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
#### 3.3.5 Fine-Tuned ResNet-34  
ResNet, a revolutionary architecture, was formulated by [PERSON], [PERSON], [PERSON], and [PERSON] from Microsoft Research. It introduces a residual learning framework that facilitates training deeper neural networks.  
The ResNet architecture consists of multiple layers, typically divided into several blocks. Each block contains a set of convolutional layers followed by batch normalization and ReLU activation. The key innovation of ResNet lies in the introduction of residual connections or skip connections. These connections allow the network to learn residual functions by adding the original input of a layer to its output. This way, the network can focus on learning the residual mapping, making it easier to train deeper models.  
The basic building block of ResNet is the residual block, which includes two convolutional layers with smaller filter sizes (usually \(3\times 3\)) and the skip connection. In addition to the residual blocks, ResNet also incorporates pooling layers, fully connected layers, and a softmax layer for classification.  
By leveraging the residual connections, ResNet models can achieve remarkable depths, with variants ranging from 18 layers to over 150 layers. The specific configuration used in the original ResNet paper is ResNet-152, which consists of 152 layers [30]. 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. The pretrained ResNet-34 extracted features from the input image and transferred them to the layers to classify the skin lesion as either benign or malignant. The model parameters are shown in Table 6.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Layer** & **Output Shape** & **Parameters Trained** \\ \hline VGG16 & (None, 7, 7, 512) & 14,714,688 \\ Flatten & (None, 25,088) & 0 \\ Dense\_1 & (None, 32) & 802,848 \\ LeakyReLU\_1 & (None, 32) & 0 \\ Dense\_2 & (None, 16) & 528 \\ LeakyReLU\_2 & (None, 16) & 0 \\ Dense\_3 & (None, 1) & 17 \\ \hline
**Total Parameters** & & 15,518,081 \\
**Trainable Parameters** & & 803,393 \\
**Nontrainable Parameters** & & 14,714,688 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameters of fine-tuned VGG16.  
The fine-tuned ResNet-34 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.
# Nano Aerial Vehicles for Tree Pollination
## 2 Related Works
### Nano Aerial Vehicles
#### 2.2.1 Limitations  
NAVs must have a weight of 3 to 50 g and a wingspan of 2.5 to 15 centimetres. This is due to the weight and size restrictions imposed. In the publications reviewed, the following limitations are highlighted:  
* Poor flight endurance [26];
* Highly susceptible to aerodynamic effects (air currents, drift, and turbulence) [26];
* Asymmetries and inconsistencies, which can affect the performance [26];  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Communication** & **Localisation** &
\begin{tabular}{c} **Obstacle** \\ **Avoidance** \\ \end{tabular} & **Applications** \\ \hline
[26] & & & & \\
[27] & ✓ & ✓ & ✓ & ✓ \\
[28] & ✓ & ✓ & ✓ & ✓ \\
[29] & ✓ & & ✓ & \\
[30] & ✓ & ✓ & ✓ & ✓ \\
[31] & ✓ & ✓ & ✓ & ✓ \\
[32] & ✓ & & & \\
[33] & & & ✓ & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Aggregation of research results.  
Fragile and safety-critical systems [29].  
The reduced flight time is due to the battery's weight and size, which is how most electronics are powered. To overcome this limitation, the article \"Self-Sustainability in Nano Unmanned Aerial Vehicles: A Blimp Case Study\" [32] developed a nano blimp prototype with a 55 g payload. A helium balloon and a solar panel were added to the Crazyflie 2.0, allowing it to have a flight time of up to 100 h under normal lighting conditions. On the other hand, in the paper \"Tensile Web Construction and Perching with Nano Aerial Vehicles\" [26], a method was implemented to mitigate flight time limitations by building a structure within the environment where the NAV can perch, thereby conserving energy.  
NAVs are fragile safety-critical systems that can overcome these barriers using Artificial Intelligence (AI) through neural networks. Real and simulated data are combined in the developed algorithms. This approach requires the addition of a monocular [29] or pulp-shield camera (which has an ultra-low-power grey-scale QVGA camera) [33] to a NAV.  
