esa-sceva/satcom-chunk-collection huggingface dataset:
# Precision Manufacturing of Patterned Beryllium Bronze Leaf Springs via Chemical Etching
## 4 Experimental Results
### Leaf Spring Performance  
Leaf spring is a key component of a micro/nano probe and determines the measurement precision of the probe to a great extent. The leaf spring shown in Figure 1 was manufactured by chemical etching method and applied to our probe [4]. Both the floating plate and the supporting ring connected to the leaf spring have a dimensional tolerance of 0.1 \(\upmu\)m. The assembling accuracy of the probe on the leaf springs are controlled by the manufacturing accuracy since the symmetric design of the leaf spring and its connected attachments. An experimental setup shown in Figure 10 were built and the probe was tested. The probe was assembled in a frame of stand. A 2 mm \(\times\) 2 mm square hole formed by four 0 grade gauge blocks with same horizontal sizes of 30 mm \(\times\) 9 mm and thicknesses of 2 mm, 2 mm, 1.6 mm and 1.7 mm respectively, was used to contact the probe tip in lateral directions. As for the inner surfaces of the square hole, two parallel surfaces of them are the reference planes of the gauge block but the other two are not. Only the reference planes of the gauge block were used to contact the probe tip in the experiment. Therefore, rotation for the probe is required when changing a contact direction in horizontal. A round dial with an accuracy of 0.5 degree was equipped on the rotating shaft of the probe, so as to index the rotated angle of the probe. A 2D linear stage was used to change the initial position of the square hole manually. A high-precision 3D nano-positioning stage made by Physik Instrumente (2013, PI, model P-561.3 CD with a repeatability of 2 nm and a distance of travel of 100 \(\upmu\)m in each direction, Germany) was used as a reference with an interval of 2 \(\upmu\)m. 10 measurements were repeated at each point. The results (X2 and Y2) were shown in Figure 11. From Figure 11, we can see that the probe's measurement standard deviation is about 16 nm.  
In comparison, the same leaf spring was manufactured by wire-EDM, used in the same probe and similar experiments were conducted by the same experimental setup. The results (X1 and Y1) were also shown in Figure 11. In this case, the measurement standard deviation of the probe is about 30 nm. Compared with the leaf springs fabricated by the proposed method, the ones fabricated by EDM have  
Figure 10: Photograph of the experimental setup.  
evident thermal deformation and poor edge uniformity, which cause an anisotropic and lead to worse measurement standard deviation of the probe. The experimental results demonstrate that the chemical etching method is a better choice in fabricating high-precision leaf springs for a micro/nano probe.
# Predicting Topology Propagation Messages in Mobile Ad Hoc Networks: The Value of History
## 5 Evaluation of OLSR-HBP in Mobile Scenarios  
In order to present the evaluation of OLSR-HBP in mobile scenarios, we first describe the experimental framework used in the simulations and the scenarios involved on it. Then, we present the results obtained by the predictor in the previous settings, and particularly analyse the control traffic decrease and memory overhead. Finally, we determine the actual impact of using this predictor in mobile networks considering the data transfer capabilities and also the energy consumption in the network. This impact was determined comparing the network performance when using the regular OLSR protocol, and then adding it the proposed predictor.
# Unsupervised Learning for Concept Detection in Medical Images: A Comparative Analysis
## 1 Introduction  
In an era of a steadily increasing use of digital medical imaging, image recognition poses an interesting prospect for novel solutions supporting clinicians and researchers. In particular, the representation learning field has grown fast in recent years [1], and many of the breakthroughs in this field are occurring in deep learning methods which have also been strongly considered in healthcare [2]. Leveraging representation learning tools to the medical imaging field is feasible and worthwhile, as they can provide additional levels of introspection of clinical cases through content-based image retrieval (CBIR).  
While multiple initiatives for the provision of medical imaging data sets exist, the process of annotating the data with useful information is exhaustive and requires medical expertise, as it oftenails down to a medical diagnosis. In the face of few to no annotations, unsupervised learning stands as a possible means of feature extraction for a measurement of relevance, leading to more powerful information retrieval and decision support solutions in digital medical imaging.  
Although unsupervised representation is limited for specific classification tasks when compared to supervised learning approaches, the latter requires an exhaustive process from experts to obtain annotated content. Unsupervised learning, which avoids this issue, can also provide a few other benefits, including transferability to other problems or domains, and can often be bridged to supervised and semi-supervised techniques. We hypothesize that a sufficiently powerful representation of images will enable a medical imaging archive to automatically detect biomedical concepts withsome level of certainty and efficiency, thus improving the system's information retrieval capability over non-annotated data.  
