esa-sceva/satcom-chunk-collection huggingface dataset:
# A Low-Cost Contactless Overhead Micrometer Surface Scanner
## 1 Introduction  
The generation of image mosaics out of partial images of a surface is a useful task in many applications. Mosaics are useful because they image a larger amount of surface than a single image does. If a mapping between pixel and metric coordinates is achieved, then world measurements can be performed using the mosaic, much like in cartographic maps. Image registration upon general surfaces enables photorealistic maps for Geographical Information Systems; photopanoramas [1]; and also specialized mosaics from usually unseen surfaces such as the inner of pipes [2], the gastrointestinal tract [3], and the human retina [4]. At the core of all methods for image mosaic generation is the problem of image registration.  
The simplest case of mosaic generation is met when imaging a planar surface, by multiple and conveniently tessellated overlapping frontal views. Just this case is useful in several domains, such as remote sensing [5], document scanning [6], bioinformatics [7, 8], art [9], and others. Approaches to this problem that are based purely on visual cues are continuously making progress but, given pixel quantization, they exhibit error. For a large number of images, this error accumulates and gives rise to distortions. For this reason, applications that require large mosaics make use of independent information about the location of the camera. For example, photorealistic stitching of remote sensing and aerial images is supported by GPS measurements. We use this principle in the context of overhead scanning, where approximate location measurements are available from the motion mechanism of the scanner.  
The optical resolution of scanners is measured by the number of pixels, or points, per unit area. We use the equivalent of dots per inch--that is, points per inch (ppi). The ppiis a 1D metric that denotes the resolution of points across a line and, thus, a resolution of 100 ppi means that in the scanned image, \(100\times 100=10\) Kp would be devoted for a surface area of 1 in \(\times\) 1 in = 1 in \(\mathrm{m}^{2}\). It is equivalent to say that the scanned image has a resolution of 10 Kp/\(\mathrm{in}^{2}\) (number of points/per square inch).  
In this work, a contactless, flatbed scanner, which costs less than 1000 USD, offers 19754 ppi resolution, and has a scanning area of 33 \(\times\) 37 cm\({}^{2}\), is proposed. Accurate surface mosaics are obtained based on a novel image acquisition and image registration approach that actively seeks registration cues by acquiring auxiliary images and utilizing proprioceptive data in correspondence and registration tasks. The implementation in a device with accompanying software is presented, provided as an open prototype, and evaluated qualitatively and quantitatively.
## 8 Conclusions and Future Work Plan  
### Conclusions  
In order to achieve a continuous obstacle-avoidant picking of multi-target clustered crabapples on the tree, and avoid collision with the tree branches during the picking process, the picking sequence and obstacle avoidance plan for picking clustered crabapples on trees were studied to improve the efficiency of picking and save picking time.  
1. The problem of picking the sequence of multiple crabapples on the tree was simplified to the TSP in three-dimensional space. The ACO algorithm was improved by improving the transition probability and optimizing the pheromone concentration update. Compared with the traditional ACO algorithm, it can be concluded that the improved ACO algorithm has shorter picking sequence paths, fewer iterations, faster convergence speed, and can effectively plan the picking sequence of multiple crabapples on the tree.
2. To avoid collisions between the robotic arm and the tree branches during picking progress, which may cause damage to the robotic arm and the trees, the APF algorithm and the RRT algorithm were combined, and a gravitational field was introduced to enhance the directional guidance of the RRT algorithm's path search. Compared to the original RRT algorithm, it can be concluded that the improved RRT algorithm has fewer turning points and faster convergence speed and can achieve an obstacle-avoidant picking path planning for the robotic arm.
3. To verify the effectiveness and feasibility of the algorithm proposed in this article, a picking test platform was constructed to conduct multiple groups of picking tests on clustered crabapples (1 to 6 clusters) on an artificial simulation crabapple tree. The test results show that the average recognition success rate is 95.83%, the average positioning success rate is 93.75%, and the average picking success rate is 89.58%. Moreover, there are no collisions between the robotic arm and the main branches of the tree during the picking process, and the robotic arm can pick the crabapples according to the planned picking sequence with no collision, meeting the actual picking requirements and achieving the goal of picking.