Determining the hardware is a crucial step in NAV development. The selection must take into account the intended functionalities. The importance of the selection is due to the weight restrictions and all the related limitations.
## 2 Peculiarities of ATR Measurements
### A Useful Condition When Atr Becomes Invalid
#### 2.2.1 Example: Water in Butter around Water Freezing Conditions  
The THz-ATR technique with a top-reflecting gold mirror is used to measure the absorbance of bio-materials [15]. At the chosen \(\sim\)0.7-2.2 THz synchrotron radiation band,  
Figure 2: (**a,b**) Photo and schematics of the beam path in ATR unit. Three reflections are encountered from the IN-port to the sample (mirrors m1–3) and extra six for the path sample-to-OUT port (m5–10). (Image: courtesy Dr. [PERSON]) (**c**) Schematics of measurements with top reflecting mirror when TIR conditions are not fulfilled due to the high refractive index \(n_{2}\) of the sample. Plot shows the evanescent field ratio for the p-pol. (TM) and s-pol. (TE) \(d_{p}/d_{s}\) (Equation (3)) vs. the angle of incidence \(\theta_{i}\). For the non-polarised illumination, the penetration depth of evanescent field is \(d_{eq}=\frac{d_{+}d_{p}}{2}\). Critical angle is at \(\theta_{c}=\arcsin\left(\frac{n_{2}}{n_{1}}\right)\).  
polarisation is mostly linear and corresponds to the s-pol. at the sample-prism interface. Freezing water reduces its real part of the refractive index only slightly (\(n\) is proportional to mass density \(\rho\)); however, a very strong reduction in absorption occurs. Experimental values of ice reflectivity [17; 18; 19] show a significant increase at the 56.5-75.0 cm\({}^{-1}\) window (1.70-2.25 THz). Up to three times difference between the reflectivity of water (\(\sim\)4%) and ice (\(R>12\%\)) to that of air is observed [17; 18; 19].  
Butter (Lurpak, a supermarket brand) has 14.7% \(w/w\) water, 3% \(w/w\) non-fatty solids, which are dissolved in the water, and the rest is fat [20]. The temperature is varied from \(-20\) to 24 \({}^{\circ}\)C over a total time frame of 24 min with continual scanning; a total of 140 sets of 40 averaged scans are collected during that time span. Figure 3 shows the reflectance evolution with temperature at two spectral regions. The inset presents reflectivity changes with temperature. There is a featureless temperature dependent reduction variation with butter, which is evident at the most sensitive frequency range of 0.95 to 1.0 THz. A considerably stronger temperature dependence of reflectivity is observed at the 2 THz band. Overall, the reflected ATR signal for Lurpak butter shows a 13% decrease over the \(-20\) \({}^{\circ}\)C to +24 \({}^{\circ}\)C change at 1.0 THz and a 3.5% decrease at 2 THz. A slight decrease in reflectance with temperature variation \(\pm\)20\({}^{\circ}\)C around water freezing indicates that the water content \(\sim\)14.7% needs to be regarded as being \"bound\" in a homogenous mixture of fat and protein. The temperature-related reduction in reflectance in the region of 2.0 THz shows a plateau in the 2-12 \({}^{\circ}\)C range. These results suggest that other materials with bound water may not show the effect of water freezing.
## 5 Discussion  
### Surface Velocity Changes before and after the Earthquake  
The glacier flow velocity monitoring images before and after the earthquake in the local area of the Mt. Gongga glaciers were compared to check whether the earthquake caused changes in the glacier surface geomorphology. As shown in Figure 9, there are significantly more locations with accelerated flow velocities after the earthquake in the same metric. The movement of loose moraine material on the surface of the main peak of Mt. Gongga and the ice waterfall of Hailuogou accelerated. The earthquake is thought to have destabilized the lateral slopes of the Hailuogou Glacier, and no new damage was found on either side of the ice tongue. Many previous studies have shown that, in most cases, earthquakes are only a prerequisite for paraglacial slope damage.
