esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In the literature, we can find several blind adaptive deconvolution algorithms based on closed-form approximated expressions for the conditional expectation (the expectation of the source input given the equalized or deconvolutional output), involving the maximum entropy density approximation technique. The main drawback of these algorithms is the heavy computational burden involved in calculating the expression for the conditional expectation. In addition, none of these techniques are applicable for signal-to-noise ratios lower than 7 dB. In this paper, I propose a new closed-form approximated expression for the conditional expectation based on a previously obtained expression where the equalized output probability density function is calculated via the approximated input probability density function which itself is approximated with the maximum entropy density approximation technique. This newly proposed expression has a reduced computational burden compared with the previously obtained expressions for the conditional expectation based on the maximum entropy approximation technique. The simulation results indicate that the newly proposed algorithm with the newly proposed Lagrange multipliers is suitable for signal-to-noise ratio values down to 0 dB and has an improved equalization performance from the residual inter-symbol-interference point of view compared to the previously obtained algorithms based on the conditional expectation obtained via the maximum entropy technique.  
Bayesian approach; deconvolution; maximum entropy density approximation technique
# Multiscale YOLOv5-AFAM-Based Infrared Dim-Small-Target Detection
## 2 Methods  
### Overview of YOLOv5  
YOLOv5 ([[https://github.com/ultralytics/yolov5](https://github.com/ultralytics/yolov5)]([https://github.com/ultralytics/yolov5](https://github.com/ultralytics/yolov5)) (accessed on 5 May 2023)) proposes four network structures of different sizes, including YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x. They have the same overall structure, but the depth and width of the CSPNet differ from each other. The YOLOv5 network consists of the backbone network, neck network, and head network. As shown in Figure 1, the backbone network mainly consists of the Conv module, the C3 module [27], and the SPPF module. The neck network adopts a feature pyramid network [28] structure to achieve multiscale target detection in a top-to-bottom manner. The head network consists of three target detectors. It uses a grid-based anchor to perform target detection on feature maps of different scales. As the feature map becomes larger, it will contain more contour and geometric information, facilitating the detection of small targets.  
In this paper, we follow YOLOv5 to tackle the target detection task for infrared dim and small vehicle targets. Since the target is weak in brightness, small in size, inconspicuous in texture information, and susceptible to noise interference [29], the network needs to  
Figure 1: The architecture of YOLOv5. Specifically, the Conv module is the basic component of many important modules. The C3 module includes the Conv Module and Bottleneck module. The SPPF module consists of the Conv module, Maxpool layer, and Concat layer. Max pooling with a kernel size of 5, stride of 1, and padding of 2 maintains consistent dimensions with the convolutional module.  
increase the contextual information of the target and increase the receptive field to improve the performance of detection. Based on this analysis, we propose multiscale YOLOv5-AFAM with three improvements based on YOLOv5: (1) Propose the AFAM attention mechanism; (2) Improve the feature fusion scale; (3) Optimize the downsampling strategy.
## 3 Geophysical Survey
### Time-Domain Electromagnetic (TDEM) Survey  
The TDEM equipment used in this work was the AIE-2 model of the ELGEO Research & Production Company. This device has a maximum output power of 200 W and a current intensity of up to 10 A. The TDEM receiver is based on a 16-bit analogue-digital converter with a signal processor that provides an analogue-digital conversion immune to input voltage noise and real-time signal pre-processing [36].  
In the field campaign, a total of six TDEM measurement stations were established in the NW-SE direction, coinciding with the seismic line analysed. The points located in Figure 2 indicate the centres of the reading stations for square loops of 100 and 200 m on each side. At each of these points, measurements were taken with central-loop and single-loop devices, and the measurement parameters (intensity, time and voltage) were varied, allowing the efficiency and stability of the different configurations to be compared. Thus, to reach research depths on the order of 200 m (at the first three stations), transmitter loops of 100 \(\times\) 100 m were used. For the last three stations, which were designed to study the graben block of the fault, larger transmitter loops (200 \(\times\) 200 m) were used since a greater Palaeozoic basement depth was expected. In all cases, a 6 mm\({}^{2}\) section cable was used, which reduces its resistance and, therefore, allows a higher effective current intensity to be achieved. At those stations in which a central loop device was used, the receiving coil had dimensions of 20 \(\times\) 20 m and a loop of five turns. For the visualization and processing of the different curves, the software TEMBIN version 1.1 was used. The modelling and inversion process was carried out with the software ZondTEM1D version 1.1.
