm c}\), \(\mu_{\rm s}\) and \(\mu_{\rm g}\) denote the pollutant emissions' governance tariffs (unit: \(\rm{CNY}/\rm{kW}\cdot\rm{h}\)) of CCHPs, storage devices and the main grid, respectively. And, let \(P_{\rm g}(t)=0\) if \(P_{\rm g}(t)\) is negative.
# Investigations on Interface Shear Fatigue of Semi-Precast Slabs with Lattice Girders
## 4 Fatigue Tests on Semi-Precast Slabs with Lattice Girders
### Description of Test Specimens  
For the test specimens, the precast slabs were manufactured with a slab thickness of \(h_{\rm pre}\) = 7 cm and a width of \(b\) = 85 cm. To extend the application of the limited diameter of the longitudinal (l) reinforcement in the technical approvals of \(\bar{Q}_{1}\leq 16\) mm and to exclude flexural failure, longitudinal reinforcement bars with \(\bar{Q}_{1}\) = 20 mm and a yield strength of \(f_{\rm y}\) = 900 N/mm\({}^{2}\) with a longitudinal reinforcement ratio of \(\rho_{\rm l}\) = 2.0% were applied. The lower chords of the lattice girders have not been considered for determination of longitudinal reinforcement ratio.  
The investigated shear lattice girders were KTS and EQ with a girder height of 10 cm for the 16 cm slabs and 30 cm for the 36 cm slabs. Both girders, KTS and EQ, have identical structural properties. For Specimen EG14, the concrete cover in the compression zone was reduced and thus the slab thickness was decreased to 33 cm. The span of the slabs for the four-point bending tests was defined to 3-\(a\), with \(a\) being the distance between load application and support, to achieve a shear slenderness of \(a/d\) = 4.0. The geometrical parameters of the test series are summarized in Table 3.
# _electronics_
## 3 Coded Packages Naming Lookup and Cache Placement
### Quantity Decisions for Coded Packages  
In this paper, we consider generating different numbers of coded blocks for content with different popularity types for storage. The more popular content generates more coded blocks, and the less popular content generates fewer coded blocks; the number of cached coded blocks for the content is proportional to its request probability.  
\[O_{i}\ =\ p_{i}\frac{C}{m_{\max}}k \tag{4}\]  
and  
\[p_{i}=\frac{1/\hat{r}^{\omega}}{\sum_{j=1}^{N}1/\hat{r}^{\omega}} \tag{5}\]  
where \(O_{i}\) represents the number of coded blocks of the \(i\)th content, \(p_{i}\) represents the request probability of the content, \(C\) is the storage space size of the satellite node, and \(m_{max}\) is the maximum content size.  
For a larger \(p_{i}\), the obtained \(O_{i}\) may be far greater than \(k\), resulting in serious cache redundancy. Therefore, to balance the amount of cached content, the number of coded packages is adjusted based on matching the number of coded packages, and based on the request probability above. The specific algorithm is shown in Algorithm 1.  
```
1: initialize the number of data packages \(k\), content request probability \(p_{i}\) and number of content \(N\)
2:for\(i\gets 1\) to \(N\)do
3:if\(p_{i}\frac{C}{m_{\max}}k\leq k\)then
4:\(O_{i}=p_{i}\frac{C}{m_{\max}}k\)
5:else
6:\(O_{i}=k+\frac{k}{O_{1}-k}(p_{i}\frac{C}{m_{\max}}k-k)\)
7:endif
8:endfor
9: the number of coded packages \(O_{i}\)
```  
**Algorithm 1** Chart of the coded package number decision algorithm based on request probability matching.  
If \(O_{i}<k\), it means that the content is not very popular; we then keep the results of the number of coded packages; if \(O_{i}>k\), we map the number \(k-O_{i}\) of coded packages to the interval of \(k-2k\), and round up the final result. Since the content is divided into \(k\) data packages, every \(k\)-coded package stored by the satellite node is equivalent to storing a copy in the non-coded cache. We cache the same cache benefit, so the upper limit of the number of coded packages is \(2k\).
# Calculation of Nonlimit Active Earth Pressure against Rigid Retaining Wall Rotating about Base
## 2 Basic Assumptions  
In this paper, as shown in Figure 1, the soil behind the wall was divided into thin layers on the basis of the principal stress traces, and the force analysis of the thin units was conducted by leveraging the concept of static equilibrium. The principal behind this method is the theoretical calculation of the differential cells. The following assumptions were made for the theoretical investigation:  
1. The soil slip surface is considered a plane slip surface going through the heel of the wall. If the sliding surface is not a plane passing through the wall heel, the backfill cannot be regarded as a triangular wedge when calculating the earth pressure.
