esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Polygonal and Textured Models  
The parameters for the resulting models are presented in Table 10. The number of faces of the digital camera's polygonal meshes is almost 3 times the number of those of the smartphone camera. This is due to the higher density associated with its corresponding dense clouds. The output data regarding the generated textures are provided in Table 11. Although the texture atlas size was set the same (4096 pixels) for all models with the same mapping and blending settings, the difference in texture quality is quite established in the images provided for the textured models in Figure 19. This difference in quality is mainly attributed to the higher resolution of the digital camera images compared with the smartphone images, as they are the texture data source.  
Figure 17: The density of the sandpile densified point cloud generated from the digital camera data: (**a**) number of points enclosed by a sphere (\(r=5\) cm) and its volume density distribution; (**b**) the volume density histogram of the same dense cloud with its normal distribution fitting (\(\mu=74912,696\) pts/m\({}^{3}\), \(\sigma=15,519,577\) pts/m\({}^{3}\)). The cloud is scaled in meters.  
Figure 18: The density of the sandpile densified point cloud generated from the smartphone data: (**a**) number of points enclosed by a sphere (\(r=5\) cm) and its volume density distribution; (**b**) the volume density histogram of the same dense cloud with its normal distribution fitting (\(\mu=24,072,578\) pts/m\({}^{3}\), \(\sigma=4,679,245\) pts/m\({}^{3}\)). The cloud is scaled in meters.  
By examining the processing time and memory usage parameters in Tables 9-11, it can be generally concluded that the DC's data require a longer time and utilize more memory to generate the photogrammetric outputs compared with the SP's data. This is ascribed to the DC's larger files that were initially generated from larger-sized images. For instance, a smartphone image has an average size of 3.2 MB, whereas the average digital camera image size is 19 MB. This substantial difference is mainly due to the higher image resolution and the RAW format of the DC's images compared with the JPG format that compresses images resulting in smaller image size. Figure 20 presents the processing time and memory utilization as cumulative bar charts for the whole photogrammetric workflow for easier visualization and interpretation. The processing steps can be easily compared with each other based on their processing time and memory utilization. For instance, building a polygonal mesh and generating a texture map are the most time and memory-consuming steps in all data sets.
## 2 Results
### Real-Time PCR Analysis  
The influence of test compounds on cell death, biofilm formation activity and stress was evaluated by monitoring the change in the expression level of autolysin-like genes (_Atl_E- and _Atl_A-like genes), general stress gene (_Pntp_A), oxidative stress gene (_sod_A gene), and genes associated with biofilm formation (_ymc_A and _gtf_B genes). Since _Atl_A- and _Pntp_A-like genes were not amplified sufficiently by simple PCR using genomic DNA from _S. mutans_ as template, these primers were not included for further RT-PCR studies. Moreover, the specificity of the primers was confirmed through the sequencing of amplified PCR products ([PERSON] et al., 2017). The most significant change was recorded in the expression levels of Autolysin gene (_Atl_E), wherein a 3.36-, 2.66- and 2.76-fold increase was observed when _S. mutans_ was grown in the presence of 10 \(\mu\)g/mL of CHX, MD-1 and MF-1, respectively (Figure 7B). This increased expression of _Atl_E gene indicates that the apoptosis-like activity in _S. mutans_ is induced upon exposure to the test compounds. The expression of _ymc_A gene involved in the biofilm formation also increased by 2.93-, 1.81- and 2.71-fold in the presence of CHX, MD-1 and MF-1, respectively. On the contrary the expression of another gene involved in the biofilm formation _gtf_B decreased by \(-\)0.6-, \(-\)0.3- and \(-\)0.4-fold in the presence of CHX, MD-1 and MF-1, respectively, while, the expression of _sod_A genes increased by 1.1-, 1.32- and 1.43-fold when the bacteria were grown with clove oil, MD-1, and MF-1, respectively (Figure 7B).