## 3 Results
### Optimal Absorption in Diffuse Field  
As we have shown, using a locally reacting material, perfect absorption can only be achieved at one single angle for a given frequency. However, the structure can be optimized to maximize the absorption at a broad range of angles, even when we know from Equation (10) that the absorption will be the unity only at a single angle. To use a generalized criteria, we make use of the absorption in diffuse field [20] at frequency \(f_{0}=300\) Hz, given by  
\[\alpha_{\text{diff}}(f_{0})=2\int_{0}^{\pi/2}\alpha(\theta,f_{0})\cos(\theta) \sin(\theta)\text{d}\theta, \tag{11}\]  
which takes into account a broad range of incidence angles, weighted to mimic diffuse field conditions. Substituting Equation (10) into (11) yields, after some algebra,  
\[\alpha_{\text{diff,PA}}=2\cos(\theta_{\text{PA}})\left[4+2\cos(\theta_{\text{PA }})\left(4\log\left[\cos(\theta_{\text{PA}})\right]-4\log\left[1+\cos(\theta_{ \text{PA}})\right]+\sec(\theta_{\text{PA}}/2)^{2}\right)\right]. \tag{12}\]  
This expression shows the absorption in diffuse field that can be achieved using a locally reacting material presenting perfect absorption at an incidence angle \(\theta_{\text{PA}}\).  
In the following, we look for the optimal geometrical parameters of a structure that maximize \(\alpha_{\text{diff}}\) at a single frequency \(f_{0}\), using the cost function \(\varepsilon(f_{0})=1-\alpha_{\text{diff}}(f_{0})\). The algorithm and constraints were the same as in the previous section. Figure 6a shows the optimized absorption using three HRs as a function of the incidence angle. The absorption in diffuse field is maximized for a structure showing perfect absorption at an angle \(\theta_{\text{PA,diff}}=50.34^{\circ}\approx 0.28\pi\) radians, and this leads to a maximum diffuse field absorption of \(\alpha_{\text{diff,MAX}}=0.951\). It is worth noting here that this result is independent of the number of resonators and the kind of material; the only condition is that the structure must be a locally reacting one. Several tests were performed using 1, 2, and 3 HRs, and the results were the same. In fact,  
Figure 5: Complex frequency representation of the reflection coefficient for the metamaterial with \(N=3\) HRs, in logarithmic colour scale, for different angles of incidence. (**a**) \(\theta=0^{\circ}\); (**b**) \(\theta=65^{\circ}\); (**c**) \(\theta=85^{\circ}\); (**d**) \(\theta\to 90^{\circ}\). The trajectories of the poles and the zeros of the reflection coefficient as the incidence angle is increased are plotted in coloured lines, corresponding to the blueish and reddish colour bars, respectively.  
it is enough to use Equation (10) to obtain these limiting values: as Figure 6b shows, the retrieved \(\theta_{\text{PA,diff}}\) angle corresponds to the maximum of the curve given by Equation (12). In summary, the angle \(\theta_{\text{PA,diff}}\) is the the optimal to obtain the maximal value of absorption in diffuse field \(\alpha_{\text{diff, MAX}}\) for any locally reacting material.
# Detection and Mitigation of RPL Rank and Version Number Attacks in the Internet of Things: SRPL-RP
## 4 Simulation and Results Analysis
### Simulation Setup  
To implement and measure the effectiveness of the proposed secure protocol, the Cooja simulator program on Contiki Operating System (Contiki OS 3.00) was used [36]. This is a networking system and a multitasking operating system for IoT devices. Hence, this system was used for creating different simulations in this research paper. We conducted three types of topologies to analyze the security effectiveness of the proposed protocol and the network performance: grid-center topology, Grid-random topology, and random topology. The nodes were placed in a 100 m x 100 m area, and each node was distributed in a transmission range of 50 m that maintained the linkage between nodes and the interference range of 100 m based on the Unit Disk Graph Medium (UDGM)-Distance Loss model (link failure model). These parameters are the default settings of the Cooja simulator. The network topology can be deployed in E-applications [37]. Table 2 shows a summary of the simulation model.
