esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing Mobile Vision Transformers for Land Cover Classification
## 5 Discussion
#### Overfitting and Model Complexity  
To ascertain if 500 epochs cause models to overfit, a benchmark test with a reduced training period of 200 epochs was conducted. In this test, the MViT model on the AID dataset with a 50-50 split showed a decrease in training accuracy from 99.46% to 98.54%, while the validation accuracy slightly improved from 85.82% to 86.06%. However, the MViT-Depth model on the same dataset and split saw a decrease in training accuracy from 99.46% to 99.37%, but the validation accuracy decreased from 87.44% to 82.90%, indicating that reduction in the number of epochs alone is not a universal solution for overfitting. The complex architectures of vision transformer models like MViT are a significant factor in this context. While their advanced self-attention mechanisms are adept at capturing intricate data patterns, this can sometimes lead to over-specialization on the training data, which does not always generalize to new, unseen data. Our 200-epoch benchmarks reveal that, while a reduced number of training epochs can lower the training accuracy, it does not automatically translate into higher test accuracy, emphasizing the need for a balanced approach in model training.  
Moving forward, our efforts will focus on tailoring the architecture of vision transformers to better suit the characteristics of the datasets they are trained on. We aim to employ strategies like rigorous regularization, prudent model pruning, and extensive data augmentation to create models that are not only accurate but also possess strong generalization capability, which is essential for practical applications in GIS.
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 9 Research Gaps and Opportunities
### Opportunities for Future Research
#### 9.2.3 Enhancing 3D Reconstruction and Semantic Mapping  
Semantic SLAM, which combines traditional geometric mapping with semantic understanding, offers richer representations of the environment by identifying and categorizing elements within the map [176; 177]. Future research should focus on the following aspects:  
* **Optical Distortion Compensation:** developing algorithms that compensate for underwater optical distortions, enabling more accurate 3D reconstructions from monocular or stereo-vision systems;
* **Underwater Semantic Segmentation:** creating specialized underwater semantic segmentation datasets to train robust object recognition and semantic segmentation models tailored for underwater conditions;
* **Graph Neural Networks (GNNs):** exploring GNNs to capture spatial relationships between objects within underwater environments, providing a richer semantic understanding;
* **Real-Time Performance:** optimizing algorithms for efficiency and leveraging specialized hardware accelerators to achieve real-time performance in 3D reconstruction and semantic mapping on resource-constrained underwater vehicles.
# Assessment of the Impacts of Climate Change on Power Systems: The Italian Case Study
## 4 Climate Change Impacts on Demand and Electricity Supply of Present-Day Power Systems
### PV Power  
Due to global warming, the probable increase in water vapor will likely lead to a decrease in downward shortwave radiation [37]. This effect will likely reduce solar PV generation in the coming decades. The results in [13] confirm this projection. In particular, changes in the ensemble mean for all European countries were estimated to be less than 5% for both the 1.5 \({}^{\circ}\)C and 2 \({}^{\circ}\)C global warming scenarios. For the 3 \({}^{\circ}\)C warming scenario, the decrease is expected to be below 5% for most countries, except for the northern countries (Sweden and Finland) and the Baltic countries, where the decrease is likely to be in the range of 5-10%. It is worth noting that the southern European countries (e.g., Italy) will likely experience a smaller decrease in PV production than the other countries. As expected, the magnitude of climate impacts on PV production increases with the severity of the global warming scenario. Finally, looking at the projections of the models, the magnitude and sign of the changes (positive or negative) were in agreement. This indicates that the model in [13] was robust. Moreover, the scatter between individual models was very low.  
In [14], as in [13], a global decrease in European PV generation was predicted at the multi-model mean change (mean changes of the ensemble). Specifically, an annual decrease of \(\sim\)1% was observed, which was larger for RCP 8.5 than for RCP 4.5. This decrease was the combined effect of a 3-5% reduction in winter and spring, together with a moderate increase in generation in summer and autumn.  
Regarding the individual case study countries, the northern European countries (Sweden and Germany) will experience a reduction in PV generation, as in [13], in agreement with [38] which predicted an increase in precipitation and cloud cover in northern Europe. Regarding Italy, [13; 14] are in agreement: very small annual reductions were found for Italy. Interestingly, in [14], a large dispersion between the models was observed, in addition to a small sampling uncertainty within each model.
# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## Abbreviations  
* 2D Two Dimensional 3D Three Dimensional 4G 4 th Generation 5G 5 th Generation 6G 6 th Generation AP  Access Point API  Application Programming Interface BS Base Station BVLoS  Beyond Visual Line of Sight D2D Device-To-Device DTN  Delay Tolerant Networking FAA Federal Aviation Administration GPS Global Positioning System HetNets Heterogeneous Networks HO Handover H-RRM HO and Radio Resource Management ICIC Inter-Cell Interference Coordination LC Loaded Cells Line of Sight UDCUN Ultra-Dense Cloud-UAV Network UDN Ultra-Dense Networks UE User Equipment UL Uplink UXNB UAV-Mounted Bs LTE Long Term Evolution LWA Leaky-Wave Antenna mcMTC Mission-Critical Machine-Type Communication MIMO Multiple Input Multiple Output mm-wave Millimeter Waves OTT Over-The-Top Pf False Ho Initiation Probability Ps Seamless Ho Success Probability QoS Quality of Service RLF Radio Link Failure eNB  evolved Node BS UAV Unmanned Aerial Vehicle RSS Received Signal Strength SDN Software-Defined Networking SINR Signal-To-Interference-Plus-Noise Ratio SWAP Size, Weight, And Power THz Terahertz TCP/IP Transmission Control Protocol/Internet Protocol FANET Flying Ad-Hoc Network VR Virtual Reality WLAN Wireless Local Area Network RWP Random Waypoint WWAN Wireless Wide Area Network RW Random Walk
## 5 Simulation Results and Performance Analyses
### Summary and Discussion  
The simulation results in Sections 5.2 and 5.3 show the effectiveness of the CLCQ design. First, the forwarding candidate set selection method proposed in Section 4.2, which comprehensively considers node depth, residual energy, and buffer state, reduces the exploration of invalid nodes and significantly speeds up the convergence rate of the algorithm. As shown in Figure 8, the convergence rate of the CLCQ is notably faster than that of QELAR, proving the contribution of this method to improving the overall efficiency of the protocol.  
Second, Figures 9-12 highlight the necessity of the reward function design. The reward function in Section 4.3 accounts for not only channel quality (BER) but also node residual energy and buffer state. As shown in Figures 9 and 10, despite the increase in energy consumption, the residual energy distribution is more balanced, indicating the effectiveness of energy-related rewards. Figures 11 and 12 further illustrate that the reward function based on channel quality and congestion effectively reduces end-to-end latency and improves the PDR.  
Finally, the comparison of the CLCQ with QELAR and GEDAR (Figures 13-16) shows that the CLCQ has clear advantages in energy consumption control, delay reduction, and delivery rate improvement in large-scale networks. This is primarily due to the cross-layer design presented in Section 4.1, which enhances transmission reliability and efficiency by dynamically evaluating channel quality and buffer state.
# Scan Matching by Cross-Correlation and Differential Evolution
## 4 Scan Matching by Cross-Correlation and Differential Evolution  
A novel mobile robot self-localization strategy that uses the combination of cross-correlation of laser range scans [42] and the DE is designed and evaluated in this work. It improves a recent DE-based scan matching algorithm [44] through the use of laser range scan cross-correlation as an accurate and computationally lightweight fitness function. In this approach, the DE is used as an optimization mechanism to find the optimal set of parameters for an affine transformation that aligns two 2-dimensional laser range scans so that their mutual cross-correlation is maximized. The parameters of the transformation correspond to the displacement of a mobile robot in the direction of the \(x\) and \(y\) axes, \(t_{x}\) and \(t_{y}\) and its rotation, \(\Phi\). The algorithm evolves a population of 3-dimensional candidate transformation vectors, \(\vec{t}=(t_{x},\,t_{y},\,\Phi)\).  
A graphical outline of the proposed scan matching process is shown in Figure 4. The figure shows a mobile robot moving in an indoor environment. On its way, the robot moved from a point of reference, A, to its current position, B. At each location, it acquired laser range scans denoted as \(\mathbf{A}\) and \(\mathbf{B}\), respectively. Each laser range scan produces a 2-dimensional point cloud in polar coordinates that represents the surroundings of the robot in the field-of-view of the LiDAR. The first scan, \(\mathbf{A}\), is transformed into a grid map, map\({}_{\text{A}}\), by Algorithm 2, to represent the original position of the robot.  