2. The soil behind the wall is noncohesive with a soil friction angle of \(\varphi\).
3. The back of the retaining wall is rough, and the interface friction angle between the wall and the soil is \(\delta\). Notably, without special explanation, \(\delta=2/3\ \varphi\). If the wall back is assumed to be smooth, then the distribution of earth pressure is linear, which is inconsistent with the measured value [19].
4. When the soil behind the wall is in a nonlimit state, developed values are employed by the soil's and the interface friction angle. Otherwise, when the fill is in the active limit state, the soil's and interface friction angle take fully developed values.  
There is no surcharge effect, and the fill surface is level (q = 0). The obvious law can be obtained that the increase in overload causes the increasing earth pressure. Therefore, the surcharge effect is not considered in the simplification of the model.
6. The rigid retaining wall rotates outward about the base, leaving no room between the soil mass and the wall.
7. Backfill is regarded as an isotropic material, because [PERSON]'s research [18] indicates that soil inherent anisotropy has less effect on active earth stress.
# Advances in the IoT and Smart Cities
###### Abstract  
The proliferation of the Internet of Things (IoT) and the rise of smart cities are revolutionizing how societies operate and urban ecosystems are managed. This Special Issue of Applied Sciences, \"Advances in the IoT and Smart Cities\", brings together contributions that explore the dynamic intersection of data-rich technologies, real-world applications, and emerging challenges in urban environments. IoT technologies are at the heart of smart city development, enabling automated and real-time decision-making for infrastructure, energy, mobility, and governance. However, as cities strive toward intelligence and sustainability, they encounter critical issues related to interoperability, privacy, performance, and system integration.  
The aim of this Special Issue is to offer insights into the recent scientific and technological advances supporting the next generation of smart and connected urban infrastructures. These contributions demonstrate how innovations in data management, edge computing, behavior-driven development, and secure communication protocols are shaping the future of IoT in smart city applications. More importantly, the topics addressed resonate with global efforts to foster open smart city platforms and adopt frameworks that support data sovereignty and platform interoperability.
## 6 Conclusions and Future Developments
It is expected that more analytic methods perform better for slight discontinuities between the layers (because analytical models are more mathematically rigorous than the method exploited here), but strong discontinuities can be problematic (because analytical models are approximated). In particular, it is certainly rare to find analytic focusing methods applied to a layered medium with strong discontinuities of the permittivity between two adjacent layers. Moreover, we have not found a combined time-depth conversion or something similar applied for achieving the final image. This is an important point because, regardless of which focusing algorithm one exploits, if the layers show a different propagation velocity of waves, the spatial depth formally considered is not the same vs. the abscissa, and this should be accounted for in order to achieve a more correct time-depth conversion. These considerations are also valid for the case of soil in which the characteristics change more smoothly (e.g., because of some moisture gradient), producing a continuous variation in the permittivity either along the horizontal or vertical directions. In these cases, the focusing of targets will likely be better accommodated with time reverse migration, but the issue of correct time-depth conversion remains, and a suitable combined time-depth conversion is the tool that can address this problem properly. Conceptualization, R.P. ; methodology, I.C., G.E., L.C. and G.D.M. ; software, D.Y. ; validation, D.Y. and R.P. ; formal analysis, R.P., I.C., L.C., G.D.M., G.M. and D.Y. ; investigation, I.C., L.C., G.D.M. and G.M. ; resources, G.M. ; data curation, D.Y. ; writing--original draft preparation, R.P. ; writing--review and editing, I.C., G.E. and D.Y. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The raw data supporting the conclusions of this article will be made available by the authors on request. Author [PERSON] was employed by the company Geostudi Astier s.r.l. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Materials and Methods
### Classification Methods
#### 2.2.3 Random Forest  
Random Forest (RF) [8] is a powerful ensemble learning technique that combines multiple randomized decision trees to make predictions based on a given set of input data. This method is renowned for its accuracy in building predictive models and assessing the significance of individual features. Additionally, RF is highly effective in managing large datasets and handling biased data, demonstrating its versatility across various machine learning applications.