# Vibration Control of Multi-Modular VLFS in Random Sea Based on Stiffness-Adjustable Connectors
## 4 Numerical Assessment on the Performance
### Parameters of the VLFS and the Random Sea Condition  
The wave spectrum represents the energy distribution of the random waves in the frequency domain, which usually represents a semi-empirical relationship formularized from observed wave datum such as wave height, excitation angle, etc. In random sea conditions, individual waves may be variant and irregular, but the statistic characteristics of random waves can be described by a wave spectrum. In this paper, the Jonswap spectrum [39] is used for the numerical studies, which can be expressed as  
\[\text{S}(\omega)=\alpha_{s}g^{2}\omega^{-5}\exp\left(-1.25\omega_{s}{}^{4} \omega^{-4}\right)\gamma_{s}\exp\left(-\frac{(\omega-\omega_{s})^{2}}{2\omega^{ -2}\omega_{s}{}^{2}}\right) \tag{32}\]  
where  
\[\begin{array}{l}\alpha_{s}=\frac{5}{16}\big{(}\frac{A_{s}^{2}\omega_{s}}{g^{ 2}}\big{)}[1-0.278\ln(\gamma_{s})]\\ \sigma=\left\{\begin{array}{cc}0.07&\omega\leq\omega_{s}\\ 0.09&\omega>\omega_{s}\end{array}\right.\end{array} \tag{33}\]  
In Equations (32) and (33), symbols \(A_{s}\), \(\omega_{s}\), and \(\gamma_{s}\) are the significant wave height, the peak wave frequency, and the peak enhancement factor, respectively. In numerical simulations, we take \(A_{s}=5\) m, \(\omega_{s}=2\pi/10\) rad/s, and \(\gamma_{s}=3.3\). The wave depth is 300 m. The number of modules of the VLFS is N = 5, and each module is the same [38], and the parameters can be found in Table 1. It is worth noting that only the first module and the last module are connected with mooring chains. The units of the first three moor stiffness elements, \(\mathbf{K}_{M,1}\) and \(\mathbf{K}_{M,5}\), are \(\text{N}/\text{m}\), and the units of the last three elements are N\(\cdot\)m/rad.  
Between two adjacent modules, there are four novel connectors that are mounted horizontally in parallel at the four corners of the modules, and another one is vertically installed in the middle, as illustrated in Figure 3. The four connectors at the corners mainly suppress the surge, pitch, and yaw motions, and the middle connector can suppress the heave motion of the modules. This connector configuration was specifically designed to suppress the four unwanted motions in this paper. It should be remarked there are many possible layouts for the novel stiffness-adjustable novel connectors depending on designer preference and on motion control. In numerical simulations, the coordinates of the mounting points of the five connectors in the local frames are shown in Table 2. All of the novel connectors are identical. Each individual connector has the same radial and axial stiffness of \(10^{7}\) N\(/\)m, as used in [38].
# Feasibility of LoRa for Smart Home Indoor Localization
## 4 Discussion  
### Accuracy  
Although the accuracy of implementation of LoRa localization is better than 3.2 m, it may be desirable to improve the accuracy depending on the application. We discuss three potential solutions to improve the localization accuracy, especially in the NLOS conditions. Similar solutions have been discussed in the literature in this area, but it is still worth exploring such ideas in the context of indoor localization in home setting.  
1- Filtering: In the presence of multiple location estimates, we can use signal filtering techniques, such as low-pass filtering, to detect and mitigate any spurious or sudden changes in the signal power.  
2- Anchor selection: With more than three anchor nodes available, we can choose the best subset of anchor nodes to provide more accurate location estimate. We can select the best of anchors by monitoring a signal quality measure such as RSSI.  
3- Different localization schemes: Instead of RSSI, we might be able to use time-of-arrival (TOA) or time-difference-of-arrival (TDOA) techniques.  
4- Geometric correction: Since the standard deviation of error is small in all cases, we only have the accuracy problem which can potentially be corrected with the knowledge of the geometry of the environment or using fingerprinting techniques.
