esa-sceva/satcom-chunk-collection huggingface dataset:
# A CSI Fingerprint Method for Indoor Pseudolite Positioning Based on RT-ANN
## 5 Result
### Fingerprint Data Generation  
In the actual test, 25 position reference points with an interval of 1 m are selected to test the positioning accuracy. In order to show the positioning error intuitively, an appropriate relative coordinate system is established so that the x and y coordinates of the 25 reference points are exact integers 1-5. As shown in Figure 7, in test area 1, the observation data of six pseudolites are collected in real-time by the receiver at an interval of 1 m, the collection time of each node is 30 s. The collected data is used as the input of the measured part of the neural network after normalization processing.  
In the simulating calculation process, there are multiple propagation paths between the transmitting and receiving points. The direct path has the shortest transmission distance and the strongest reaching power. With the increase of the propagation distance and the number of reflection and transmission times, the signal transmission distance and the corresponding transmission delay also increase, and the pseudolite signal power attenuation is more serious until it is lower than the receiver signal power threshold. As shown in Figures 8 and 9, the electromagnetic information of 400 test points in text area 1 and 100 test points in text area 2 is obtained through 3D ray tracing calculation, which is converted and normalized as the simulation input part. Due to space limitations, Figures 8 and 9 show some examples of electromagnetic calculation results at 10 receiving test points.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Parameter**} & **Text Area with** & **Text Area without** \\  & **Fingerprints Collection** & **Fingerprints Collection** \\ \hline scene model size & \(100\) m \(\times\) 20 m \(\times\) 4.5 m & \(100\) m \(\times\) 20 m \(\times\) 4.5 m \\ bearing pillar size & \(0.5\) m \(\times\) 0.5 m \(\times\) 4.5 m & \(0.5\) m \(\times\) 0.5 m \(\times\) 4.5 m \\ wall material/permittivity/conductivity & concrete/5/0.0015 & concrete/5/0.0015 \\ floor material/permittivity/conductivity & tile marble/6/10\({}^{-8}\) & tile marble/6/10\({}^{-8}\) \\ signal frequency & \(1561.098\) MHz & \(1561.098\) MHz \\ antenna type & omnidirectional & omnidirectional \\ receiving area size & \(5\) m \(\times\) 5 m & \(5\) m \(\times\) 5 m \\ receiving point interval & \(0.25\) m & \(0.5\) m \\ transmitting power & \(-70\) dBm & \(-70\) dBm \\ number of reflections/transmissions/diffractions & \(3/1/1\) & \(3/1/1\) \\ ray interval & \(0.25\) degree & \(0.25\) degree \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation calculation parameters for airport model.  
Figure 7: Experimental environment and method.
## 3 Methods
### Problem Formulation  
For the short-term OD prediction of this paper, the historical OD matrices \(M\) of previous days and the historical inflow series \(N\) of the same day are used to generate the predicted OD matrix \(O\), where any of the historical OD matrices and any historical inflow series can be formulated as follows:  
\[M^{d,t}=\begin{bmatrix}m_{11}^{d,t}&m_{12}^{d,t}&\cdots&m_{1j}^{d,t}&\cdots&m_{1 \phi}^{d,t}\\ m_{21}^{d,t}&m_{22}^{d,t}&\cdots&m_{2j}^{d,t}&\cdots&m_{2\phi}^{d,t}\\ \vdots&\vdots&&\vdots&&\vdots\\ m_{i1}^{d,t}&m_{i2}^{d,t}&\cdots&m_{ij}^{d,t}&\cdots&m_{i\phi}^{d,t}\\ \vdots&\vdots&&\vdots&&\vdots\\ m_{\phi 1}^{d,t}&m_{\phi 2}^{d,t}&\cdots&m_{\phi j}^{d,t}&\cdots&m_{\phi\phi}^{d,t} \\ \end{bmatrix}, \tag{1}\]  
\[N^{t}=\begin{bmatrix}n_{1}^{t}\\ n_{2}^{t}\\ \vdots\\ n_{i}^{t}\\ \vdots\\ n_{\phi}^{t}\end{bmatrix}, \tag{2}\]  
where \(m_{ij}^{d,t}\) denotes the OD flow between station \(i\) and station \(j\) at the time interval \(t\) on day \(d\), and \(n_{i}^{t}\) represents the inflow of station \(i\) at the time interval \(t\) of the same day. Given that the predicted time interval is the time interval \(\kappa\) on day \(\delta\), the time delay of OD data acquisition is \(\alpha\) minutes, and prediction time granularity is set to \(\beta\) minutes. Figure 1 shows the temporal relationship between the historical OD data and the predicted OD matrix. According to Figure 1, the short-term OD prediction can be formulated as follows:  