## 2 Method and Materials
### Establishment of the Numerical Simulation Models
#### 2.2.3 Meshing and Grid Validation  
ANSYS Meshing 2022 R2 software was employed to generate the mesh for the model. The sweep meshing method was applied to the filter bags and filtered gas, while the tetrahedron meshing method was used for the chambers, inlet channels, and hopper. To improve the accuracy of the numerical simulation, local refinement was applied to the inlet, outlet, and filter bag surfaces, and three layers of expansion were added to the wall boundaries.  
Taking the single-chamber model as an example, the base mesh consisted of 8.23 million grids. Mesh refinement and coarsening were subsequently performed, resulting in four configurations with varying mesh densities and grid counts. Numerical simulations were conducted under constant boundary conditions and simulation parameters. Since the primary focus of this study is the uniformity of airflow within the filter chamber, the outlet velocity of filter bag X\({}_{8}\)Z\({}_{7}\) was selected as the evaluation metric for comparison, as shown in Figure 3a. The results indicate that when the grid count reaches 14.91 million, the outlet velocity simulation results for bag X\({}_{8}\)Z\({}_{7}\) stabilize, indicating that further increases in the number of grids have a negligible effect on simulation accuracy. Therefore, considering mesh quality, computational accuracy, and efficiency, 14.91 million grids were selected for the subsequent simulations. The mesh division of the single-chamber model and a zoomed-in view of the grid division are shown in Figure 3b.  
Figure 2: Porous medium resistance performance of filter bags and comparison of simulation models: (**a**) Resistance curve of the tested porous medium; (**b**) velocity contours for the porous zone and porous-jump models in the single filter bag model.
# Software Application for Automatic Detection and Analysis of Biomass in Underwater Videos
## 2 Software Application Description
### Application Server and Database: Backend  
The application server's functionalities include storing and managing multimedia files (videos), managing databases, and managing the application programming interface (API) for communication between the client and server. It was developed in Python for easy integration with DeepViewCore and functions under the open-source framework Django [36].  
As for the database management system, we chose to use MongoDB in its Community Edition 5.0 version. MongoDB stores data as documents in JSON format. The JSON format and Django library allow a direct mapping between the stored data and the native Python objects of the application. Additionally, it performs integrity checks and validations to ensure that only \"clean\" data is stored in the database. We have defined a video model class that contains the data (document ID, created time) concerning the media file. In other words, once the processing of the video is started, a document with the parameters, including document ID, time stamp, path, status, results of processing, and computation time, is generated. On the other hand, each video model class has an associated document where the processing parameters are stored, that is, the threshold, video link, and top-hat filter dimension.  
For the communication between the server and the client of the application, an API (application programming interface) was designed using the REST (representational state transfer) architecture [37]. This is based on the HTTP application layer protocol, which allows for data exchange through JSON objects. In order to allow clients to access and manage the current state of resources on the server, this API is implemented through endpoints named 'video' and 'parameters', which accept requests by both GET and POST methods.  
The API, to respond quickly to the client request for the underwater video stream, creates a concurrent thread for each video request. These threads share the memory of the main thread, which permits the API, in an easy way, to manage them from the server code. Additionally, the API displays the available videos through an element of the Django fork  
Figure 6: Detected items (20) in an underwater scene. Numbers represent the item size (area) in pixels.  
client. However, this client does not support requests for video ranges dynamically. This is useful because it allows the user to conduct a partial analysis of the whole video. To solve this, we have implemented a new Django element (hosted on the Django GitHub repository) [38], which allows the client, in addition to requesting the whole video from the server, to make partial requests for the video.