## 5 Discussion  
To improve the accuracy of cold wave event recognition and enhance wind power forecasting under cold-wave conditions, this paper proposes a recognition method for cold wave events and a day-ahead wind power forecasting model for wind farm clusters.  
* Starting with the impact of cold wave weather on wind power, this paper establishes the discriminant criteria for cold wave events and extracts key features through an in-depth analysis of historical data, providing a basis for subsequent cold wave events recognition.
* Aiming to address the recognition problem of small-sample cold wave events, an improved multi-modal U-Net classification model is proposed. Cold wave events samples generated by this model are added to the training set, significantly enhancing the classification model's ability to recognize cold wave events. The model utilizes future NWP to accurately forecast the occurrence of cold waves, providing an effective early warning for the power system to handle extreme weather events.
* Based on cold wave recognition, this paper establishes distinct wind power forecasting models for the cold-wave and non-cold-wave seasons, respectively. Compared with the experimental results of different classification models, the Ns-Transformer model demonstrates superior wind power forecasting accuracy under cold-wave conditions.  
The proposed method emphasizes the consideration of wind power forecasting under cold wave conditions. To further improve the accuracy of wind power forecasting under extreme weather, future work should consider additional extreme weather events, such as strong winds and heavy snow.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Model** &
\begin{tabular}{c} **Cold Wave** \\ **Characteristics** \\ \end{tabular} & **Data Generation** & **Continuity Correction** \\ \hline Method I & Transformer & ✓ & ✓ & ✓ \\ Method II & & ✓ & & \\ Method III & & ✓ & & \\ Method IV & Ns-Transformer & ✓ & ✓ & \\ Proposed Method & & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 7: Content of various methods for ablation experiments.  
Figure 16: Forecasting metrics of different methods in the ablation experiment.
## 2 Human Microbiome
### Microbiome Types
#### 2.1.2 Nasopharynx  
As previously mentioned, each region of the human body provides a different ecological niche that influences the establishment of a normal microbiome, which typically remains constant. In the upper respiratory tract, although the majority of microorganisms inhaled through the air get trapped in the nasal passages and are expelled through nasal secretions, there is a restricted group of microorganisms that colonize the surfaces of deeper mucosal tissues. A prime example of this is the fact that the nasopharynx harbors the highest number of inhaled microorganisms, which become trapped in this region [42].  
The microbiome of the nasopharynx contains the same species found in the oral microbiome, along with others such as the genera _Staphylococcus_, _Neisseria_, and _Corynebacterium_[42; 43].  
In individuals with a considered-normal nasopharyngeal microbiome, it is common to find some potentially pathogenic microorganisms that, however, do not cause host disease. This is because other commensal microorganisms compete with the pathogens, preventing their progression [44]. Additionally, the host's immune system is particularly active on the mucosal surfaces, helping to inhibit the growth of these pathogenic microorganisms. Examples of such microorganisms include _Streptococcus pneumoniae_, _Haemophilus influenzae_, and _Mycoplasma_, which do not have a commensal relationship with the host [45].  
In the lower respiratory tract, specifically the bronchi and pulmonary alveoli, it is not common to find bacteria, as they cannot easily reach these areas. If they do manage to reach them, there are first-line defenses, such as alveolar macrophages, ready to avoid any potential colonization [46].