\[O^{\kappa,\delta}=f(M^{\kappa-y,\delta-x_{1}},N^{\delta-\alpha-\beta-x_{2}}), \tag{3}\]  
where \(O^{\kappa,\delta}\) represents the OD matrix at the predicted time interval, and \(M^{\kappa-y,\delta-x_{1}}\) denotes the historical OD matrix at the time interval \(\delta-x_{1}\) on day \(\kappa-y\), where \(y=1,2,\cdots,\lambda_{1}\) and \(x_{1}=0,1,\cdots,\lambda_{2}-1\). \(N^{\delta-\alpha-\beta-x_{2}}\) denotes the inflow series at the time interval \(\delta-\alpha-\beta-x_{2}\) of the same day, where \(x_{2}=1,2,\cdots,\lambda_{3}\). \(f(\cdot,\cdot)\) is a nonlinear function. In this paper, \(f(\cdot,\cdot)\) denotes the forward process of the proposed PIANet.
## 5 Results
### Results of Capacity Variations  
This section shows the results of the experiments with the variation in hubs' capacity in comparison with the baseline scenario. Table 8 shows these results.  
As expected, the greater the capacity, the greater the quantities are traded, and the greater the net profits are. In the three new scenarios, the number of open hubs is 21. The trend, however, is of diminishing returns. Doubling the capacity in relation to the baseline produces a net profit of USD 1,162,000 (39% increase) whereas tripling the capacity produces a net profit of USD 1,498,000 (57% increase). In terms of quantities, the relation with the capacity increments is approximately linear: An addition of one percentage point in capacity represents an increase of approximately 37 handled tons. The fixed operation costs also increase in a linear fashion in relation to the baseline. An increment of one percentage point in capacity represents on average an increase of around USD 1360 in fixed costs.  
We can also observe that the transportation costs also increase compared to the baseline as hubs handle larger quantities of products, which in turn require more trucks. However, the transportation costs do not increase that steeply: For example, tripling the hubs' capacity results in an increase of 58% in the transportation cost. This implies that the greater the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \begin{tabular}{c} **Increase in** \\ **Hubs’ Capacity** \\ \end{tabular} & \multirow{2}{*}{**Gross Profit**} & \begin{tabular}{c} **Transportation** \\ **Costs** \\ \end{tabular} & \begin{tabular}{c} **Emission** \\ **Costs** \\ \end{tabular} & \
\begin{tabular}{c} **Fixed Hubs’** \\ **Operation** \\ **Costs** \\ \end{tabular} & \multirow{2}{*}{**Net Profit**} & \multirow{2}{*}{**No Hubs**} &
\begin{tabular}{c} **Total Handled** \\ **Quantities (tons)** \\ \end{tabular} \\ \hline Baseline \(1\times\) & 1310 & 198 & 16 & 146 & 950 & 23 & 8866 \\
1.5\(\times\) & 1617 & 232 & 17 & 206 & 1162 & 21 & 11,095 \\
2\(\times\) & 1884 & 265 & 19 & 274 & 1326 & 21 & 13,255 \\
3\(\times\) & 2244 & 313 & 21 & 412 & 1498 & 21 & 16,488 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Summary of results of hubs’ capacity variations. The weekly gross profit, total costs, and net profit are presented in thousands of USD. The letter \(\times\) represents the capacity’s initial value.  
Figure 9: Representation of supply-to-hub quantities in scenario 5 shown in Table 7. Figure (**a**) shows the total sent tons, and (**b**) shows the classification of those delivered tons in the five food supply chains.  
hubs' capacity, the better use the vehicles' capacity. In terms of emission costs, there is a moderate increase. For example, tripling the hub's capacity increases the emission costs in 23%.
## 3 Results and Discussion
### Results of the Description Models on Spectral Features  
The 14 characteristic wavelengths and full spectral bands screened by the CARS-SPA method were inputted as parameters into the SVR and PLSR algorithms to construct a prediction model for the corn LNC, and the results are shown in Figure 11.  