# A Coal Mine Excavation Tunnels Modeling Method Based on Point Clouds
## 5 Discussion
### Application of the Algorithm  
The tunnel mesh model can be used to create realistic tunnel textures, resulting in an instantiated model with a high degree of fidelity to the real scene, as shown in Figure 22. The model includes tunnel support structures, ventilation ducts, electrical wires, conveyor belts, lighting signs, and scattered coal blocks in the tunnel. Among these, equipment such as the conveyor belt and electrical wires are positioned by calculating their 3D spatial coordinates from the point cloud data, and complete 3D models of the equipment, constructed in 3 DMax based on their blueprints, are placed within the tunnel scene. The road surface in the tunnel is textured with a dirt material, while the roof and side walls are textured with coal seams. Additionally, tunnel support equipment, such as anchor nets, is added to the side walls and roof, aligning with the 3D spatial structure of the tunnel model. This enhances the level of detail in the model and accurately restores the 3D spatial scene of the tunnel, making it suitable for applications such as coal mine digital twins and coal mine intelligent control platforms.  
Although this study focuses on coal mine tunnels, the proposed method exhibits significant potential for application in other industries. For instance, it can be effectively utilized in the modeling of transportation tunnels, such as those used in subway, railway, and highway systems, where accurate 3D models are essential for monitoring structural integrity and safety. Similarly, the method is applicable to underground utility tunnels and pipelines, which often face similar challenges with occlusion and irregular geometries. Additionally, the technique can be adapted for geological surveys, particularly in cave exploration and natural tunnel mapping, where complex and unpredictable formations require precise modeling. The versatility of the point cloud-based approach, especially in handling occlusions and optimizing geometry, makes it suitable for a wide range of tunnel-related applications beyond the coal mining industry.
# A Survey: Future Smart Cities Based on Advance Control of Unmanned Aerial Vehicles (UAVs)
## 2 Important Applications of UAVs in Smart Cities
### Scientific Research  
Drones are used in various scientific research fields, including meteorology, geology, and archaeology. They can collect data in hazardous or inaccessible areas, making them a valuable tool for researchers [28].
# A Tri-Satellite Interference Source Localization Method for Eliminating Mirrored Location
## 5 Results
### Experiment on the Influence Factors of the Multi-Moment Localization  
The performance of multi-moment interference source localization is mainly related to the number of selected moments and the total length of time. The following will explore its change relationship through simulation experiments. The location of the interference source is 125\({}^{\circ}\) E, 30\({}^{\circ}\) N, and the TDOA measurement root mean square error is 0.5 \(\upmu\)s. First, the total time is simulated for 20 min, and the relationship between multi-moment location method and the location error of the proposed method varies with the number of moments as shown in Figure 4a. It can be found that the performance of the multi-moment localization improves with the increase of the number of times, and continues to approach the proposed method. The location error of the proposed method is basically unchanged. Then, the relationship between multi-moment localization and the location error of the proposed method with the total time is simulated when the number of the moments is 10 as shown in Figure 4b. It can be found that the performance of the multi-time localization improves with the increase of the total time, and it continues to approach the proposed method. The location error of the proposed method also remains stable and can achieve high-precision and rapid localization.
## 5 Theoretical Analysis  
In this section, the theoretical analysis is provided to prove the efficiency of the TS strategy. Firstly, we derive the condition to keep the connectivity of the trail. Then we evaluate the performance of TS strategy in terms of search delay and energy consumption, and compare it with that of ERS.  
Without loss of generality, we assume that the sensor nodes are uniformly deployed that partition the network area into grids. Each grid is a \(L\times L\) square, and the nodes are deployed at the corners of the grids. The transmission range of both the sensor nodes and the actor are limited to their adjacent grids (excluding diagonal grids), constrained by \(R\in(\sqrt{2}L,2L)\). An example of a network is shown in Figure 5.  
In the sensor network, the nodes always use duty cycle to reduce the energy consumption which results in non-trivial transmission latency. To clarify its impact on the analysis, in this section, we use the _waiting time_ of packet \(\tau_{P}\) and that of actor \(\tau_{A}\) to formulate the transmission latency and actor mobility [20]. The \(\tau_{P}\) is defined as the expected number of time slots required for one hop data transmission, and the \(\tau_{A}\) is defined as the expected number of time slots required for the actor moves from one grid to any adjacent grid. The value of \(\tau_{P}\) depends on the specific MAC and PHY protocol used in the sensor nodes, while the value of \(\tau_{A}\) is determined by the node density and the velocity of actor's movement.  