The second scan, \(\mathbf{B}\), is matched with \(\mathbf{A}\) by the DE algorithm. In each step, \(k\), of every generation of DE, scan \(\mathbf{B}\) is transformed by \(\mathcal{T}\) with parameter vector, \(\vec{t}_{k}=\left(t_{x},t_{y},\Phi\right)\), encoded by the \(k\)-th trial vector. Grid map \(\mathrm{map}_{\mathrm{Bk}}\) is then created from \(\mathbf{B}k\) and the cross-correlation of \(\mathrm{map}_{\mathrm{A}}\) and \(\mathrm{map}_{\mathrm{Bk}}\) is evaluated using Algorithm 3. The resulting degree of alignment,  
\[J(\mathbf{A},\mathbf{B},\vec{t}_{k})=\mathrm{CC}\left[\mathrm{GM}\left( \mathbf{A}\right),\,\mathrm{GM}\left(\mathcal{T}\left(\mathbf{B},t_{kx},t_{ky},\Phi_{k}\right)\right)\right], \tag{12}\]  
is taken by the DE as a fitness function. The fitness function consists of cross-correlation, CC, which is basically the implementation of \(\odot\) (see Equation (8)) and the function which creates grid map from a scan, GM. It determines whether the trial vector substitutes the corresponding target vector in the population. This procedure is iteratively repeated until the DE terminates. The best solution found by the DE corresponds to the best set of parameters and defines the transformation leading to the maximum cross-correlation between grid mas \(\mathrm{map}_{\mathrm{A}}\) and \(\mathrm{map}_{\mathrm{Bk}}\).  
Figure 4: Description of the proposed algorithm: laser range reading and DE-based scan matching.
## 4 Elastic Fusion Location Algorithm
 \tag{14}\]  
Calculate the distance \(d_{ki}\) between the positioning results of the remaining \(M_{1}\) sensors and the centroid \(g_{k}\), that is:  
\[d_{ki}=\sqrt{\left(x_{ki}-x_{k}^{\prime}\right)^{2}+\left(y_{ki}-y_{k}^{\prime }\right)^{2}+\left(z_{ki}-z_{k}^{\prime}\right)^{2}} \tag{15}\]Then define the weight of \(k\)-time fusion weighting as:  
\[\omega_{k}^{i}=\frac{1}{\sqrt{2\pi\sigma_{i}}}e^{-\frac{d_{ki}}{2\sigma_{i}^{2}}} \tag{16}\]  
where \(\sigma_{i}\) is the mean square error of the positioning of the \(i\)th sensor. Normalize the weight to obtain:  
\[\overline{\omega}_{k}^{i}=\ \omega_{k}^{i}\Bigg{/}\sum_{i=1}^{M_{1}}\omega_{k}^{i} \tag{17}\]  
Weight the positioning results of the remaining \(M_{1}\) sensors, that is:  
\[a_{k}= \begin{bmatrix}x_{k}\\ y_{k}\\ z_{k}\end{bmatrix} = \begin{bmatrix}\overline{\omega}_{k}^{1}x_{1}+\cdots \overline{\omega}_{k}^{i}x_{i}+\cdots\overline{\omega}_{k}^{M_{1}}x_{M_{1}}\\ \overline{\omega}_{k}^{1}y_{1}+\cdots\overline{\omega}_{k}^{i}y_{i}+\cdots \overline{\omega}_{k}^{M_{1}}y_{M_{1}}\\ \overline{\omega}_{k}^{i}z_{1}+\cdots\overline{\omega}_{k}^{i}z_{i}+\cdots \overline{\omega}_{k}^{i}z_{M_{1}}\end{bmatrix}  \tag{18}\]  
According to Equation (18), the elastic fusion positioning result at time \(k\) can be obtained. The implementation steps of the algorithm can be described as follows:  
(1) Compare the data of the same sensor at the current time and the previous time to judge whether there is a large jump in the current data. If the jump is greater than the set threshold, it is considered that the current data are not available and eliminated during positioning; on the contrary, it is considered that the current data are available;  
(2) Calculate the distance between the \(k\)-time positioning result of available multisource sensors and the \(k-1\)-time fusion positioning result and compare it with the set threshold \(\eta_{th1}\) to eliminate the positioning result greater than the threshold;  
(3) Calculate the centroid \(g_{k}\) of the remaining positioning results, and calculate the distance between the positioning results \(d_{ki}\) of the remaining \(M_{1}\) sensors and the centroid \(g_{k}\);  
(4) Construct the fusion weighting coefficient at the current time according to \(d_{ki}\) and location variance \(\sigma_{i}\), and normalize the fusion weighting coefficient;  
(5) Weight the remaining \(M_{1}\) sensor positioning results and fuse the results to obtain the fusion positioning results at the current time. This paper proposes an algorithm based on the elastic fusion of multisource data to realize stable and continuous navigation and positioning in complex environments, which can solve navigation and positioning problem in indoor and outdoor complex environments, improve the stability and positioning performance of navigation systems in complex environments and is suitable for multisource fusion navigation and positioning in complex environments.