## 2 Materials and Methods
### Methods
#### 2.2.1 Flow Calibration
\[\mathrm{NSE}=1-\left[\frac{\sum_{i=1}^{n}\left(Q_{i}^{\mathrm{obs}}-Q_{i}^{ \mathrm{sim}}\right)^{2}}{\sum_{i=1}^{n}\left(Q_{i}^{\mathrm{obs}}-\overline {Q^{\mathrm{obs}}}\right)^{2}}\right] \tag{5}\]  
\[\mathrm{PBIAS}=\left[\frac{\sum_{i=1}^{n}\left(Q_{i}^{\mathrm{obs}}-Q_{i}^{ \mathrm{sim}}\right)*100}{\sum_{i=1}^{n}\left(Q_{i}^{\mathrm{obs}}\right)^{2} }\right] \tag{6}\]  
\[\mathrm{R}^{2}=\frac{\left(\sum_{i=1}^{n}\left(Q_{i}^{\mathrm{obs}}-Q_{i}^{ \mathrm{sim}}\right)\left(Q_{i}^{\  
Model Setup  
The model implemented follows the methodology adopted by [PERSON] et al. [60]. The Lobo watershed in Nibeinbe covers an area of 6442.66 km\({}^{2}\). The delimitation of the watershed and the extraction of the hydrographic network were possible thanks to the processing of the DEM under QSWAT. The D8 algorithm [61] integrated into QGIS makes it easy to extract watershed boundaries and river networks using QSWAT. This step is followed by the integration of meteorological data, land use, pedology, and their physico-chemical properties. Then, the hydrological response units (HRU) combining land use type, soil type, and slope in a sub-basin were calculated. HRUs are the basis for calculating the SWAT model. They highlight the differences in evapotranspiration and other hydrological conditions between different types of land cover. The multiple HRU method of the SWAT model made it possible to constitute 163 HRUs. After that, QSWAT established the various input data tables. This made it possible to integrate information relating to the watershed, pedology, hydrometeeorology, the HRUs themselves, water resources and their use, watershed management, wetlands, septic tanks, different reservoirs on the watershed, and the watershed master file. At this stage, the hydrological simulation of the Lobo watershed can be done. Streamflow Calibration Process  
The flow calibration at a monthly time step was possible by using observed data at the Nibeinbe hydrometric station using the SUFI-2 method [37; 38] in the SWAT-CUP program [62]. For this study, ten (10) flow parameters have been used. These parameters were the CN2 expressing the humidity conditions II, the quantity of water available in the soil SOL_AWC, the ESCO factor which translates the compensation coefficient of the soil evaporation, RCHRG_DP which is the fraction of percolation water from the deep aquifer, the re-evaporation coefficient of groundwater GW_REVAP, ALPHA_BF (Baseflow alpha factor), the water depth threshold in the shallow aquifer depth GWQMN required for return flow to occur, the water depth threshold in the shallow aquifer REVAPMN for revaporation to occur, the groundwater delay time GW_DELAY, the CANMX (Maximum storage of canopy). Flow calibration was performed over the period 1981 to 1994. A total of 500 iterations were performed during the monthly water flow simulation process. Then, the Nash-Sutcliffe coefficient (NSE), the percentage of bias (PBIAS), and the R\({}^{2}\) determination coefficient were calculated in order to judge the robustness of the SWAT model on this watershed.
# Adversarial Machine Learning for NextG Covert Communications Using Multiple Antennas
## 4 Adversarial Perturbations Using Multiple Antennas at the CJ
### Elementwise Maximum Channel Gain (EMCG) Attack  
Unlike the previous attacks that considered the channel gain of the channel vector with dimension \(p\times 1\) as a way to allocate power among antennas, the EMCG attack considers the channel gain for each time instance to fully utilize the channel diversity as shown in Figure 2b. First, the CJ compares the channel gains elementwise and selects one antenna that has the largest channel gain at each instance. Specifically, the CJ finds and transmits with the antenna \(j^{*}=\underset{j=1,\cdots,d}{\text{max}}\,\{\|h_{ar_{j},t}\|_{2}\}\) that has the largest channel gain at instance \(t\). Furthermore, a virtual channel \(h_{vir,t}\) at instance \(t\) is defined as the channel with the largest channel gain among antennas which is \(h_{ar_{j},t}\). Then, the adversary generates the perturbation \(\delta^{vir}\) with respect to \(\boldsymbol{h}_{vir}=[h_{vir,1},\cdots,h_{vir,p}]^{T}\) using the MRPP attack and transmits each element of \(\delta^{vir}\) with the antenna that has been selected previously. The details are provided in Algorithm 3.  
