esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Whole Genome Sequencing, Assembly, and Quality Control  
DNA extraction was performed according to [PERSON] et al. [12] and DNA sequencing by Novogene Genomics Service (Novogene Co., Cambridge, UK). Degradation and contamination of DNA was examined by agarose gel electrophoresis while quantification by Qubit 2.0 (ThermoFisher Scientific, Waltham, MA, USA). Library construction was performed as follows: sonication was employed for random fragmentation of DNA, followed by end polishing, A-tailing, ligation with the Illumina sequencing adapters, and finally PCR amplification with P5 and P7 oligos. Purification of the PCR products was performed with the AMPure XP system (Beckman Coulter, Brea, CA, USA), size distribution assessment with the Agilent 2100 Bioanalyzer (Agilent Technologies, CA, USA), and quantification by qPCR. Sequencing of the qualified libraries took place in the Illumina Novaseq 6000 sequencer (Illumina, San Diego, CA, USA) (2 \(\times\) 150 bp). FastQC v.0.11 tool of the KBase platform [13; 14] was employed for quality assessment of the adapter-free raw reads. Polishing and de novo assembling of the reads into contigs took place using the Pilon tool and the Unicycler assembler, respectively, provided by the PATRIC v3.6.8 web platform [15; 16; 17]. The Multi-Draft based Scaffolder (MeDuSa) v1.6 platform [18] was used to assemble the contigs into scaffolds. The latter were then ordered and oriented on the basis of multiple complete reference genomes present in the NCBI database ([[https://www.ncbi.nlm.nih.gov/](https://www.ncbi.nlm.nih.gov/)]([https://www.ncbi.nlm.nih.gov/](https://www.ncbi.nlm.nih.gov/)) accessed on 10 January 2022); _E. ficeum_ ATCC8459, _E. ficeum_ NBRC100486, _E. ficeum_ SRR24, _E. ficeum_ LMG8148, _E. ficeum_ NCTC7171, and _E. ficeum_ NRRLB-2354 (for _E. ficeum_); _E. ficeum_ ATCC19433, _E. ficealis_ EnGen0336_T5, _E. ficealis_ NCTC775, and _E. ficealis_ NBRC100480 (for _E. ficealis_); _E. durans_ NBRC100479, _E. durans_ ATCC6056, _E. durans_ NCTC8130; _E. durans_ NCTC8129, and _E. durans_ RL1-82 (for _E. durans_); and _E. italicus_ DSM15952 (for _E. italicus_). The CheckM tool v1.21 [19] of the PATRIC v3.6.8. platform was used for quality assessment of the contigs and scaffolds to ensure that assembled genomes ere of high quality, i.e., completeness (\(\geq\)95%) and contamination (\(\leq\)5%). The Skew Index Test (SkewIT) v1.0 web tool [20] was used to assess possible mis-assemblies after scaffolding.
## 2 Problem Formulation and Application Environments
* It could be used to build networks whose cluster can be formed by different types of devices such as mobiles, PDAs, PCs, sensors, but the requirement is for networks formed by the same type of devices. * It could be used for virtual wireless sensor network creation too.
# Design Considerations and Performance Evaluation of Gossi Routing in LoRa-Based Linear Networks
## 5 Result Evaluation and Discussion  
In this section, we discuss the outcome of all experimental trials that we designed in the previous Section 4.2. The experimental result outcomes during each particular design are further discussed below.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Packet Size & 10 Bytes \\ Transmission Power (TP) & 14 dBm \\ Spreading Factor (SF) & 7 \\ Bandwidth (BW) & 500 kHz \\ Coding Rate (CR) & 4/5 \\ \hline \end{tabular}
\end{table}
Table 1: LoRa configuration.