## 5 Experimental Validation
### Experimental Result for Shading Pattern 3  
Figure 20a-c illustrates the experimental results of the conventional and proposed MPPT schemes for shading pattern 3. In this shading pattern, GMPP, LMPP1, and LMPP2 comprise 64.34, 47.63, and 25.08 W in descending order, while voltages are held at 13.18, 31.31, and 48.98 V, respectively.  
Figure 20: Comparative analysis of the algorithms at the left GMPP of the PSC: **(a)** INC-MPPT; **(b)** P&O-MPPT; and **(c)** Proposed algorithm.  
As depicted in Figure 20a, the conventional INC algorithm failed to reach nearer to the GMPP location while experiencing this environmental condition. The INC algorithm starts searching for a local MPP location throughout the PV curve and ends up tracking a local MPP point, which exerts power oscillation (46.52-48.53 W) and duty cycle perturbations (0.451-0.471) at that MPP point. Therefore, only 73.67% power efficiency is confirmed and 0.42 s is required to reach closer to the LMPP coordinate (31.31 V, 47.63 W). Similarly, the ordinary P&O algorithm failed to track GMPP and located the same LMPP location with different power oscillations (48.415-48.3 W) (Figure 20b). A continuous duty cycle perturbation occurred from 0.474 to 0.477 in the P&O technique under SSC. Thus, this algorithm is unreliable for tracking GMPP in the shading pattern 3, because of low tracking efficiency (73.51%) and slow tracking speed (0.51 s). However, GMPP is tracked accurately within 0.25 s when using the proposed algorithm under this shading condition (Figure 20c). Dragging the operating point to the stage of SSC and reaching the GMPP, requires only 0.25 s in the proposed method. In this method, the perturbation of the duty cycle starts from the right side of the PV curve to search for the MPP and stops at the left side GMPP (13.18 V, 64.2 W) and the maximum power efficiency (99.83%) is confirmed. However, one disadvantage of tracking the GMPP using the proposed technique is that MPPT should be operated at a constant duty cycle of 0.778 under SSC. Comparatively, the proposed technique is two times more efficient than conventional techniques.
# Integrating Knowledge Graphs into Distribution Grid Decision Support Systems +
## 5 GridKG--Graph-Based Representation of Distribution Grid  
The previously defined vocabulary PowerLOV is used to construct a KG of a power distribution grid. The graph is a model of a full-sized power distribution system built based on partial information obtained from the utility. Hereafter, the graph is called GridKG. It contains more than 2 million nodes and 10 million connections. _Neo4j_[32] is used to store the graph.  
GridKG combines several types of information of a very different nature: details of electrical equipment, system topology, and system events. All individual pieces of information are semantically interconnected. Connections between the nodes of GridKG provide the ability to process and analyze the modeled grid in several different ways.
# Attack Classification Schema for Smart City WSNs
## 3 Intrusion Detection Framework
### WSN Data  
Smart city data centers collect multiple types of data, which can be used to detect intrusions:  
* Basic information about the nodes: ID, latitude, longitude, etc. The geographical position is a basic parameter to determine the area affected by the attacks.
* Basic information about the WSN: service purpose (e.g., parking, environmental monitoring), communication protocol (e.g. ZigBee, LoRa), etc. From this information, other information about the WSN can be extracted. For example, ZigBee has two possible topologies (i.e., tree and star), and its frequency bands in Europe are either 868 MHz or 2.4 GHz. This information can be used to discard possible attacks against a service and to cluster data.
* Basic information about the packets: packet number, gateway ID, timestamps, etc. Additional fields can be computed from this basic information, such as the one-way delay (OWD), which indicates the time taken by the packets from the sensor nodes to the server. This is an important field for the detection of some attacks, such as DoS, since these attacks tend to slow down packet reception.