The variables used in the analysis are summarized in Table 1.  
Figure 4: An example of data transmission in the redundancy reduction scheme based on TS (TS-R).
## 3 Results and Discussion
### Assessment of Soil Contamination  
The evaluation of soil contamination levels involved the use of pollution indices that facilitate comparisons between the degree of assessed soil contamination and the natural background incidence of identified metals. The _Igeo_ was estimated using the normal values specified in Romanian Order no. 756/1997 [60] (Table 4) as the geochemical background [1]. The results obtained for _Igeo_ are presented in Table 6.  
\begin{table}
\begin{tabular}{  
According to the values obtained for _Igeo_, all the soil samples were considered to be in Class 0--unpolluted with Cd, Co, Pb, Zn, and Cu (except sampling sites T-1a, T-1b, V-3a); in Class 1--unpolluted to moderate pollution with Ni (all the samples), Cr (sites T-2b), and Cu (sites T-1a, T-1b, V-2b, V-3a); Class 2--moderate polluted with Cr (sites T-1a, S-1a, S-1b, S-2a, S-2b, V-2a, V-2b); and Class 3--moderate to high polluted with Cr (sites T-1a, S-1a, S-1b, S-2a, S-2b, V-2a, V-2b, V-3a, V-3b). To evaluate the impact of anthropogenic activities, the Enrichment Factor (EF\({}_{i}\)) was computed utilizing aluminum (Al) as a normalizer element, which is found in abundant quantities in the geological background [1]. Considering the values for Al in European topsoil [63], EF indicates Cd, Co, Pb (except site T-1b), Zn (sites T-3a, T-3b, T-5a, T-5b, T-6a, T-6b, S-1a, S-1b, S-2a, S-2b), deficiency to minimal enrichment (Class 1); Cu (except sites T-1b, T-6b), Ni (sites T-1a, T-1b, T-2b, T-5b, T-6a, T-6b, S-1b, V-1a, V-2a, V-2b, V-3a, V-3b), Zn (sites T-1a, T-1b, T-2a, T-2b, T-4a, T-4b, V-1a, V-1b, V-2a, V-2b, V-3a) moderate enrichment (Class 2); Cr (except site T-1a, V-2a, V-2b, V-3a, V-3b), Cu (sites T-1a, T-6b), Ni (sites T-2a, T-3a, T-3b, T-4a, T-4b, T-5a, S-1b, V-1a, V-2a, V-2b) significant enrichment (Class 3); and Cr (site T-1a, V-2a, V-2b) high enrichment (Class 4). Based on EF\({}_{i}\) quantification, Table 7 displays the distribution of soil metal enrichment due to human activity. Upon analyzing the EF values derived from the concentration of Cr, Cu, Ni, and Zn found in the uppermost layers of agricultural soils, it becomes apparent that the origins of these elements are multifaceted, encompassing both past and present factors. The extended duration of land cultivation and the industrial practices of the adjacent iron and steel plant are the primary contributors. According to PI\({}_{i}\) values (Table 8), soil is uncontaminated (Class 1) with Cd, Co, Cu (sites T-3b, T-4a, T-4b, T-5a, T-5b, S-1a, S-1b, S-2b), Pb, and Zn (sites T-2b, T-3a, T-3b, T-4a,
# Multi-Agent Modeling and Jamming-Aware Routing Protocols for Movable-Jammer-Affected WSNs
## 4 Results and Discussion
### Simulation Results and Analyze
#### 4.2.2 Simulation 2  
In the second simulation, we aimed to analyze the impact of global maintenance on the WSN's reliability and performance. We designed two cases for this experiment: with and without global maintenance. In the case of local maintenance, the sensor node can choose the node with the largest surrounding target value as the successor node. However, the network structure is affected by the jammer and local maintenance, causing changes in the structure over time, which deviates from the optimal path combination. To show this phenomenon, we designed a longer running time of 100 ticks for the simulation experiment investigating global maintenance.  