## 2 Materials and Methods
### Classifiers
XPM: We used a linear kernel (kernel = 'linear') for simplicity and efficiency, with class_weight = 'balanced' and random_state = 42 (kernel: specifies the kernel type to be used in the algorithm. 'linear' uses a linear hyperplane; class_weight: weights associated with classes. 'balanced' mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data; random_state: controls the randomness of the algorithm). \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Max\_Iter/** & **Class** & **Random** & **Kernel** & **Max** & **Min** & **Min** & **Min** & **No** & **Use** & **Eval** \\  & **n\_Estimators** & **\_Weight** & **\_State** & **\_Depth** & **\_Samples** & **\_Samples** & **\_Samples** & **\_Label** & **\_Encoder** & **\_Metric** \\ \hline Random Forest & 300 & balanced & 42 & - & 50 & 2 & 2 & False & \(-1\) & - \\ XGBoost & 300 & - & 42 & - & 5 & - & - & - & - & mlogloss \\ Decision Tree & - & balanced & 42 & - & 50 & 2 & 2 & - & - & - \\ Logistic & 500 & balanced & 42 & - & - & - & - & - & - & - \\ Regression & SVM & - & balanced & 42 & linear & - & - & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The parameter settings. To ensure a fair and unbiased comparison of the five classifiers, a consistent 80:20 train-test split ratio was employed for all models. This approach promotes consistency and strengthens the validity of the study's conclusions by ensuring that each model is evaluated under the same conditions, allowing for a more reliable comparison of their performance and generalization capabilities. The training results demonstrate varying effectiveness across the five classification algorithms employed for music emotion recognition. Random forest achieved the highest accuracy (0.95), indicating its strong ability to model the complex relationships within the data and generalize well to unseen examples. XGBoost also delivered a competitive performance with an accuracy of 0.89, showcasing its efficiency and power in handling large datasets. While the decision tree offered reasonable accuracy (0.85), its performance was notably lower than the ensemble methods, likely due to its susceptibility to overfitting. The linear models, logistic regression (0.79) and SVM (0.78), exhibited the lowest accuracies, suggesting that the underlying patterns in the music data may not be easily separable by linear decision boundaries. In conclusion, the ensemble-based algorithms, random forest and XGBoost, proved to be the most effective for classifying music emotion in this study, with Random Forest exhibiting the most substantial performance overall. We show the accuracy results in Table 2 and the accuracy and loss functions of the random forest training and test sets in Figure 1.
# Smart Meter Gateways: Options for a BSI-Compliant Integration of Energy Management Systems
## 2 Related Work
### Handling of Metrological Data: PTB Specification  
As the SMGW deals with metrological data which, among others, are used for billing purposes, legal requirements with respect to the _German Measurement and Calibration Law_ (\"Mess- und Eichgesetz\", MessEG) have to be met [17]. To reflect new technological developments in the context of _Smart Metering Systems_, an amendment of the MessEG has been passed in 2013, with validity from 2015 onwards. Based on this, the _German National Metrology Institute_ (\"Physikalisch-Technische Bundesanstalt\", PTB) defined technical requirements regarding the acquisition and processing of metrological data by the SMGW. These have been published in the requirements document _PTB-A50.8_ in 2014 [18]. Among others, the document defines the need for a display (for the verification of utility bills by comparison with local meter measurements), and a trustworthy time server architecture (for guaranteeing the assignment of valid timestamps to meter measurements). SMGWs as nationally regulated devices within the meaning of the MessEG have to comply with these requirements and have to obtain a type-examination certificate (\"Baumusterprufbescheinigung\") according to the _PTB-A50.8_, in order to grant the permission to be deployed in practice.
## 5 Case Study: Application of MHAR-OLSR  
In this work, the network HAdN_MVF is assumed to be composed of mobile, vehicular, and UAV nodes (c.f., Figure 8). It is divided into the ground area whose size depends on the communication range of vehicles (300 m) and mobiles (100 m). As for the aerial zone, we have two different cases: small aerial zones and large aerial zones depending on the communication range of the drones, which is 400 m and 1000 m, respectively [36, 37]. The communication between two nodes can be successfully established if they are within the communication range of each other.  
The choice of drones that fly at low and constant altitudes [38] ensures a wide coverage that supports more terrestrial devices (cars and mobiles) [39, 40]. Each node in the network is equipped with a GPS system that specifies its current geographical position.  
On the energy side, vehicles and drones are equipped with rechargeable batteries that can be powered by solar energy, gasoline, or electric energy [41].  
The exchange of data packets over the network is done by means of integrated wireless interfaces using IEEE 802.11p at the MAC layer [42] and MHAR-OLSR at the network layer. The last protocol uses the same principle as standard OLSR routing [43] by exchanging Hello and TC messages between its nodes:  
* The Hello message allows each node to have an overview of its first and second neighborhoods (discovery of neighbors). Each device sends a Hello message including all the necessary information and utilities: type of connection, will of the node, neighbors' information, etc.
* The topology control message (Tc message) is broadcasted periodically within the network. It's the structure where nodes insert the list of their neighbors to build the routing tables.  
