esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Solution Approach  
Figure 1 shows the wireless sensor network being deployed over a large geographical area. Each node is able to measure the amount of soil moisture through the soil moisture sensor in the ground as well as detect the presence of rain through the rainfall sensor. The latter is necessary as the amount of soil moisture may not be enough to detect the presence of rain. In addition, the system must quickly react in order to discover over which part of the sensor network is experiencing the event: in this case rain. When rain is detected as falling in a given part of the system, the data is sent from the relevant nodes to a central administrative server or base station through the PC-USB Gateway. We have to change the routing so that information on the change in the soil content in the affected area could be sent to the server as quickly as possible. Hence, this data is routed at a higher priority that other information being sent. Our modified AOMDV protocol works as follows in two schemes. Under the first scheme, the sensor data under normal events (no rainfall) are sampled after every 3 h for 10 s and sent to the server. During these 3 h the sensors are in sleep mode to save energy and after the data is sampled they again go to sleep mode for another 3 h. If we have rainfall event we implement second scheme, in which the data sample is taken after every 1 s for the sensor node experiencing the rainfall event, hence the affected nodes send data after every 1 s, since the soil moisture may change very rapidly. In second scheme we also increase the priority of this affected node to transfer data to the base station through the gateway. All the other sensor nodes where there is no rainfall still keep sampling after every 3 h. In our previous work [30], we proposed a modified version of the AOMDV protocol. To accommodate these changes dynamically we made changes in the existing AOMDV protocol and proposed a dynamic and robust event driven routing protocol as described below.
# Real-Time Identification of Look-Alike Medical Vials Using Mixed Reality-Enabled Deep Learning
## 2 Related Works
### YOLO-Based Small Object Detection in Mixed Reality  
Recent studies have explored the potential of YOLO models in AR/MR environments for real-time tracking and classification. [PERSON] et al. [20] proposed an enhanced YOLOv4-based object detection and tracking system in Mixed Reality environments, improving classification accuracy and localization. Their model achieved an mAP of 0.988, surpassing previous YOLO versions, making it highly effective for general object detection tasks. However, their study focused on large and medium-sized objects in collaborative workspaces rather than small medical vials, which present unique challenges in terms of feature extraction and real-time usability.  
Additionally, [PERSON] and [PERSON] [8] conducted a comprehensive review of MR applications in healthcare, emphasizing the growing role of Mixed Reality in medical training and procedural guidance. However, real-time vial identification in MR-assisted pharmaceutical environments remains underexplored, highlighting the need for specialized deep learning architectures tailored to small-object detection in MR-based workflows.
## 2 Manufacturing and Measurement Techniques
### Qualitative Analysis  
SEM (JSM5600 LV, JEOL, Japan) images were taken at different magnifications to understand the surface morphology. From the SEM images, asperities present in the as-built AM surface were identified and explained based on their size differences. Additionally, the \(\varphi_{\text{m}}\) of each irregularity (loosely attached powders, partially melted powders, and semi-welded structures) was extracted using a suitable image processing technique. However, it should be noted that SEM images are difficult to calibrate and size measurements from the images also possess difficulties due to imaging artifacts.
# Research on the Path Planning of Unmanned Sweepers Based on a Fusion Algorithm
## 6 Experimental Results
### Real-Time Motion Planning  
In order to verify whether the fusion algorithm can be applied in practice, the fusion algorithm was embedded in an unmanned sweeper vehicle for validation. The unmanned sweeper was built on the Autoware development platform with integrated sensors such as LiDAR and IMU. LiDAR is used to scan environmental information and generate highly accurate maps of the environment. The unmanned sweeper used for the experimental validation is shown in Figure 12, and the motion parameters were set as shown in Table 2.  
The roslaunch work_driver work_driver. launch command was run to open the chassis driver node, and then the roslaunch work work_keyboard run. Through the I, J, K, L four direction keys, the movement of the vehicle was controlled. Then, the ntd_mapping algorithm was used to construct the environment map. After the construction, the global map was saved as pcd map, and the final high-precision vector map was created according to the pcd map.
# DDL R-CNN: Dynamic Direction Learning R-CNN for Rotated Object Detection
## 3 Method  
In this section, we first introduce the design and functionality of the routing function (Section 3.1). Figure 2b illustrates its overall structure, where the input image features are processed by the function to generate a set of two-dimensional data. One dimension pertains to the angles of targets within the image, and the other dimension corresponds to the angle weights associated with those data. We then further discuss the structure and implementation details of the DDL module (Section 3.2). Figure 2a shows the overall architecture of this module, which uses the routing function to generate a set of two-dimensional data from the input feature maps, combined with a set of original convolution kernel parameters, to ultimately produce a new set of feature maps \(y(\Delta\alpha,\Delta\beta)\). Finally, Section 3.3 explains the overall network structure of the proposed DDL R-CNN and provides detailed implementation details of BC-ROPN. Figure 3 presents the overall architecture of the network, which is based on a two-stage detector using FPN for feature extraction and processing of images. It incorporates the anchor box boundary center offsets extracted from the feature maps generated by the DDL module before anchor box regression. This enables anchor box regression based on the boundary center points of the target's maximum rectangle and ultimately outputs the image results.  