```
Inputs: input \(\mathbf{r}_{le}\), desired accuracy \(\varepsilon_{acc}\), power constraint \(P_{max}\), and model of the classifier \(L(\mathbf{\theta},\cdot,\cdot)\). Initialize: \(k\leftarrow\mathbf{0}^{\
u\times 1}\), \(\mathbf{\delta}_{i}\leftarrow\mathbf{0}^{p\times 1}\) for \(\forall i\). for\(i=1\) to \(p\)do \(h_{virt,j}=\max\{\|h_{c_{1}e_{j}}\|_{2},\cdots,\|h_{c_{m}e_{i}}\|_{2}\}\) \(k[i]=\arg\max\{\|h_{c_{1}e_{j}}\|_{2},\cdots,\|h_{c_{m}e_{j}}\|_{2}\}\)  end for  Virtual channel : \(\mathbf{H}_{vir}=\text{diag}\{h_{vir,1},\cdots,h_{vir,p}\}\) \(\varepsilon_{max}\leftarrow\sqrt{P_{max}},\varepsilon_{min}\gets 0\) \(\delta=\frac{\mathbf{H}_{vir}^{*}\
abla_{x}L(\mathbf{\theta}_{irt,y}\mathbf{\theta}_{ irt,y}^{target})}{(\|\mathbf{H}_{vir}^{*}\
abla_{x}L(\mathbf{\theta}_{irt,y}\mathbf{\theta}_{ irt,y}^{target})\|_{2})}\) while\(\varepsilon_{max}-\varepsilon_{min}>\varepsilon_{acc}\)do \(\varepsilon_{avg}\leftarrow(\varepsilon_{max}+\varepsilon_{min})/2\) \(\mathbf{x}_{adv}\leftarrow\mathbf{x}-\varepsilon_{avg}\mathbf{H}_{vir}\delta\) if\(\hat{I}(\mathbf{x}_{adv})==noise\)then\(\varepsilon_{min}\leftarrow\varepsilon_{avg}\) else\(\varepsilon_{max}\leftarrow\varepsilon_{avg}\) end if \(\varepsilon^{*}=\varepsilon_{max}\) \(\delta^{vir}=\varepsilon^{*}\delta\) for\(i=1\) to \(p\)do \(\delta_{k[i]}=\delta^{vir}[i]\)  end for  Transmit \(\delta_{i},i=1,\cdots,q\)
```  
**Algorithm 3**EMCG attack
## 5 Methodology for Reliability and Availability Evaluation
### Network Failure Condition  
The network failure condition defines which combination of devices may lead to a network failure. In the proposed methodology we support any combination that can be expressed using boolean operators (_i.e._, _AND_, _OR_). The failure condition associated to field device \(N_{i}\) is defined as \(fc\_Fd_{i}\). The case where the device failure condition is related with several failure events will be described in the next sections. A combination of devices that lead to a network failure is defined as \(nfc\_and_{j}\), where \(j\) is the identification of combination and is represented by the boolean _AND_ of the failure condition of the devices (note: if the \(AND\) gate has only one input, the event \(nfc\_and_{j}\) is replaced by the respective input). A device can belong to more than one failure condition. The network failure condition (\(nfc\)) is represented by the boolean _OR_ of all combinations that lead to the network failure (Figure 6).
## 2 Digitalization and Computer Vision Technics
### Computer Vision  
Based on CNN, computer vision consists of a technology that accepts inputs in the form of images (photos or videos) and provides the automatic analysis of these images [18; 19]. Moreover, information and data science include methods for representing building information [20] and methods for AI processing of data, including machine learning (ML) and pattern recognition [21; 22; 23].  
The semantic understanding process is based on the image collection (image databases) for construction anomalies' detection.  
The image recognition process permits the objects' or patterns' automatic recognition from one or more images. The image segmentation process consists of the image being partitioned into different regions according to established criteria as expressive features permitting the shape information's extraction, the noise background reduction and, consequently, making image recognition easier. Thus, image segmentation is naturally applied before image classification [24].  
In the construction sector, different applications have been developed. The 3D point cloud classification process based on machine learning has been used for the automatic recognition of architectural components such as columns, facades or windows in large datasets [25]; archeologic mosaic image segmentation based on deep cascade learning [26] for the remote inspection of reinforced concrete (RC) structures using Unmanned Aerial Vehicles (UAVs) and based on the advanced digital image processing the automatic identification of various types of RC pathologies, particularly biological colonies, efflorescence, cracks and exposed steel rebars [10; 27]; automatic surface concrete damage recognition (cracks, rebar exposure, delamination and intact surface) utilizing a multi-level attention convolutional neural network [28]; concrete surface cracks' automatic identification and analysis using deep learning and machine learning techniques [29; 30; 31; 32]; and concrete spalling [33]. All of this research highlights the accuracy and importance of these methods for the inspection, maintenance and preservation of the built assets and to assure their performance and quality along with their service life. Computer vision-based techniques have been developed to automatically detect and categorize surface concrete anomalies, avoiding human visual inspections and the associated probability of error.  