In this work, we present an assessment of unsupervised mid-level representation learning approaches for images in the biomedical domain. Representations are built using an ensemble of images from existing biomedical literature. The learned representations were validated with a brief qualitative feature analysis and by training simple classifiers for the purpose of biomedical concept detection. We show that feature learning methods based on deep neural networks can outperform techniques that were previously common-place in image recognition, and that models with adversarial networks, albeit harder to train, can improve the quality of feature learning.
## 2 Methodology
### Test Procedure  
The main test procedures are shown in Figure 6, and the specific steps are set out below:  
1. In order to ensure that the density of soil in the container is consistent, a certain mass of soil is compressed into a certain volume. In this study, the soil density is 1.7 g/cm\({}^{3}\); each layer of the soil is compressed to 2 cm, because the container length is 70 cm; width is 36 cm; and each layer of the soil is compressed to 5040 cm\({}^{3}\), so it can be calculated that each layer requires a soil mass of 8568 g. First, 8568 g of soil was weighed and placed in plastic bags, then a shovel was used to spread the soil evenly into the container, and finally a vibrator was used to compress the soil to 2 cm; the operation was repeated 20 times and the soil was filled to 40 cm high. Then the hole was drilled according to the pile position; the position of the model piles are shown in Figure 7. The depth of the hole was 30 cm; the aperture was slightly smaller than the pile diameter, so that the shaft resistance between the pile and soil was closer to the actual value. Finally, the pile was pressed into the hole.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & **Density** & **Compressive Modulus** & **Moisture** & **Cohesion \(c\)** & **Internal Friction Angle** \\  & **(g/cm\({}^{3}\))** & **(MPa)** & **Content \(\omega\) (\%)** & **(kPa)** & **\(\varphi\) (\({}^{\circ}\))** \\ \hline Loess & 1.7 & 26.9 & 13.5 & 27 & 21 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Physical properties of loess.  
Figure 5: Tests: (**a**) Oedometer test; (**b**) Moisture content test; (**c**) Direct shear test.  
Figure 6: The main test procedures.  
2. The container was put into the centrifuge by hoisting machine.
3. First, the displacement meter was mounted on the inverse force frame to measure top displacement. Then the reverse force frame was fastened to the container by bolts. Finally, strain gauges, earth pressure cells and the displacement meter were connected with the centrifuge channel, which can obtain the data from the computer.
4. Five minutes preliminary operation at the 100 g level was conducted.
5. The test data was transferred to the computer.
6. Load was increased after one load ended, and then the steps above were repeated. Vertical loading was achieved by adding steel plates to the loading platform at the top of the model pile, and the vertical loading was divided into eight levels; the weight of the loading platform itself can be considered as the first stage load, the loading size is 225 N, 450 N, 675 N, 900 N, 1125 N, 1350 N, 1575 N, and 1800 N. The loading device is shown in Figure 8.
## 2 Materials and Methods
### Evaluation Metrics
#### 2.4.2. Global Accuracy (GA)  
Global Accuracy (GA) represents the proportion of correctly classified pixels over the total number of pixels in all classes. It is computed as:  
\[GA=\frac{\sum_{i=1}^{N}TP_{i}}{\sum_{i=1}^{N}TP_{i}+FP_{i}+FN_{i}+TN_{i}}, \tag{2}\]  
where \(TP_{i}\), \(FP_{i}\)\(FN_{i}\)\(TN_{i}\) are the number of true positives, false positives, false negatives, and true negatives for class \(i\) and \(N\) is the total number of classes.
# Advanced Hybrid Beamforming Technique in MU-MIMO Systems
## 2 System Model
### 3-Dimension Spatial Channel Model  
Figure 3 shows 3-dimension spatial channel model (3D SCM) [14; 15]. The SCM is a 3D geometric probability model that describes the environment scattered by clutter in the azimuth and elevation angles between the transmitter and the user. Additionally, the 3D SCM is applied to the hybrid beamforming system to increase the reliability of the communication environment in the actual 3-dimensional space. The equation of 3D SCM is as follows,  
\[\mathbf{H}_{3D}=\sqrt{\frac{N_{t}N_{r}}{M}}\sum\limits_{m=1}^{M}\mathbf{a}_{r}( \phi_{m}^{r},\,\theta_{m}^{r})\mathbf{a}_{t}(\phi_{m}^{t},\,\theta_{m}^{t})^{ H}, \tag{3}\]  
where \(M\) denotes the number of multi-paths by scattering of the signal. \(\mathbf{a}_{t}(\phi_{m}^{t},\,\theta_{m}^{t})\) denotes steering vector for azimuth and elevation angles at the transmitter. Also, \(\mathbf{a}_{t}(\phi_{m}^{r},\,\theta_{m}^{r})\) denotes steering vector for azimuth and elevation angles to the user.  
Figure 1: The architecture of fully connected structure hybrid beamforming system.  
Figure 2: The architecture of partially connected structure hybrid beamforming system.  