Figure 2: The schematic diagram of the DDL module. From a broad perspective, the final feature map \(y(\Delta\alpha,\Delta\beta)\) is jointly guided by the original convolutional parameters \(w\) and the input feature map \(x\), and is generated through the processing of dynamic directional convolution kernels and other related convolutions. **(a)** The rotating convolutional kernel is generated by combining the convolutional kernel parameters extracted from the original convolution with the rotation angles predicted by the routing function. **(b)** The functional makeup of the routing function involves encoding image features, represented by \(x\), through deep convolutional layers that follow an average pooling layer. Parameters \(\theta\) and \(\lambda\) are predicted by two distinct branches, each with its own specific activation function.
###### Abstract  
In this paper, we propose a novel low-complexity multi-user superposition transmission (MUST) technique for 5G downlink networks, which allows multiple cell-edge users to be multiplexed with a single cell-center user. We call the proposed technique _diversity-controlled MUST technique_ since the cell-center user enjoys the frequency diversity effect via signal repetition over multiple orthogonal frequency division multiplexing (OFDM) sub-carriers. We assume that a base station is equipped with a single antenna but users are equipped with multiple antennas. In addition, we assume that the quadrature phase shift keying (QPSK) modulation is used for users. We mathematically analyze the bit error rate (BER) of both cell-edge users and cell-center users, which is the first theoretical result in the literature to the best of our knowledge. The mathematical analysis is validated through extensive link-level simulations.  
5G wireless networks; non-orthogonal multiple access (NOMA); OFDM; frequency diversity; multiple antennas; error probability +
Footnote †: journal: Sensors
# Wave Propagation in Laminated Cylinders with Internal Fluid and Residual Stress
###### Abstract  
Numerical computation of wave propagation in laminated cylinders with internal fluid and residual stress is obtained using a Wave Finite Element formulation for 2D waveguides. Only a very small segment of the system is modelled, resulting in a very low-order finite element (FE) model to which the theory of wave propagation in 2D periodic structures is applied. The method uses standard FE formulations and exploits the capability of commercial FE software to model both fluid and structure and their interaction, resulting in a very large reduction in computational time. The presented approach is general, and can be applied without the need to make assumptions related to shell theory or low-frequency analysis. In particular, the laminated structure is discretised using 3D solid elements, thus representing the through-thickness dynamics with high accuracy. Residual radial and hoop stresses are included in the model by adding the FE pre-stress stiffness matrix to the original stiffness matrix of the system. The method provides simultaneously a very substantial reduction of computational cost, accurate solutions up to very high frequency and prediction of the dispersion curves for selected circumferential orders without the need for any further analysis. Here, the formulation of the method is introduced and its application to laminated cylinders filled with an acoustic fluid is presented. A composite, reinforced rubber cylinder, pre-stressed by a circumferential tension, is also shown as an example of a laminated pipe for high-pressure applications.  
wave propagation; dispersion curves; fluid-structure interaction; laminated cylinders; finite element +
Footnote †: journal: Applied Physics Letters  
1 Dipartimento di Ingegneria e Architetura, Universita di Parma, 43124 Parma, Italy 1  
2 Department of Mechanical and Mechatronics Engineering, University of Auckland, Auckland 1142, New Zealand 2
## 2 Materials and Methods
### Study Area  
The study area is shown in Figure 2. The study area comprises four counties of Kenya: Turkana, Marsabit, Mandera and Wajir. The selected region lies in the northern part of Kenya, which is characterized as part of the arid and semi-arid lands (ASALs) of Kenya. The selected counties are classified as arid and part of the ASALs monitored by the National Drought Management Authority (NDMA) of Kenya. The four counties cover a combined area of 215,242 km\({}^{2}\) with a total population of around 2.8 million. The annual average rainfall is 250 mm (Turkana, Marsabit and Mandera) to 370 mm (Wajir). Rainfall patter is bimodal with the long rains in March, April and May (MAM) and the short rainy season between October, November and December (OND) with 6 months considered wet months. The monthly average NDVI for the period 2003-2015 from the NDMA's operational drought monitoring system, is shown in Figure 3. The low values indicate sparse vegetation cover, even during the wettest months (e.g., May and December).