# Segmenting Individual Tree from TLS Point Clouds Using Improved DBSCAN
## 2 Methods and Materials  
The process of our method is as follows: (1) The improved DBSCAN algorithm is used to detect trunk on slice data of point clouds and obtain initial clustering results. (2) The Hough circle fitting method is used to modify the clustering results obtained from trunk detection to eliminate the interference of brush and other noise data. (3) The remaining point clouds are divided by region growth to achieve individual tree segmentation of TLS point clouds.  
Since the traditional DBSCAN algorithm needs to manually determine the neighborhood distance threshold \(\varepsilon\) and the minimum number of points _MinPts_ for clustering, we proposed an improved DBSCAN method. The improved DBSCAN method can fully consider the characteristics of dataset, and automatically determine the optimal cluster number and corresponding input parameters (\(\varepsilon\), _MinPts_) based on distance distribution matrix (DDM).
## 5 Discussion
### Connections to Bus Stops and Metro Stations  
In the study, the connections to public transport stops were also analyzed, particularly the stops located in the study area for the trolleybus and metro station. In the case of the trolleybus, the following elements were evaluated:  
* Identification of the stops.
* Distinction between the pedestrian area and the waiting area.
* Condition of the pavement in the stop area.
* Blockage of the stops by vehicles.  
These elements were analyzed directly at the stops during the investigation. Concerning stop identification, it was observed that none of the stops had shelters. However, a stop sign was found at each stop but not at the trolleybus terminal. It is important to focus on installing shelters and signs at these stops to improve identification for pedestrians and provide a safer and more comfortable environment for passengers.  
It is recommended to make efforts to implement shelters at both stops and ensure clear and visible stop signs at both the stop and the trolleybus terminal. This will help facilitate the identification of proper transport pick-up points and improve the pedestrian experience at the stop.  
Regarding the distinction between the pedestrian area and the waiting area, no clear separation was observed at these locations. This is directly related to the absence of shelters at the stops, which could have provided this separation.  
Regarding the condition of the pavement at these stops, it was determined that 100% of the pavement is stable. This means that the pavement in the waiting area is free of irregularities or obstacles that hinder pedestrian movement.  
Regarding the presence of vehicles blocking the stops, it was observed that in 100% of the cases, no blockage occurred. This indicates that the stops are free of obstacles caused by improperly parked vehicles.  
Lastly, the Baron metro station was analyzed from the perspective of a pedestrian moving both inside and outside the station. The following elements were considered:  
* Minimum space of 1.5 m for wheelchair users.
* Ramps inside and outside the station.
* Minimum width of 3 m between platforms and pedestrian crossings.
* A marked zone exclusively for people with reduced mobility.
* Benches obstructing the path of pedestrians to and from the platform.
* A free height of 2.1 m.  
Overall, the Baron Station meets most of these accessibility requirements. However, one important shortcoming was identified: the lack of a designated zone for people with reduced mobility. This should be addressed to improve accessibility at the station.  
In conclusion, the descriptive method clearly reveals that accessibility varies from one sector to another, with some areas exhibiting excellent accessibility while others are affected by certain elements.
# Smart(Sampling)Augment: Optimal and Efficient Data Augmentation for Semantic Segmentation
## 4 Experiments and Results
### Experimental Setup
#### 4.1.4 Datasets  
We evaluate all approaches on four datasets with pixel-level annotated images from diverse semantic segmentation applications. ErFASst is a bridge inspection dataset with 50 images and two classes used for crack detection (Figure 3(a)). We use KITTI [34], a popular autonomous driving dataset consisting of 200 images with 19 classes (Figure 3(b)). RaVeNNa [1], is a cystoscopic medical imaging dataset comprises 1684 images with seven classes that is used in detecting artifacts such as tumors in human bladder (Figure 4(a)). EM [35] is a brain electron microscopy dataset of 30 images derived from a 2D segmentation challenge dataset and consists of two classes (Figure 4(b)). To achieve meaningful results, these datasets differ in size, resolutions, and type of images (RGB natural images, Grayscale, see Table 3). Since RaVeNNa and ErFASst are highly class-imbalanced datasets, we use a weighted cross-entropy loss during training. The weights are computed beforehand with inverse frequency of the number of pixels belonging to a specific class in the training set.