Comparing Figures 7a and 8a, we observed that in the absence of global maintenance, the value of APL fluctuates slightly, and the average value is roughly the same as that of global maintenance. Figure 9b also confirms this finding; the presence or absence of global maintenance did not appreciably impact APL in a substantively meaningful manner. Although the expected global maintenance was aimed at periodically resetting the depth of the sensor nodes to restore the efficient structure of the sensor node ad hoc network to the initial state, this effect did not materialize.  
In contrast, when comparing Figures 7b and 8b, it is evident that the value of MCS not only fluctuates more, but the average value is lower without global maintenance than  
Figure 8: (**a**) APL trend with global maintenance; (**b**) MCS trend with global maintenance.  
Figure 7: (**a**) APL trend without global maintenance; (**b**) MCS trend without global maintenance.  
Figure 9: (**a**) Comparison of MCS with or without a global maintenance mechanism; (**b**) Comparison of APL with or without a global maintenance mechanism.  
with global maintenance. Figure 9a also supports this claim. Global maintenance has a considerable impact on MCS. Due to the influence of moving jammers, sensor nodes update their selection of successor nodes multiple times, and eventually, many peripheral sensors choose some of the same sensor nodes as relay nodes. In other words, compared to the initialization network, the number of relay nodes responsible for data relay tasks is less. At this point, if the jammer moves and destroys the communication transmission function of these small number of relay nodes, it will result in a large number of peripheral nodes connected to the base station relying on these relay nodes to be disconnected, as shown in Figure 7b, and the value of MCS plummeted.  
From the results of simulation experiments, it can be concluded that the primary function of global maintenance is not to maintain the low latency of the network but to sustain the robustness of the network and avoid relying on a small number of relay nodes.
## 4 Experiments
### Ablation Study
Their structures are shown in Figure 5, and their experimental results are shown in Table 2. For (a) structure in Figure 5, the feature maps output from the previous MFFUs and the original feature map output from the backbone are added with weights and then input to the current MFFU. In this way, the deep structure can retain some original and lower-level features, but its experimental performance is not good, and its \(MR^{-2}\) is 4.12% when level = 3. For (b) structure in Figure 5, the output of all MFFUs before the last MFFU and the original feature map output by backbone are added in the form of weights and then output to the last MFFU for processing, but it may be that the last MFFU cannot process and fuse so much information well. This method is not effective, and its \(MR^{-2}\) is 4.35% when level = 3. For (c) structure in Figure 5, starting from the original feature map output by backbone, the weighted addition of the output of the current MFFU and the shortcut of the input of the current MFFU has the best effect, and its MR is 3.65% at level = 3, far better than that of (a) and (b). Among the three structures, (a) is the fastest and (c) is the slowest, but there is just slight difference in the overall speed. (a), (b) and (c) are 35.9\(FPS\), 35.1\(FPS\) and 32.3\(FPS\) respectively when level = 3. Experiments show that (c) structure of our MFFM has the best comprehensive performance and speed, and has great advantages. Figure 5: Different external structures of MFFUs in MFFM. (**a–c**) are three different structures.
## 7 Open Research Challenges in UWSNs
### Big Data-Related  
Integrating UWSNs and big data is one of the recent trends. However, the flexibility and scalability of a traditional UWSN is still dissatisfied because of its dependent ability on hardware infrastructure. In UWSN, big data processing encounters several challenges related to accuracy, real-time analytics, and visualization.  
1. Hardware Dependent Underwater sensor nodes such as autonomous underwater vehicles (AUVs), wheels, or unmanned aircraft consume energy from a battery and difficult to reconfigure after deployment. Hence, it is a challenge for another system to do customization due to different data format, protocol, and service constraint of varied applications.
2. Communication Underwater sensor nodes are commonly coming from different manufacturers. As a result, it makes them challenging to perform communication as cooperation among UWSN technologies is not flexible to each other.
3. Visualization The result analysis of the UWSN application is significant and understandable if there is a synchronization between visualization and data analytics. However, it is a challenge to establish a visual representation of highly heterogeneous big data.