###### Abstract  
The identification of the position of rail vehicles plays a crucial role in the control of rail traffic. Available, up-to-date information on the position of vehicles allows us to efficiently deal with selected traffic situations where the position of vehicles is very important. The main objective of this article is to introduce (i) a concept of a solution for identification of the current position of rail vehicles based on the worldwide-recognized system of the GNSS with the use of an original railway network data model, and (ii) the use of this concept as supplementary support for the dispatcher control of rail traffic on regional lines. The solution was based on an original, multilayer rail network data model supporting (i) the identification of rail vehicle position and (ii) novel algorithms evaluating the mutual positions of several trains while detecting the selected crisis situation. In addition, original algorithms that enable automatic network model-building (on the database server level) directly from the official railway infrastructure database were developed. The verification of the proposed solutions (using rail traffic simulations) was focused on the evaluation of (i) the changing mutual positions (distances) of trains on the railway network, (ii) the detection of nonstandard or crisis traffic situations, and (iii) the results of the calculations of necessary braking distances of trains for stopping and collision avoidance. The above verification demonstrated the good applicability of the proposed solutions for the potential deployment within supplementary software support for real traffic control. The described concept of the supplementary support determined for railway traffic control (using the localization of trains by means of the GNSS) is intended mainly for regional, single-rail lines. This type of line is very often not sufficiently equipped with standard signaling and interlocking equipment to ensure the necessary traffic safety. Therefore, when deploying this support, the new algorithms for the automatic detection of critical traffic situations represent a significant potential contribution to increasing operational safety.  
a 2022  
Accepted: 3 June 2022  
Published: 7 June 2022  
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# Factors Affecting the Quality of Bacterial Genomes Assemblies by Canu after Nanopore Sequencing
## 2 Materials and Methods
### Assembly Quality Assessment  
The quality of each assembly was assessed using QUAST v.5.0.2 [22], comparing them with the S. Bovismorificans reference chromosome (GenBank: NZ_CP073715.1, molecule size: 4,667,486 bp). Considering the high variability of the plasmid structures, only the chromosome was considered.  
The total number of contigs and bases in the assembly, the length of the largest contig, and N50 (the length for which the collection of all contigs longer than or equal to that value that covers at least half an assembly) were taken into account for each assembly. For the comparison against a reference genome, the number of misassemblies, misassembled contigs, mismatches, and indels were considered.  
The nucleotide sequence contigs from each setting underwent a similarity sequence against the nucleotide database of NCBI Basic Local Alignment Search Tool (BLAST) [23]. Specifically, the smaller contigs were aligned via the web interface, while the larger ones (>1,000,000 bp) with the BLAST+ Command Line Applications tool v.2.12.0 [24] using the default setting of the task \"megablast\".  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & & & & & & & & & **Canu Parameters \({}^{*}\)** & & & & & & \\ \hline S1 & BT & 279 & 0.30 & 0.12 & 0.50 & 4 & 2 & 4 & 2 & 4 & 1 & - & - & - \\ S2 & BT & 819 & 0.40 & 0.144 & 0.50 & 4 & 2 & 4 & 2 & 4 & 1 & High & 0 & 100 \\ S3 & BT & 268 & 0.30 & 0.144 & 0.50 & - & - & - & - & - & - & - & - & - \\ S4 & B434 & - & 0.30 & 0.144 & 0.50 & - & - & - & - & - & - & - & - \\ S5 & B530 & - & 0.30 & 0.144 & 0.50 & - & - & - & - & - & - & - & - \\ S6 & B593 & 162 & 0.30 & 0.144 & 0.50 & - & - & - & - & - & - & - & - \\ \hline \hline \end{tabular}  
* \({}^{*}\)- default settings.  
\end{table}
Table 1: Description of the six different settings adopted for Canu assembly and parameters variations. Setting S1, S2, and S3 use the combined basecalled reads from the three sequencing experiments (BT), while S4, S5, and S6 use separate reads (B434, B530, B593, respectively). De novo assembly with default Canu parameters for S4 and S5 failed.  
The _Salmonella_ serotype determination was performed by using the online versions of SeqSero v.1.0 [25] and SeqSero2 v.1.1.0 [26] from the polished assemblies obtained from settings S1, S2, S3, and S6.  
Additionally, comparison data from pairwise alignments between each assembly consensus against the reference genome were generated using Artemis Comparison Tool (ACT) v.18.1.0 [27].  
All analyses were performed on a workstation mounting Ubuntu v.20.04.3 LTS (Intel Core i5-4460 3.20 GHz x 4, 8 GB RAM, 1 TB HDD).