Figure 4 shows the structure of antenna arrays connecting the RF chain [16; 17; 18; 19; 20]. If the distance is far between the antenna arrays and the users, the same azimuth and elevation angles are radiated for all antennas. The matrix in the analog precoder is denoted as follows,  
\[\mathbf{V}_{RF}=diag\left\{\mathbf{a}_{t}(\phi_{1},\,\theta_{1}),\,\mathbf{a}_ {i}(\phi_{2},\,\theta_{2}),\,\cdots,\,\mathbf{a}_{t}(\phi_{N_{t}^{RF}},\, \theta_{N_{t}^{RF}})\right\}, \tag{4}\]  
where \(\mathbf{a}_{t}(\phi_{i},\,\theta_{i})\) denotes the steering vector connecting the \(i\)-th RF chain and \(\mathbf{V}_{RF}\) denotes the diagonal matrix with the elements of \(\mathbf{a}_{t}(\phi_{i},\,\theta_{i})\). \(\mathbf{a}_{t}(\phi_{i},\,\theta_{i})\) is denoted as follows,  
\[\mathbf{a}_{t}(\phi_{i},\,\theta_{i})=\frac{1}{\sqrt{N_{t}}}e^{-\frac{2\pi}{ \lambda}\mathbf{r}_{i}\mathbf{d}}, \tag{5}\]  
\[\mathbf{r}_{i}=\left(\begin{array}{c}\sin\theta_{i}\cos\phi_{i}\\ \sin\theta_{i}\sin\phi_{i}\\ \cos\theta_{i}\end{array}\right), \tag{6}\]  
\[\mathbf{d}=\left(\begin{array}{c}s_{x}\\ s_{y}+(p-1)d_{H}\\ s_{z}+(q-1)d_{V}\end{array}\right), \tag{7}\]  
where \(\mathbf{r}_{i}\) denotes spherical unit vectors expressed in Cartesian coordinates for the \(i\)-th RF chain and \(\mathbf{d}\) denotes location vectors of antenna elements. \(p\) denotes the number of antenna elements in the horizontal direction and \(q\) denotes the number of antenna elements in the vertical direction. \(d_{H}\) denotes interval of the elements in the horizontal direction and \(d_{V}\) denotes interval of the elements in the vertical direction.  
Figure 4: The structure of antenna arrays.  
Figure 3: 3-dimension spatial channel model.
# Optimal Control Method of Semi-Active Suspension System and Processor-in-the-Loop Verification
## 3 Results  
### Simulation Results  
The passive suspension system and the SASS were analyzed to reveal the superiority of the controller for the simulations. The simulation studies were conducted utilizing a random Type-C road profile in accordance with ISO 8608 standard for a quarter car model. The road spanned a length of 100 m, while the vehicle was traveling at a speed of 20 m/s. The results of the SASS were evaluated and compared with the passive suspension system results concerning sprung mass acceleration, displacement, suspension deflection, and DLC.  
The Type-C road input is shown in Figure 13. The sprung mass displacements and the accelerations of the passive and the SASS are shown in Figures 14 and 15, respectively. The other evaluation parameter is suspension deflection, and it can be seen from Figure 16. According to Equation (10), the DLC values for SASS and passive suspension system were calculated as 0.7401 and 1.069 respectively. As seen from these results, the DLC was improved by 30.77%. When all these results are analyzed, the SASS parameters are highly improved compared to the passive system. This shows that the developed controller has good performance for the random input.  
For the verification of the control algorithm, a quarter car model was used to test the SASS in PIL simulation. The test scenario was a bump to the model 20 ms after the simulation started as seen from Figure 17. The sprung mass displacement of the passive and the SASS obtained from this scenario is shown in Figure 18. In the SASS, it was observed that the sprung mass displacement moved about 23% less. In addition, the acceleration caused by the displacement of the sprung mass after the bump was applied is shown in Figure 19. Similar to Figure 18, the SASS had about 23% less acceleration at the first moment of bump application. The other parameter of the quarter car model, suspension deflection, is shown in Figure 20. It can be seen that the suspension deflection is similar. The control algorithm of the system was validated by obtaining zero error for the difference between model-in-the-loop (MIL) and processor-in-the-loop (PIL).  
Figure 16: Suspension deflections for random Type-C road profile.  
Figure 15: Sprung mass accelerations for random Type-C road input.  
Figure 17: Bump road input.  
Figure 18: Sprung mass displacements for the bump input.  
Figure 19: Sprung mass accelerations for the bump input.  
Figure 20: Suspension deflection for passive suspension system and SASS.