In the HAdN_MVF network, we have six possible types of links (c.f., Figure 9): Vehicle-to-Vehicle \"V2V\", Vehicle-to-Mobile \"V2M\", Vehicle-to-UAV \"V2U\", UAV-to-Mobile \"U2M\", UAV-to-UAV \"U2U\", and Mobile-to-Mobile \"M2M\".  
Figure 8: Example of HAdN_MVF network.
## 4 Approaches of Optimizing Geolocation Measurements  
In this section, we review previous and state-of-the-art geolocation and target tracking approaches that deal with wireless and satellite applications employing different geolocation measurements.
## 3 Results and Discussion
### Hyperspectral Imaging
#### 3.1.2 PLS-DA Model Results
study to identify intramuscular fat distribution [3]. The waveband at 932 nm is responsible for the third overtone of the C-H stretching, which is related to the presence of fat in the muscle [35]. Similar spectral information in the selected waveband was found by the [PERSON] et al. study for fat profile of the beef [2].
# Convergence Analysis of the Phase-Scheduled-Command FXLMS Algorithm with Phase Error
## 4 Simulations
### Case 3  
In this case, a real acoustic duct system was used, and the active sound profiling algorithm was employed to obtain the influence of the phase error, as mentioned above. The ASP performance under different phase errors in the steady state is shown in Figure 10. When the command signal was in phase with the disturbance signal, as shown in Figure 10a, there was a superior control performance, reducing the disturbance amplitude range from approximately 24 dB to less than 4 dB. When the phase error increased, as shown in Figure 10b-d, the profiled sound (error signal) limit had a significant decrease in perfor  
Figure 8: The MSE of the pseudo-error under different phase errors when \(f\) changes from 30 Hz to 150 Hz. The step size was set to a linear change from 0.000005 to 0.000022.  
Figure 9: The MSE of the pseudo-error under different phase errors when \(f\) changes from 150 Hz to 30 Hz. The step size was set to a linear change from 0.000005 to 0.000022.  
mance. The results indicated that the out of phase between command and disturbance signals could be adverse to the main objective of profiling the nonstationary noise.
# A Review of Research on Dropwise Condensation Heat Transfer
## 3 Dropwise Condensation Heat Transfer Model  
Unlike filmwise condensation, dropwise condensation is a discrete phase transition process in which droplets grow in random sites on the surface and then coalesce and depart. This characteristic of dropwise condensation makes it difficult to simulate the overall heat transfer characteristics by computational fluid dynamics (CFD). Based on this problem, many scholars usually establish a one-dimensional heat transfer model of a single liquid droplet and then use the statistic method (population balance) to predict the whole surface's overall heat transfer coefficient.
# Semi-Direct Point-Line Visual Inertial Odometry for MAVs
## 2 Mathematical Formulation
### Point Feature Projection  
For a pin-hole camera model, the projection \(\pi_{c}(*)\) from the camera coordinate \(\mathbf{P}_{c}=\left[\begin{array}{cc}x_{c}&y_{c}&z_{c}\end{array}\right]^{T} \in\mathbb{R}^{3}\) to the camera image plane \(\mathbf{p}=\left[\begin{array}{cc}u&v&1\end{array}\right]^{T}\) can be defined as:  
\[\mathbf{p}=\left[\begin{array}{c}u\\ v\\ 1\end{array}\right]=\pi_{c}(\mathbf{P}_{c})=\frac{1}{z_{c}}\mathbf{K}\mathbf{P }_{c}=\frac{1}{z_{c}}\left[\begin{array}{ccc}f_{x}&0&c_{x}\\ 0&f_{y}&c_{y}\\ 0&0&1\end{array}\right]\left[\begin{array}{c}x_{c}\\ y_{c}\\ z_{c}\end{array}\right] \tag{4}\]  
where \((f_{x},f_{y},c_{x},c_{y})\) represent camera intrinsic parameters.