Figure 11: Accuracy of the measured and predicted values of the validation set: (**a**) full spectrum -SVR model; (**b**) characteristic wavelength-SVR model; (**c**) full spectrum-PLSR model; (**d**) Characteristic wavelength-PLSR model.  
Figure 10: Correlation between selected sensitive spectral bands and \(n\) values.  
As seen in Figure 11, in the SVR model, the \(\text{R}\text{P}^{2}\) of the full-spectrum prediction model was 0.704, and the RMSEP was 0.248; the \(\text{R}\text{P}^{2}\) of the prediction model with the characteristic wavelength as the input variable was 0.807 and the RMSEP is 0.192. In the PLSR model, the \(\text{R}\text{P}^{2}\) of the full-spectrum model was 0.876, and the RMSEP was 0.143. The accuracy of nitrogen content prediction in both models was higher than that of the full-spectrum model because the use of the CARS-SPA algorithm could effectively reduce data redundancy and model processing complexity, remove the interference of a large amount of useless information on the correlation features, better represent the degree of light absorption by nitrogen groups in corn leaves, and improve the robustness of the model.  
Figure 11b,d shows the results of the prediction models based on sensitive wavelengths. It can be seen that the PLSR model has a better fit between the predicted and actual values with an improved \(\text{R}\text{P}^{2}\) of 0.1 and an improved RMSEP of 0.05, so the prediction based on the PLSR model was better than the SVR model. Table 4 shows the training results based on different features and models. It can be seen that the PLSR model outperforms the SVR model in terms of the complete spectral band as well as the characteristic wavelength as input variables, indicating that the PLSR model can further eliminate the influence of covariance variables on the model prediction and improve the model generalization ability.
## 4 Conclusions and Future Developments  
A procedure, based on advanced spatial analysis and geoprocessing tools, has been developed for mapping and assessing buildings seismic vulnerability. The proposed geospatial approach improves the effectiveness of disaster monitoring, management and awareness. In fact it facilitates analyzing the potential hazard due to a certain earthquake magnitude. A conceptual model has been developed for quickly managing the probable damage scenarios by using the implemented GIS architecture. In order to produce the thematic maps, PGA, \(I_{\text{MCS}}\), \(I_{\text{V}}\) and building characteristics (reported in census data) are the main parameters to detect the areas (subdivided in parcels) that would probably face serious problems as a consequence of total or partial collapse. The model described in this paper is fully functional and available on a regional scale. The advantage of the methodology implemented is that the system is open and additional data can be integrated as soon as new information is available. Further, VGI data and collaborative tools can contribute to a better emergency planning, providing fundamental information for immediate response when disaster occurs. The results become tools for an interactive DSS, which is able to support the public stakeholders to evaluate damages of buildings and lifelines and to address--in the post-event phase--activities related to emergency management. As a next step, the proposed system architecture will be enhanced by implementing a backup system, in order to manage and/or mitigate the effects potentially arising from a network failure (electricity, telecommunications, _etc._).
# Multiscale Dynamic Attention and Hierarchical Spatial Aggregation for Few-Shot Object Detection
## 4 Experiments  
### Datasets  
#### 4.1.1 Description of Datasets  
To evaluate the proposed framework, we conducted experiments on two widely used benchmark datasets: PASCAL VOC [32] and COCO [33].  
**PASCAL VOC**  
The PASCAL VOC dataset serves as a standard benchmark for object recognition and detection. We utilized the VOC2007 version, which comprises 9963 images and 24,640 annotated objects spanning 20 object categories. The dataset was partitioned into training, validation, and test sets. For our experiments, we designated 5 out of the 20 categories as novel classes, specifically used during the fine-tuning stage, while the remaining 15 categories were treated as base classes for the base-training stage.  
**COCO**  
The COCO dataset is another prominent benchmark, encompassing tasks such as object detection, segmentation, and image captioning. In this study, we employed the COCO 2014 version, containing over 200,000 images annotated with more than 80 object categories. Similarly, 20 categories were selected as novel classes for fine-tuning, while the remaining categories served as base classes for pre-training. This configuration aligns with the FSOD paradigm, enabling rigorous evaluation of the model's ability to generalize to unseen categories.