###### Abstract  
Post-earthquake structural rehabilitation faces critical challenges from aftershock-induced cumulative damage, particularly through residual displacement accumulation that compromises structural realignment feasibility. While residual displacements serve as pivotal indicators for repair-or-replace decisions, the amplification effects of aftershocks on such displacements remain systematically underexplored. This study investigates residual displacement demands of bilinear single-degree-of-freedom (SDOF) systems subjected to mainshock-aftershock sequences. A novel metric is proposed, defined as the maximum residual displacement considering both isolated mainshock and full sequence scenarios, normalized against peak inelastic displacements (termed residual displacement ratio) for predictive analysis. The influence of sequence characteristics (duration, frequency content, aftershock intensity) and structural properties (post-yield stiffness ratio, displacement ductility, natural period) on residual displacement ratios is evaluated. Statistical analysis reveals that aftershocks amplify mainshock-induced residual displacements in the statistical mean sense, with an observed maximum increase reaching up to 72%. The mainshock with stronger aftershocks tends to result in larger residual displacement ratios. A constant-ductility residual displacement ratio response spectrum is finally developed for the repairability assessment of structures against mainshock-aftershock sequences in terms of residual displacements.  
SDOF systems; residual displacement ratios; mainshock-aftershock sequences; aftershock intensity; response spectra +
Footnote †: journal: Applied sciences
# Optimization of Sensor Positions and Orientations for Multiple Load Case Scenarios
## 4 Results  
The optimization of sensor placements for strain and temperature distribution monitoring was carried out using genetic algorithms for different numbers of sensors: single, dual, and triple configurations. For each configuration, ten independent optimization runs, each consisting of 100 generations, were performed. In this example, the values of the elevation angles \(\phi_{j}\) are not provided, as this parameter was constrained to zero in order to eliminate the sensor orientation out of structural domain (sensors are placed on a flat surface).  
The number of sampling points along the length of a sensor was set to \(v=20\) (see Equation (9) and Figure 5). The small number \(e\) (Equation (11)) was set to \(10^{-6}\).  
The sensitivity coefficients \(\alpha\) and \(\beta\), used in the strain model to take into account perpendicular strain components, are based on the literature data. The values depend on sensor type, size, and material and are defined by sensor producers. Nowadays, foil strain gauges are characterized by a very low sensitivity for the transverse strain, less or equal to \(1\%\) for the sensitivity in the axial direction, as explained in [30]. The grid geometry and manufacturing for such gauges led to a nearly identical response in orthogonal directions (\(\alpha\approx\beta\)). According to the engineering practice outlined in [31,32], we consider \(\alpha=\beta=0.01\). For FBG sensors the sensitivity can be negative or positive up to a few percent [33].
# PDTR: Probabilistic and Deterministic Tree-based Routing for Wireless Sensor Networks
## 4 Proposed Method PDTR
### Updating the Probabilistic Routing Phase  
After some time, the residual energy of each node starts to decrease, depending on the quantity of the transmitted packet by each sensor node. Hence, the necessity of updating the tree-routing in order to balance energy consumption and prolong the network lifetime is shown. PDTR proposes a strategy to reach this goal. Each time any node \(n_{i}\) loses \(\ell_{e}\) of its energy, and if it is not a leaf, then it will be defined as a node that needs help, and selects the child with the lowest residual energy to be removed from its children. Then, the selected child node has to probabilistically select a new-parent (forwarder) from its candidate parents, according to the updated probabilistic routing table. This process will be repeated for the node \(n_{i}\) each time it loses \(\ell_{e}\) of its energy, until the value of its remaining energy is less than \(\ell_{e}\).