## 5 Research Findings
### RQ1: What Techniques Are Utilized to Abuse the CPLS as C&C Communication Channels?
#### 5.2.8 2020: A Year of Escalated CPLS Abuse for C&C  
As depicted in Figure 5, the year 2020 witnessed a substantial increase in the abuse of CPLS platforms for C&C operations. This escalation could potentially be attributed to the global COVID-19 pandemic, which resulted in a significant increase in online activities. The expanded online presence created a fertile ground for cybercriminals, offering them a larger pool of potential targets.  
RQ3: What Countermeasures Have Been Proposed to Detect the Abusive Use of CPLS as C&C Infrastructure?  
Several approaches have been proposed to detect the abusive use of CPLS as a C&C infrastructure. These countermeasures include the following.
# Multi-Class Strategies for Joint Building Footprint and Road Detection in Remote Sensing
## 2 Methods
### Direct Multi-Class Approach  
A pixel-wise classification task involving more than two classes is known as a multi-class semantic segmentation problem. Here, the aim is to assign a label from a set of classes to each pixel on an image. However, depending on the use case, the boundaries between the classes may overlap, increasing the complexity of the pixel-labeling task.  
The standard and most popular architecture for addressing semantic segmentation problems is the fully convolutional network (FCN), introduced by [PERSON] et al. [37]. The FCN was one of the first CNN-based architectures, where the segmentation map was obtained through a single forward pass. Moreover, since FCNs only have convolutional and pooling layers, they can make predictions on arbitrarily sized inputs. On the downside, multiple alternated convolutional and pooling layers downsample the resolution of the output feature maps, resulting in coarse object boundaries. However, novel semantic segmentation architectures based on the idea of FCNs, such as the SegNet [38], DeepLab [39] or U-Net [40], address this issue.  
In this work, we have opted for the U-Net architecture, which has shown remarkable performance across a wide range of applications. As it is shown in Figure 1, this symmetric architecture consists of two major parts. The contracting path, also known as the encoder, reduces the spatial dimensions in every layer, while increasing the channel dimension. On the other hand, the expansive path, also known as the decoder, increases the spatial dimensions, while reducing the channel dimension. Both paths are symmetric and connected by the bottleneck. In the U-Net architecture, the feature maps extracted by the encoder are concatenated to the decoder, avoiding the loss of pattern information and thus, enabling precise localization.  
The final \(1\times 1\) convolutional layer of the U-Net architecture performs the pixel-wise classification. Depending on the scenario, both the number of channels and the activation function employed in this layer may vary, that is, when addressing a binary semantic segmentation problem, the channel dimension will be set to 1, and the sigmoid will be used as the activation function. However, when facing multi-class settings, the channel dimension will be set to the total number of classes, and the softmax will be chosen as the activation function.
## 2 Antenna Design
### Design of Antenna Array  
Based on the proposed wideband element, a \(4\times 4\) uniformly placed array is designed to provide high gain. Figure 9 displays the geometry and dimensions of the ME dipole array. The array dimension is 52.7 mm (4.92 \(\lambda_{0}\)) \(\times\) 59.6 mm (5.57 \(\lambda_{0}\)) \(\times\) 1.47 mm (0.14 \(\lambda_{0}\)). The element spacing is 8.75 mm (0.82 \(\lambda_{0}\)) along the \(x\)-axis and is 8.7 mm (0.81 \(\lambda_{0}\)) along the \(y\)-axis. At the bottom of the array, a microstrip 1-to-16 power divider is printed to provide each element with an input impedance of 75-\(\Omega\) with equal amplitude and equal phase. The microstrip divider does not need an extra substrate layer, thus it is much simpler than the SIW divider [36]. A thin substrate layer is adopted to avoid energy leakage from the microstrip feed line. A T-junction is a crucial part of the power divider, as displayed in Figure 10a. Ports 1, 2, and 3 all have impedances of 75-\(\Omega\), and a 53-\(\Omega\)\(\lambda_{0}\)/4 transformer section is used to match the impedances of these three ports. The \(\lambda_{0}\) refers to the wavelength in free space at 28 GHz. Figure 10b illustrates the simulated S-parameters results of the T-junction, and the reflection coefficient of the Port1 is under \(-18\) dB in the range of  
Figure 8: Simulated normalized radiation patterns of the CP element at three separate frequency points: (**a**) 24 GHz; (**b**) 28 GHz; (**c**) 32 GHz.  