* Basic information about system status: received signal strength indicator (RSSI), signal-to-noise ratio (SNR), etc. Some attacks have a direct impact on some of these variables. For instance, attacks generating interference impact RSSI.
* Information about the services: sensor readings, service data aggregated in time intervals, etc. These data are sent either in scheduled regular time intervals (e.g., environmental data) or when sensors have reacted to an environmental condition (e.g., parking activity). Anomalies in these data can indicate badly-calibrated sensors and data integrity attacks.
* Battery status: The objective of many WSN attacks is to exhaust sensor batteries. Therefore, this information is very useful for the detection of this type of attack.  
This WSN information is accessible for the smart city administrators. Therefore, the attack models presented below are based on analyzing these types of data in order to extract the conclusions presented in this article.
# Highly Flowable Nano TiO2/Porous Organic Polymer (POP) Supports for Efficient Metallocene Catalysts
###### Abstract  
Porous organic polymers (POPs) have proven to be an efficient support in the olefin polymerization catalyst field. In this paper, nano TiO2 beads were used to modulate the pore structure, bulk density, and surface morphology and flowability of the prepared POPs. With the incorporation of the hydrophilic nano TiO2 beads, the prepared TiO2/POP supports obtained reasonable specific surface area (100-300 m\({}^{2}\)/g) and higher bulk density (0.26-0.35 g/mL) and flowability than the pure POP supports. The results show that bulk density of the prepared TiO2/POP particles increased when adding an increased amount of TiO2 and when 37.5% TiO2 (weight percent to the total comonomers divinylbenzene (DVB) and 2-hydroxyethyl methacrylate (HEMA)) and 3:1 DVB/HEMA (molar ratio) were added, highly flowable TiO2/POP composites (POP-6 and POP-7) were obtained. With the modulation of the nano TiO2 template during the support synthesis, the prepared POP-7 particles successfully achieved a normal distribution with a narrow particle size distribution (PSD) of 0.717 and average particle size of 24.1 um, a specific surface area (SSA) of 279 m\({}^{2}\)/g, and relatively high bulk density of 0.30 g/mL. Furthermore, all the prepared TiO2/POP supports obtained higher ethylene polymerization activity than silica gel-supported commercial metallocene catalyst. The immobilized (n-BuCp)2 ZrCl2/MAO@POP-7 catalyst exhibited the highest ethylene polymerization activity of 4794 kg PE/mol Zr.barh and productivity of 389 g PE/g cat, more than twice that of the commercial counterpart. Even higher catalyst productivity (3197 g PE/g cat) and bulk density of the produced PE (0.36 g/mL) could be obtained in higher ethylene partial pressure at 80 \({}^{\circ}\)C for 2 h, and the prepared TiO2/POP catalyst shows no obvious Zr+ active sites decay during the ethylene polymerization.  
Keywords:nano titanium oxide; porous organic polymers (POPs); metallocene catalyst; ethylene polymerization 2021 11 60 3390/nano11010060 +
Footnote †: journal: Nanomaterials
## 4 Discussion
### Vibroacoustic Analysis  
The description of the hybrid model's response to the external acoustic field and internal acoustic field generated at lift-off by the P120 engine are reported, respectively, in terms of an average acceleration and a root-mean-square (RMS) pressure.  
The acceleration data are reported as band-limited RMS spectrum responses strictly associated to the frequency band selected for the analysis. In the analysis, the damping was assumed as constant on the frequency, and similar was assumed for all the structural (values set to 0.04) and the acoustic (values set to 0.001) subsystems. The response of some equipment is reported in Figure 5 as an example.  
Figure 5: Accelerations calculated for some equipment in the IS01 interstage.  
For IS01, the acceleration levels were very high in the low frequency range. In particular, the main contribution to the vibration energy was clearly associated to the structure's ring frequency, along with global modes' impact occurring at a lower frequency; above ring frequencies, the acceleration levels attenuated significantly for almost all the equipment, with some high-frequency contributions derived from the coincidence frequencies effect. In general, the dynamics of the lumped masses simulating the equipment appears as confined to the low-mid frequency.  
The average pressure level associated to the IS01 cavity is reported in Figure 6, described in decibel values, referred to the band-limited RMS spectrum response, in which the reference pressure is the standard pressure value, \(2\times 10^{-5}\) Pa. The responses reflect the same distribution against the frequency of the structural interstage, with the higher energy concentrated at the characteristic frequency already identified for the structure.