## 5 Discussion
### Geological Implication of Element Migration for the Beiya Deposit  
Element migration of different mineralization-alteration zones roughly describes the ore-forming process at Beiya; meanwhile, a reasonably inferred mineralization and alteration zoning model of the Beiya gold-polymetallic deposit is established as well (Figure 8). In Figure 8, along with the continuous evolution of ore-forming fluids, it generates concentric-banded alteration zone from the center of the alkaline porphyry outward or upward, i.e., [porphyry alteration] potassic zone \(\rightarrow\) phyllic zone \(\rightarrow\) propylitic zone \(\rightarrow\) argillic zone \(\rightarrow\) [contact alteration] skarnitization-marbleization zone \(\rightarrow\) [wall-rock alteration] marbleization-silicification-calcitization zone. In potassic and phyllic zones, it develops porphyry-type native gold-chalcopyrite-bornite-molybdenum-pyrite mineralization. Skarn-type native gold-pyrite-magnetite-chalcopyrite-bornite-siderite mineralization occurs outward in the contact alteration zone. Proximal hydrothermal vein-type native gold-pyrite-magnetite mineralization mainly distributes in the marbleization Beiya Fm. limestone, while distal hydrothermal-type galena-sphalerite-native gold-native silver mineralization predominantly forms in silicification and calcification alteration zones. Metallogenic element association also exhibits a significant zone, laterally and vertically, of Cu-Mo \(\rightarrow\) Cu-Au \(\rightarrow\) Au-Fe-Cu \(\rightarrow\) Au-Fe \(\rightarrow\) Pb-Zn-Au-Ag-Fe from alkaline porphyry outward to the wall-rock.  
This provides a new basis for exploration in the Beiya deposit from the element migration and its related mineralization and alteration zoning model (Table 3). In the deep area and center of the porphyry that associated with mineralization, it is beneficial to find  
Figure 8: Mineralization and alteration zoning model of the Beiya deposit.  
the porphyry-type Cu-Mo-Au ore-bodies or deposits. In the space between wall-rock and ore-forming porphyry, skarn-type Au-Fe-Cu ore-bodies or deposits are the most favorable prospecting object. Distal to the ore-forming porphyry, the hydrothermal vein-type Au-Fe and Pb-Zn-Au-Ag-Fe ore-bodies or deposits are formed and distributed; moreover, the Pb-polymetallic deposit found at Luping [47; 48] located 2 km north of the Beiya deposit is a good example.
# Empirical Perturbation Analysis of Two Adversarial Attacks: Black Box versus White Box
## 5 Transferability and Texture Bias
### How Does CNNs' Texture Bias Influence Transferability?  
Knowing that CNNs trained on ImageNet are biased towards texture, we assume that a high probability for a particular class given by such a CNN expresses the fact that the input image contains more of that class's texture. Our goal is to check whether this occurs for adversarial images as well.  
We restrict our study to adversarial images obtained by the EA and BIM for the following three CNNs, which have a similar architecture and have been proven [24] to gradually have less texture bias and less reliance on their texture-encoding neurons: \(T_{1}=\text{BagNet-17}\)[33], \(T_{2}=\text{ResNet-50}\), and \(T_{3}=\text{ResNet-50}\)-SIN [21]. The experiments amount to checking the transferability of the adversarial images between these three CNNs. The fact that the statement about the graduation is fully proven only for these three justifies that we limit our study to them, since no such hierarchy is known for other CNNs in general.  
Even in this case of three CNNs with similar architectures, the experiments show that targeted transferability between the three CNNs is 0%, regardless of the attack. Consequently, checking whether \(c_{t}\) becomes dominant for another CNN is unnecessary. Instead, we calculate the difference produced in a CNN's predictions of the \(c_{t}\) and \(c_{a}\) probabilities between the ancestor and another CNN's adversarial image. The average results over all images are presented in Table 3.  
When transferring from \(T_{2}=\text{ResNet-50}\) to \(T_{1}=\text{BagNet-17}\), the experiments show that the \(c_{a}\)-label value decreases while the \(c_{t}\)-label value increases, with the former being larger in magnitude than the latter. If the assumption formulated in the first paragraph holds, this phenomenon implies that the attacks change image texture. However, the similarly low transferability from \(T_{1}=\text{BagNet-17}\) to \(T_{2}=\text{ResNet-50}\) proves that texture change is not sufficient to generate adversarial images. The texture change observed in \(T_{2}=\text{ResNet-50}\) adversarials might simply be a side effect of the perturbations created by the EA and BIM.  
Nevertheless, Table 3 reveals that texture bias seems to play a role in transferability. It shows that the more texture-biased the CNN that the adversarial images are transferred to, the larger the decrease in its \(c_{a}\)-label values. Indeed, this \(c_{a}\) decrease is larger when transferring from \(T_{3}=\text{ResNet-50}\)-SIN to \(T_{2}=\text{ResNet-50}\) and from \(T_{2}=\text{ResNet-50}\) to \(T_{1}=\text{BagNet-17}\) than vice-versa.