Figure 7: Simulated gain of the CP element.  
22 to 36 GHz. To match the 75-\(\Omega\) input port of the T-junction with the external 50-\(\Omega\) excitation port, a microstrip line with a gradually changing width is adopted.
## 2 Related Works
### Floor Map Segmentation-Based Method  
The above methods have weaknesses that are associated with missing and noisy data, and certain works can only utilize occlusion-free data. Moreover, rebuilding an indoor watertight model is difficult in certain cases [26; 27]. In recent approaches, the focus shifted towards segmentation into different individual rooms to resolve issues with missing data for ceilings and floors [14], and then reconstruct indoor models through segmented maps.  
Floor maps can be segmented by various methods, such as the morphological method [35], k-medoids clustering method [34], spectral clustering method [48], and by the properties of Delaunay triangulation [33]. However, some approaches [33; 34] require viewpoints or a priori knowledge on the number of rooms.  
A two-step room-segmentation and wall-/ceiling-detail reconstruction method [34] was proposed to reconstruct indoor environments. The room segmentation is formulated by k-medoids clustering, whereas wall-/ceiling-detail reconstruction is determined by an \"offset map\". The proposed approach provides a new algorithm for room segmentation and reconstruction; however, the binary visibility vector is not robust for sparse point clouds. Furthermore, the output model is restricted to a simple rectangular model.  
[PERSON] et al. [35] introduced four methods for room segmentation, and [PERSON] et al. [37] proposed a novel ripple-segmentation method for floor map segmentation. Both studies focused on floor map segmentation and not a 3D model.
## 3 Unsaturated Soil  
### Characteristic of Unsaturated Soil  
The unsaturated soil exists widely in nature since most of the soil above the groundwater level is in an unsaturated state. Due to the differences in the medium and molding method, the soil has special structure and stress state. There are two types of theory frames to describe mediums in unsaturated soil. Figure 3a considers the unsaturated soil as a three-phase soil, including a solid phase (soil particles and some bio-cementitious substances), a liquid phase (water and aqueous solution), and a gas phase (air and water vapor, etc.). However, with the further study of unsaturated soil mechanics, unsaturated soil is considered as a four phase structure [80]. In addition to the solid, liquid, and gas three-phases, the liquid gas interface is also added as an independent phase. This independent phase can also be called the bound water membrane, as shown in Figure 3b. The nature of the liquid gas interface is not only different from that of water, but also different from that of gas. It is an independent phase. This phase is called the shrink film in surface chemistry. The morphology of the water pores and gas pores of unsaturated soil is closely related to the water content (saturation) of unsaturated soil [81].  
The unsaturated soil can be divided into four types: fully connected gas phase, partially connected gas phase, internally connected gas phase, and completely closed gas phase. When the gas phase in the pores exists in a fully connected way, the gas permeability has nothing to do with the water content. With the increase in the water content, the effective stress of the soil mass gradually reduces the volume of gas in the pores. A semi closed or closed bubble is formed, and the pore gas exists in a partially connected or internally connected state. At this time, the suction of soil decreases gradually, and the effective stress of soil should consider the influence of pore air pressure and pore bearing pressure, respectively. When the water content in the soil is very high, the gas phase is completely  
Figure 3: Schematic diagram of mediums in unsaturated soil: (**a**) solid liquid gas three phase system; (**b**) four phase diagram.  
surrounded by liquid, small in volume, and can only flow with the liquid, which can be regarded as a two-phase system with air and water. At this time, the soil mass is basically saturated, the air permeability of the soil is very small, the suction is basically zero, and the effective stress of the soil mass is the same as that of the saturated soil.  