The research project makes use of computer programming systems for the processing of all the data collected and its automation in a BIM environment, which will constitute the geometric basis from which in-depth and analytical scientific research will be undertaken. The data processing and training pipeline for the DL models are built using the Python programming language with the PyTorch [34] framework, besides others. The models are trained in a single GPU NVIDIA Tesla V100, and the metrics and result statistics are obtained using scikit-learn [35].
## 3 Results
### COSMO-SkyMed Dataset  
An interferometric pair of X-band COSMO-SkyMed images, taken in StripMap mode with a pixel size of approximately 2.5 m \(\times\) 2.5 m, depicting industrial buildings and vegetated areas near Pomigliano, Italy, was available. The pair was acquired by the four-satellite constellation operated by the Italian Space Agency (ASI) since 2010. The two images were processed as follows:  
1. Without a Hamming window, starting from raw data;
2. With a Hamming window, starting from raw data;
3. With a Hamming window and then spatially decorrelated.  
Figure 4 displays a comparison of the incoherent multilooking of the amplitudes of the master of the interferometric pair for a closeup of the test area in the three cases. In Figure 4b, the point scatterers are better defined than those in Figure 4a, thanks to Hamming windowing. The effects of the whitening step are visible, as the speckle appears less granular in Figure 4c, while point targets are preserved.  
Interferograms and related coherence and phase maps were calculated with coherent spatial multilooking for the three cases. In case 1, coherence is low in vegetation and also suffers from the spreading of areas featuring strong backscattering because of an increased number of sidelobes [47]. In case 2, point targets and buildings are better defined, thanks to the reduction in sidelobes achieved by the frequency window, but coherence is abnormally increased in the background because of the spatial correlation caused by windowing. Case 3 is the most favorable because the whitening operation produces low coherence in vegetated areas and high coherence in buildings, where the focusing benefits of the window are retained. Figure 5 shows the coherence maps in the three cases: Figure 5a,c exhibit better-defined high-coherence sections corresponding to built structures, as seen in Figure 5b, but do not suffer from anomalous increases coherence in temporally decorrelated areas (vegetation).  
An examination of the phase maps shown in Figure 6 strengthens the results achieved for coherence. The phase maps show that the focusing of linear targets is better after the whitening stage. Although the phase is noisy in low-coherence areas, Figure 6c shows slightly better accuracy in high-coherence structured areas compared to the case with correlated noise in Figure 6b. The case in Figure 6a is unique: while the background speckle is uncorrelated, linear scattering structures are poorly focused and hence the accuracy of the phase map is diminished.  
Here, the processing of interferograms was performed using boxcar filtering, which is the simplest tool for estimating interferometric coherence and phase maps. The goal is to objectively assess the advantages of spatial decorrelation, rather than to achieve the ultimate performance. In the next section, interferometric processing is carried out using an extremely sophisticated and powerful tool, namely NL-InSAR, which is capable of overcoming the drawbacks of local processing windows but is still affected by the spatial correlation of the speckle.
## 3 Results and Discussion
### Data Optimization  
The design of a suitable CSO extraction was performed after predicting the optimal % yield, acid value, peroxide value, TPC, and pressing time by the CCD method. For sieve size > 1.4 mm, a good desirability index (desirability = 0.705) was achieved with restriction die 0.8 cm, temperature 46 \({}^{\circ}\)C. For sieve size < 1.4 mm, a good desirability index (desirability = 0.520) was achieved with restriction die 0.8 cm, temperature 76 \({}^{\circ}\)C. The desirability index is a multi-criteria optimization algorithm used when responses must be at the specified requirements, which are maximum % yield, minimum acid value, minimum peroxide value, maximum TPC, and minimum pressing time.