# Massive Data Capture Approach for Modeling Existing Building Stocks
## 2 Methodology  
As presented in Figure 1, the new approach has four main steps: data capture (S1), data processing (S2), mesh optimization (S3), and BIM preparation (S4). The first two steps capture and process data separately for the studied building and for the site, which includes the terrain and the surrounding buildings. S3 focuses exclusively on the analyzed building, and S4 combines the building and site information. The main novelty of this approach is the data preparation for the 3D reconstruction, which is carried out in an optimized way to overcome the weaknesses of previous methods. These four steps aim to move forward from the current data capture and processing methods and to achieve greater speed and flexibility, as explained in the Introduction. This improvement is expected to be achieved by means of automatization and enhanced compatibility of the technologies required in the four steps. Compared to former similar approaches [26, 44], the first step is optimized by reducing the required time and preparation on site. The data-processing step introduces new own-developed algorithms to achieve automatization, great precision, and fast results. Mesh optimization improves the quality of the final results by introducing algorithms that have been developed recently by the authors' research group. Finally, the resulting model is prepared by merging, checking, and correcting the georeferenced data using the BIM software tool, Revit 2023. This research paper validates the proposed approach and its improved performance using data acquired from the real case study presented in Section 3.
## 3 Methodology
### Artificial Neural Network  
ANNs are computer-based mathematical models that are inspired by human brains that use continuous and parallel layers. These layers consist of nodes that are interconnected through weights [70]. These interconnected weights create associations amid the input layer and output layer of every node [50]. In the current study, unsupervised vector quantization (UVQ) was used for characterizing the faults and fractures. The UVQ used non-linear filtering and consisted of three steps: initialization, training, and application. The initialization evaluated the set of the classes, training improves these set of classes, and the application was used for the classification of the data. The number of classes was hard to determine in advance. Too many classes would have given redundant class centers, but too few classes would not have matched the seismic. Therefore, different quality checks were performed using different classes to proceed with the training.  
In unsupervised training, network performance is tracked in a graph that shows the average match (confidence) of clustered input. Typically, the average match increases in a step-function. Each step indicates that the network has found a new cluster. Training can be stopped as soon as the average match has reached a stable situation (a straight line is obtained). Training that results in maximum match is accepted and is used to make the output maps.  
Unsupervised classification maps are an advanced method that represents vectors that utilize a set of classes that lack user control [71]. The representing vector uses integrated, seismic attribute values that act as test data sets. Since the structural features are associated with erosional surfaces, reflection terminations, the variation of dip orientations, and changes in the amplitude signal, a set of seismic attributes are chosen as the input, which is very useful in identifying the network of discontinuities as an output.  
Figure 2: The workflow implemented for the current study comprised four steps; the first was data-conditioning, the second was the selection of attribute computation, the third was artificial neural network (ANN) computation, and the fourth was ant colony optimization (ACO) computation.
## 5 Societal Relevance and Significance  
In this study, we have proposed a machine learning approach that utilizes an ensemble of low-cost sensors together with remote-sensed SR data to accurately estimate wavelength-resolved SSI comparable to observations made by a research grade spectrometer. The model's performance on the testing data demonstrates its ability to estimate SSI with reasonable accuracy across the wavelength range of 360 nm to 780 nm.  
As discussed in Section 1, SSI shows spatiotemporal variability across multiple orders of magnitude, driven by factors such as cloud fields, atmospheric scattering, and absorption by gas molecules, aerosols, and surface elements like vegetation, etc. Studies [23; 60] show that the variability in SSI can be on scales as small as seconds or meters. Drawing on a decade of high-resolution SSI data and cloud size distribution modeling to study how cloud size distributions drive the spatiotemporal scales of irradiance variability, ref. [23] demonstrated that fully modeling the variability in SSI requires resolving down to scales as fine as tens of meters. However, achieving this level of spatial resolution using conventional ground-based radiometers is impractical due to cost constraints. Also, in practice, even the most detailed operational weather models run at a resolution of around 1 km [60]. Many remote sensing products providing SSI operate at scales of several kilometers. For example, the satellite-based SSI product Heliosat (SARAH) [26] typically provides data at spatial resolutions corresponding to a 0.05\({}^{\circ}\) latitude, longitude grid, and the Copernicus Atmosphere Monitoring Service (CAMS) Radiation Service [24; 25] operates on a 0.1\({}^{\circ}\) latitude, longitude grid. Therefore, these are inadequate to capture SSI at the required scales. Our approach provides a potential alternative to address this, since due to their affordability, the low-cost sensor modules can be practically deployed at the hyperlocal scale bypassing the cost constraints introduced by conventional spectroradiometers. By hosting the trained model on an edge device and integrating it with satellite-derived SR data, SSI can be estimated using real-time readings from the sensor ensemble alongside the remotely sensed inputs. Since the SR data are available at a temporal frequency of five days, and each sensor module will typically be deployed at a fixed location, this eliminates the need to query SR data for every individual reading. Instead, the SR data can be retrieved once for the specific location and reused until the next satellite revisit.