[PERSON] et al. [82] pointed out that the shape of unsaturated soil is more complex than that of saturated soil, and this complex medium composition has an important impact on the strength, deformation, and seepage of soil. Given that the structure of water pores and gas pores in unsaturated soil plays a key role in its engineering properties, the effect of the matrix suction and water content on the properties of unsaturated soil should be further studied.
# Computer-Vision-Based Product Quality Inspection and Novel Counting System
## 2 Related Works
### Computer-Vision-Based Defect Detection
#### 2.1.2 Application of Vision Algorithm in Product Classification  
The development of vision algorithms is currently making an important contribution to the detection and classification of product defects and quality control in the manufacturing industry and continues to develop in the direction of increasing real-time processing capabilities and accuracy. Through this, it plays an important role in maximizing productivity and quality control efficiency in the manufacturing process. When applying vision algorithms in product classification, commonly used image comparison techniques such as SSIM, MSE, and PSNR are used. These technologies allow for the evaluation of structural similarity and pixel differences between the same images. Although these techniques are useful for basic image quality evaluation, they have the limitation of not being sufficiently robust against rotation or size changes.  
In contrast, feature-based algorithms such as SIFT can extract feature points robustly even when image rotation or size changes, enabling accurate defect detection [22; 23]. SIFT has the advantage of being able to extract feature points stably despite rotation or size changes within the image [24; 25]. According to the results in Figure 3, we tested SURF (Speeded-Up Robust Features), SIFT, and the hybrid technique. The hybrid technique demonstrated good performance for scale changes, whereas SIFT provided more accurate performance when detecting features such as rotational invariance [26]. Based on this, this study aimed to develop a model that increases the recognition rate of products and enables more-accurate defect detection by utilizing the SIFT technique to overcome the limitations of existing techniques.
## 4 Experimental Verification  
### Experimental System  
In order to verify the calibration method of the angular positioning deviation of the rotary table based on the laser tracer multi-station measurement system proposed in this paper, a multistation measurement system of laser tracer was built (Figure 5). In the experiments, the measurement system was composed of the CMM (Leitz Infinity of Hexagon Company, Stockholm, Sweden), the high-precision rotary table (RT400, the angular positioning deviation = \(\pm 0.5\)\"), and the laser tracer (Etalon Company, 07 2009 TR IF). The space range of the measured points in CMM was planned to be \(450\times 600\times 450\) mm\({}^{3}\). In this space range, the number of planned measured points was \(n\) = 64, and the number of laser tracer stations was \(m\) = 11. The path planning of sampling points in the space range of the measured points is shown in Figure 6. There were four layers of paths. The height of each layer varied by 150 mm with the same moving path.  
In the calibration process of the angular positioning deviation of the rotary table, considering the light-tracing problem of the laser tracer at some positions, the laser tracer was fixed on the position of the rotary table of \(-145^{\circ}\). This position was recorded as the first position \(P_{1}\) of the laser tracer. The probe of CMM moved to \(A_{1}\). The rotary table rotated counterclockwise by \(30^{\circ}\). The laser tracer rotated together with the rotary table. During the rotation process of the laser tracer from the first position \(P_{1}\) to the eleventh position \(P_{11}\), the interferometric length \(l_{ij}\) of the laser tracer was respectively recorded. Then, the probe of CMM was controlled to move from \(A_{1}\) to \(A_{2}\) along the planned path in the measurement space. During the rotation process of the laser tracer from the first position \(P_{1}\) to the eleventh position \(P_{11}\), the interferometric length \(l_{ij}\) was respectively recorded in turn. A total of 704 laser tracer interferometric lengths were recorded during the experiment.  
Figure 5: Experimental platform.