## 3 Materials and Methods
### Data Analysis  
#### 3.5.1 GC-MS Data Extraction  
The GC-MS raw data files were exported in CDF format and imported into PARADISe (Version V6.0.0.14) software. PARADISe is based on PARAFAC2 modelling, which allowed simultaneous deconvolution of pure mass spectra of peaks and integration of areas of deconvoluted peaks for all samples. Resolved peaks were identified by matching their deconvoluted pure mass spectra and retention index (RI) [73; 74] against the NIST spectral library (NIST14, version 2.2, National Institute of Standards and Technology). The compounds' RI (NIST spectral library) was compared using the calculated RI. A cubic spline interpolation using the n-alkane series (C7-C25), which was run under the same conditions in GC-MS, was used to calculate the RI of each compound [75]. The compounds were further selected based on their R match greater than 800. On a few occasions, compounds with R matches below 800 were chosen because they co-eluted with background or air peaks. If a compound only had a calculated RI or if its identification was not confident, VOCs were considered \"unknown\". Finally, the data matrix of the peak area for each compound (including not-identified peaks (unknown)) for each sample replicate was exported from PARADISe.
## 2 Materials and Methods  
### Site Description  
The pot experiment was conducted at the experimental station of Jilin Agricultural University (43\({}^{\circ}\)48\({}^{\prime}\)40\({}^{\prime\prime}\) N, 125\({}^{\circ}\)23\({}^{\prime}\)38\({}^{\prime\prime}\) E), Changchun City, Jilin Province, Northeast China, from 2021 to 2022. This study aimed to enhance the quality of soda saline-alkali soil in the western region of Jilin Province to facilitate its application in rice-seedling cultivation. The soda saline-alkali soil utilized in the experiment was sourced from the top 20 cm of a field with the original saline-alkali soil in Qian'an County, Songyuan City, Jilin Province (44\({}^{\circ}\)52\({}^{\prime}\)49\({}^{\prime\prime}\) N, 124\({}^{\circ}\)02\({}^{\prime}\)32\({}^{\prime\prime}\) E), which was located within the Songnen Plain and was classified as a typical meadow alkali soil. The location of the soil sampling point is shown in Figure 1. The soil was sieved through 2 mm mesh to thoroughly homogenize and remove plant residues and stones. The soil had a pH value of 9.19, an organic carbon content of 8.57 g kg\({}^{-1}\), an electrical conductivity of 0.288 ms\(\cdot\)cm\({}^{-1}\), an exchangeable sodium content of 5.59 cmol\(\cdot\)kg\({}^{-1}\), a cation exchange capacity (CEC) of 9.32 cmol\(\cdot\)kg\({}^{-1}\), and a total alkalinity of 1.22 cmol\(\cdot\)kg\({}^{-1}\). All testing data were derived from the test results of the Jilin Agricultural University experimental station.  
The corn straw used in this experiment was sourced from the teaching experimental field of Jilin Agricultural University. After crushing, the corn straw measured approximately 1 cm in length and was mixed evenly. The organic carbon content was measured at 396.7 g\(\cdot\)kg\({}^{-1}\), while the total nitrogen, total phosphorus, and total potassium contents were 6.59 g\(\cdot\)kg\({}^{-1}\), 2.20 g\(\cdot\)kg\({}^{-1}\), and 7.32 g\(\cdot\)kg\({}^{-1}\), respectively.  
In the rice-seedling experiment, the control group utilized the farmer's seedling-raising soil (ZCK) alongside JCK. This soil was sourced from the seedling-raising area at Taohe Farm, situated in Linhai Town, Taobei District, Baicheng City, Jilin Province,  
Figure 1: Research area and soil sampling site.  
where agricultural seedling-conditioning agents had been incorporated by farmers. The soil was sieved through 2 mm mesh to thoroughly homogenize and remove plant residues and stones. The soil exhibited a pH value of 6.27, an organic carbon content of 18.01 g\(\cdot\)kg\({}^{-1}\), an electrical conductivity of 2.69 ms\(\cdot\)cm\({}^{-1}\), alkali-hydrolyzable nitrogen of 156.5 mg\(\cdot\)kg\({}^{-1}\), available phosphorus of 42.48 mg\(\cdot\)kg\({}^{-1}\), available potassium of 175.2 mg\(\cdot\)kg\({}^{-1}\), exchangeable sodium of 0.53 cmol\(\cdot\)kg\({}^{-1}\), and a total alkalinity of 0.23 cmol\(\cdot\)kg\({}^{-1}\).