## 2 Materials and Methods
### Methods
#### 2.2.1 Data Processing  
The DEM data and the land cover data were transformed into the same projection as the remote-sensing image (UTM-zone49). The slope and elevation data were calculated using the DEM data, and the farmland area and residential area vector data were converted from the land cover raster using ArcGIS. Then, the GF-2 image was used as the base map and combined with visual interpretation to manually adjust and supplement the vector boundaries of the farmland area and residential area. The Landsat 8 data were of an L2 level and had undergone radiometric calibration and atmospheric correction. The remaining preprocessing steps for the remote-sensing images were as follows:  
1. Radiometric calibration and atmospheric correction: GF images were processed through the radiometric calibration tool \"Radiometric Calibration\" and the atmospheric correction tool \"FLAASH Atmospheric Correction\" in ENVI;
2. Resample: GF-6 and Landsat 8 images in the near-infrared band and red band needed to be resampled to a resolution of 2 m using the cubic convolution method;
3. Image registration: GF images and Landsat 8 images needed to be registered based on the GF-6 image from 26 November 2022;
4. Band math: _NDVI_ was calculated using GF-6 and Landsat 8 images, as follows: \[NDVI=\frac{NIR-R}{NIR+R}\] (1) where _NIR_ is the near-infrared band, and \(R\) is the red band of GF-6 or Landsat 8 images;
5. STARFM spatial-temporal fusion: The NDVI computed from GF-6 and Landsat 8 images, obtained at a similar time, and the NDVI computed from a Landsat 8 image, obtained at another time without a GF-6 image covered, were selected to predict the GF-6 resolution-like (resolution of 2 m) NDVI.
# Dynamic Downsampling Algorithm for 3D Point Cloud Map Based on Voxel Filtering
## 6 Discussion
### Practical Implications  
The practicality of our algorithm extends beyond its immediate performance benefits. By facilitating a faster and more intuitive downsampling of 3D point cloud data, the algorithm enables more efficient subsequent processing tasks, such as segmentation and classification. This efficiency can profoundly impact industries relying on 3D scanning and modeling technologies, where processing speed and data quality directly influence operational effectiveness and innovation capabilities.
## 3 Results
### Transient Thermal Conditions  
After the tests in steady condition, the dynamic behavior of the panel was investigated with a different testing procedure. The surface temperature of the panel increases rapidly until reaching a stable average value of 86 \({}^{\circ}\)C. Measuring the temperature increase with a contact temperature probe could have instead lead to underestimation or overestimation of the temperature. A contact probe is representative of a single point of the panel, but the heating, even on small panel, is not homogenous. Only the use of infrared thermography overcomes this issue, allowing the evaluation of the entire heating surface. Figure 5 shows the average temperature increase of the surface, taking into account the environment temperature recorded during the test, which was close to 18 \({}^{\circ}\)C. The experimental temperature profile is compared against the mathematical model, as described in Section 2.2. The curve is obtained fitting the measured data with the least square method. The root mean squared error of the fit is equal to 0.32 [K].  
Figure 4: Experimental data of the steady state measurement. The heating power generated by the electrical panel (red dashed line) is balanced by the radiant ceiling (blue solid line).  
The model allows the estimation of the parameters \(\alpha\) and \(\tau\) described in Equation (13). The obtained values are, respectively, 67 K and 144 s. The \(\alpha\) value, considering the declared power of the panel of 300 W, gives an estimated value close to 15 W m\({}^{-2}\) K\({}^{-1}\) for the total heat exchange coefficient, which is compatible with the mathematical model. Due to the nature of the electric panel, the value of the time constant \(\tau\) value is lower than the ones calculated for several hydronic systems available in the literature [30].  
Infrared measurement gives also information on the surface temperature distribution. From the thermal image (Figure 6) obtained at the end of the test, it is possible to note that some zones of the panel, in correspondence to the cabling system, differ from the average temperature.  
Figure 5: Average surface temperature increase of the panel during the transient testing.  
Figure 6: Thermal image obtained at the end of the transient measurement.