## 4 Experiment Results and Discussion
### Attitude Stabilization of Hover Mode  
Attitude stabilization of the hover mode is essential for the NQTAUV, and there is a special case when the target attitude is \(\xi_{d}=[0,0,0]^{T}\). To verify the disturbance-rejection effect of the hover mode, we toggled the LESO off and on with the same controller. The LESO being off means the observer states are reset to 0. The LESO takes 0\(\sim\)30 s to turn off, and 30\(\sim\)70 s to turn on. Figure 5 shows attitude \(\xi\) of the vehicle, and Figure 6 shows control input \(\tau\). Figure 7 shows the estimation of disturbance \(d_{e}\).  
From Figure 5, we can see that the disturbances lead to an attitude-stabilization error of 0.02 of the \(x\) and \(y\) axes. When the LESO is on, the disturbance is estimated within 3 s, and with this compensation, the attitude-stabilization error is eliminated. From Figure 7, we can see the estimation of the disturbance is stable and near the true value of the added torque. However, from Figure 6, we can see that control inputs become more violent when the LESO is on.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Value** & **Unit (SI)** \\ \hline \(l_{1}\) & 0.11 & m \\ \(l_{2}\) & 0.09 & m \\ \(m\) & 1.5 & kg \\ \(B\) & 3.15 & N \\ \(l_{xx}\) & 0.006 & kg/m\({}^{2}\) \\ \(l_{yy}\) & 0.006 & kg/m\({}^{2}\) \\ \(l_{zz}\) & 0.012 & kg/m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical parameters of the NQTAUV.  
Figure 4: Testbed in the water tank.  
Figure 5: Attitude-stabilization effect with the LESO off and on.  
Figure 6: Control torque with the LESO off and on using attitude stabilization.
## 3 Results and Discussion
### Performance of the Header and Flow Characteristics in the Orifices
As a result, the liquid film above the orifice-baffle is unable to resist the liquid impact and meanwhile the interactions between the liquid and vapor becomes intensive, leading to the increasing of \(\dot{m}_{\text{V,O}}\). As for \(O_{1}\) and \(O_{3}\) that are closer to the liquid impact region, they are more likely to have a higher vapor velocity. As shown in Figure 6b, the velocity of the vapor in \(O_{1}\) and \(O_{3}\) can be up to 3 m/s as \(\dot{m}_{\text{in}}=9\) g/s. However, with more liquid accumulated on the orifice-baffle (Figure 6c,d), the effect of the fluid impact on these two orifices weakens, with only a little vapor leakage out from the orifices. Further increasing \(\dot{m}_{\text{in}}\) (Figure 6e,f), the liquid impact is enhanced again, contributing to a higher vapor velocity in \(O_{1}\) and \(O_{3}\). Summarily, the effects of the liquid impact on the orifice are the competitions between the liquid film on the orifice-baffle and \(\dot{m}_{\text{in}}\). At the lower \(\dot{m}_{\text{in}}\), the liquid film is too thin to resist the liquid impact and, consequently, the vapor escapes from the orifices, whereas at the higher \(\dot{m}_{\text{in}}\), the interactions of the liquid and vapor are upgraded and the vapor is carried to the orifices by the liquid. Figure 5: Mass flow rate of (**a**) liquid; (**b**) vapor at each orifice.
## 5 Method Comparison Validation and Parameter Analysis
### Impact of Sepage on \(\sigma_{t}/\gamma_{sat}D\)  
Seepage in the tunnel face is mainly influenced by water level and tunnel face water pressure. Therefore, these two parameters were selected for analysis with specific parameter settings as follows: \(c=0\) kPa, \(\varphi=15\)-\(35^{\circ}\), \(D=10\) m, \(C=30\) m, \(\gamma_{sat}=20\) kN/m\({}^{3}\), \(h_{w}/D=1\)-\(3\), \(\eta=0\)-\(1\).  
Figures 12a and 12b, respectively, illustrate the variations of \(\sigma_{t}/\gamma_{sat}D\) with \(h_{w}/D\) under different \(\varphi\) values and with \(\eta\) under different \(h_{w}/D\) values. It can be observed that \(\sigma_{t}/\gamma_{sat}D\) shows a linear increase with increasing \(h_{w}/D\) and \(\eta\). As \(\varphi\) increases, the magnitude of \(\sigma_{t}/\gamma_{sat}D\) increase diminishes with increasing \(h_{w}/D\). Additionally, there is an interaction between \(h_{w}/D\) and \(\eta\), intensifying the increasing trend of \(\sigma_{t}/\gamma_{sat}D\). This indicates that smaller \(\varphi\) values and larger \(h_{w}/D\) and \(\eta\) lead to higher \(\sigma_{t}/\gamma_{sat}D\).