# Enhancing Reliability of Tactical MANETs by Improving Routing Decisions
## 3 Link Quality Based Routing Metrics for Tactical MANETs
### SINR Metric  
Tactical MANETs are more likely to be subject to intentional or unintentional interference. The packet delivery rate depends on average SINR being above a certain threshold \(\delta_{th}\) (in dB). The nodes \(i\) and \(j\) are connected if both of them experiences SINR above such threshold, and they are disconnected otherwise. Assuming the SINR metric, the connectivity between two nodes is defined by a random variable:  
\[\mathbb{C}_{ij}=\left\{\begin{array}{l}1,\,\,min(\text{SINR}_{j},\,\,\text{ SINR}_{j})=\frac{P\big{(}d_{ij}\big{)  
where \(N_{0}\) is power spectral density of additive noise (AWGN), \(P\) is the received power, \(I\) is the interference level, and \(\mathrm{d}_{ij}\) is the distance between nodes \(i\) and \(j\). The field measurements for tactical MANETs suggest the SINR threshold of at least 20 dB to achieve acceptable BER performance [11]. In order to maintain the required SINR thresholds, the VHF/UHF terrestrial radios are limited to link ranges of 20 to 30 km. Whilst it is straightforward to measure RSS at the receiver, and to also estimate the long-term power spectral density of background noise, determining the interference level is rather problematic. The reason for difficulty in estimating the interference is that it usually changes completely unpredictably, and there are no known (pilot) symbols to exploit. We can at least assume that the background noise in all receivers is approximately the same. The interference can be modeled as another zero-mean additive noise with non-stationary changes in the variance given by the transmit powers, distances and path-loss attenuations. We can use hardware testbeds to accurately measure or set the interference levels for experiments [27]. In summary, even though SINR can provide better routing decisions, it is difficult to be measured in practice, especially when changes in interference level are frequent. This motivates us to exploit both RSS and possibly also estimated SNR values (without interference) to assess link quality and make routing decisions.
# DFT-Low-Latency Bit-Slice Design of Discrete Fourier Transform
## 5 Hardware Architecture of the FPGA-Based System: An Application Example
### The Proposed FPGA-Based Architecture
#### 5.2.3 The Sum and Multiplier Block  
This block performs the BFT calculation. It can be divided into the adder and the multiplier unities.  
This processing module makes the BFT calculation according to expressions of Section 3. Figures 11 and 12 show the adder symbol and the schematic in Quartus environment for the selected application example.  
The nestedSum unit consists of the below circuits, which perform the following functions (Figure 13):  
* Sum_Block, which evaluates \(X_{k}\) for every \(k\) value that is generated in the timing block. It is made of two PIPO registers and an adder (mux_sum ) which performs the BFT  
Figure 10: Black box representing the ROM BLOCK with the relevant input and output signals.  
Figure 9: Schematic design of the k_block.  
calculation from Equation (8) in an optimized manner without multiplications as outlined in Section 3 (Figure 14);
* Two 32-bit PIPO registers, which store the calculated current \(X_{k}\) value for one clock pulse;
* twid_delay, which introduces a suitable delay for the correct twiddle coefficient management by the sum_Block, after the reset signal is generated as a result of the end of the calculation cycle for every \(k\) value.
* end_sumBlock, which adds all the \(X_{k}\) values (for \(k=0,...,1023\)) as soon as the sum_block calculates them. It is composed of PIPO registers and an adder (named k_sum) which is designed in-house by Authors adopting the Verilog hardware description language (Figure 15);
## 3 Three-Dimensional Finite Element Model
### Mesh Sensitivity Study  
The effect of mesh refinement in the area of the drilled hole is studied to examine the element sizes. Therefore, a uniform pressure distribution on the sides not containing the drilled hole is applied along the entire thickness of the plate and a linear-elastic stress-strain behavior is assumed. The strains obtained with this model are compared with the theoretical calculated strains according to ASTM E837-13a. The mesh is further refined until the results of the model are well aligned with the theoretical result. This results in a mesh size of 0.2 mm in a zone of 10 mm around the drilled hole. The rest of the model has a mesh size of 0.5 mm. The mesh size near the drilled hole is smaller since a higher precision is required for an accurate evaluation of the strains on the strain gauge rosette. Outside the drilled hole region and further away from the strain gauges, a less high precision is required resulting in a shorter calculation time. There are 436,410 three-dimensional hexagonal CHEXA-elements with eight nodes present in the entire model when there is no material removed by the drilling procedure. The depth increments where material has to be removed during the hole-drilling process are meshed separately. This mesh will be used as a reference for all 3D models used in this paper. The finite element model for drilling the hole is shown in Figure 7.  
Figure 6: (**a**) Uniform in-depth and (**b**) non-uniform in-depth residual stress field.  
When the mesh is further refined, the calculation time increases drastically and the precision stays almost the same. The procedure for the mesh refinement is shown in Figure 8. The mesh refinement for radial strain 1 is displayed which is the radial strain in the x-direction and mesh 4 is the mesh used for further calculations.