# Investigation of Crack Propagation and Healing of Asphalt Concrete Using Digital Image Correlation
## 2 Materials and Methods  
### Specim Preparation  
The specimens were manufactured in the laboratory according to EN 12697-35:2016 [31] and compacted according to EN 12697-31:2007 [32] using a Gyratory Compactor. In this research, a typical asphalt concrete mixture with a maximum aggregate size of 14 mm (AC14) was investigated. In total, three identical cylindrical specimens (S1, S2, S3) with a diameter of 100 mm and a thickness of 40 mm were made. The asphalt mixture contained 4.3% of bitumen by mass of aggregates. A common 35/50 penetration grade bitumen was used. Table 1 shows the basic properties of the used bitumen and in Table 2 the composition of the mixture is presented. Figure 1 shows the final grading curve of the mixture.  
When using DIC, the surface of the specimen should be painted by some random speckles. This speckle pattern strongly influences the accuracy of the strain field measurements [33]. Therefore, to have a better accuracy in measurements on asphalt, usually, a thin white paint overprinted by a speckle pattern of black is applied on the surface, resulting in a homogeneous randomly oriented  
\begin{table}
\begin{tabular}{c c} \hline \hline Penetration (1/10 mm) & 37 \\ Softening point (\({}^{\circ}\)C) & 54.3 \\ Fraass breaking point (\({}^{\circ}\)C) & \(-\)10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic properties of the 35/50 penetration grade bitumen.  
\begin{table}
\begin{tabular}{c c} \hline \hline Limestone 6.3/14 & 39.8\% \\ Limestone 2/6.3 & 14.0\% \\ Limestone 0/2 & 30.0\% \\ River sand 0/1 & 7.5\% \\ Filler (filler 15) & 8.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mixture composition (by aggregate mass).  
texture [34]. However, this treatment will also cover the aggregate and asphalt distribution information, which is important for local damage identification. Since the natural surface of asphalt mixture includes natural speckles due to different materials inside [35], in this research, only a random black speckle pattern was added to the already existing pattern on the surface. Using this method, the cut surface of the specimen remains visible for further analysis of the crack's growing path according to the aggregate-binder structure of the specimen.  
After the paint had dried, two stripes were glued on the specimens according to the standard to hold two LVDTs to measure the horizontal deformation during the experiments. Then the specimens were conditioned inside the climate chamber of the universal testing machine (UTM).
## 1 Overview of BSNs  
A confluence of advancements in diverse areas of research, including device integration, energy storage, sensor technology, and wireless communications, have facilitated the creation of body sensor networks (BSNs). BSNs--networked body area sensor nodes that continuously capture objective measures of human physiology and performance both inside and outside of traditional healthcare settings [1]--are making an impact in numerous applications and enhancing the possibilities of wireless health systems. For example, the TEMPO (Technology Enabled Medical Precision Observation) BSN platform (Figure 1) at the University of Virginia is an integral component of three clinical studies related to movement disorder assessment, including tremor in Parkinson's Disease patients, physical agitation in dementia patients, and fall risk in the elderly [2]. The TEMPO platform sensor node integrates MEMS inertial sensors (linear acceleration and rotational rate in six-degrees-of-freedom), a mixed-signal processor, Bluetooth wireless transmission, and a lithium battery in a wearable enclosure. BSNs like TEMPO are helping clinicians improve healthcare assessment accuracy and precision for better diagnosis, treatment, and assistance of movement disorders.  
BSNs have made a significant impact in healthcare applications because of intrinsic and unique capabilities of the technology--networked body area sensor nodes capture and process precise and accurate data continuously and non-invasively. With manifold sensing opportunities paired with healthcare information technology infrastructure, body sensor networks also enable longitudinal and naturalistic monitoring, enhancing the assessment of human physiology and performance both inside and outside of traditional healthcare settings. Thus, BSNs address the weaknesses of traditional patient data collection, such as imprecision (qualitative human observation) and under-sampling (infrequent assessment). Beyond assessment, BSNs are finding use in medical actuation. For example, an artificial pancreas can regulate blood glucose via implanted biochemical sensors located in interstitial fluid [3]. Moreover, future applications of deep brain stimulation, pacemaker regulation, drug delivery, and prosthetic control will be made possible by body area sensor networks that can provide real-time assistance based on real-time assessments. Finally, BSNs are creating opportunities in the telehealth domain [4]. As medicine moves beyond the confines of hospitals and clinics, technological infrastructure will be necessary in the home. BSNs are poised to be primary enablers to this movement. While BSNs open new horizons to healthcare delivery, such technology is also helping to define new entertainment and wellness applications in the commercial space.