esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Optimal sensor placement (OSP) for arch dams is important to ensure their long-term service, but the evolution of structural states and material properties is less considered at present. This paper considers the effects of cracks, contraction joints, and elastic modulus zoning degradation of dam bodies, proposing an OSP framework based on an improved artificial hummingbird algorithm (IAHA). First, considering the compressibility of reservoir water, a finite element model of the arch dam-reservoir-foundation system is established. Second, by introducing improved circle chaotic mapping and Levy flight, IAHA is proposed. Then, a method for selecting the optimal number of sensors (ONS) based on modal assurance criterion (MAC), fitness values, and maximum singular value ratio (S) criteria is proposed. Finally, an OSP framework for arch dams with cracks is constructed and verified through a concrete arch dam. The final sensor placement is carried out for the current state of this arch dam after 45 years of operation, and the ONS is selected to give the results of the spatial location of the sensors. The results indicate that the OSP performance of the arch dam based on IAHA is the best, with MAC-MAX, MAC-AVE, MAC-RMS, and S values of 0.1521, 0.1069, 0.5478, and 1.8591, respectively, showing the best performance among the selected algorithms. The method of selecting the ONS based on MAC, fitness values, and S criteria is reasonable and feasible, considering that the changes in structural states and material properties have varying degrees of influence on the number and spatial location of sensors. The research results of this paper can provide effective technical support for the health diagnosis of arch dams with cracks and provide references and new ideas for structural health monitoring.  
technology for the dynamic testing and online monitoring of modern complex engineering structures [7; 8; 9]. In modal testing, improper sensor placement may affect the identification of modal parameters, so it is particularly important to determine the optimal number and locations of sensors for the structure. In traditional dam dynamic tests, sensor placement is based on engineering experience, which does not guarantee the optimal placement of sensors [10; 11; 12]. Therefore, the study of OSP for concrete arch dams has become an important topic [13; 14].
## 5 IETF ROLL Working Group
### Leveraging Upon CoAP to Realize the IoT
#### 6.3.1 Discovery and Naming  
_Resource Directory_ (RD) servers provide a way to collect resource descriptions from multiple servers into one central location. To facilitate setting up such a RD, draft-shelby-core-resource-directory identifies needed protocol elements [112].  
In [97], CoAP is used to facilitate discovery and deployment of constrained devices. The proposed approach makes use of CoAP and combines it with DNS in order to enable the use of user-friendly fully qualified domain names (FQDN) for addressing sensor nodes. It also includes the translation of HTTP to CoAP, thus making the sensor resources globally discoverable and accessible from any Internet-connected client using either IPv6 addresses or DNS names both via HTTP or CoAP. This approach can be enhanced by publishing the discovered resource to one or more RDs.  
Another example of how DNS can be used in a hierarchical fashion to enable easier access to resources is described in draft-ietf-core-groupcomm [113]. Here access to groups of resources can be provided via the use of Group FQDN that are uniquely mapped to a site-local or global multicast IP address via DNS resolution. For example the Group FQDN all.bldg6.example.com would refer to _all nodes in building 6_ and the Group FQDN all.west.bldg6.example.com would refer to _all nodes in the west wing of building 6_.
## 4 Discussion
To reduce the pixelation effect, the 256 \(\times\) 256 matrix size can be increased (e.g., to 512 \(\times\) 512) by padding k-space data with zeros before taking the inverse Fourier transformation to the image domain. Alternatively, recent studies have investigated deep learning-based super-resolution networks [48; 49], which could be a solution to improve the spatial resolution without compromising the temporal resolution. In addition, since manual contouring is a very onerous process, radiation oncologists may have accepted more errors for the non-clinical contours. Future studies could also benefit from including more patients, experts, and tumor sites.
# Molecular Mechanisms of Topography Sensing by Osteoblasts: An Update
## Appendix A Topographies Production
### Lithography  
This is a set of the most commonly used techniques to produce surface topographies on nano- or microscale. It is based on producing reliefs on surfaces for various applications, especially in studies of in vitro cell behavior. Some approaches include direct writing of the pattern, using lasers, or so-called natural lithography including colloidal lithography and phase separation [106]. The technique has been somewhat modified through time and can apply electron beams, lasers, UV light or other sources to produce topographies.  
Photolithography--The classical and well-established lithography method consists of applying a collimated light (UV or visible) to a resist that can be cured by light (photoresist). Using a mask, incident photons can also produce patterns in the resist by a photon-mediated corrosion (photoetching). The final resolution depends on the wavelength used [107; 108].  
Colloidal lithography--Emerging from the need for techniques to achieve nanotopographies in higher resolutions, colloidal lithography was discovered and applied worldwide. Using colloidal particles or solutions, topographies can be produced to generate unordered motifs. Colloidal crystals or nanoparticles can adhere to the desired surface in a self-organized deposition, or by charge-attraction, and be etched away leaving a pattern [106; 109; 110].  
Electron beam lithography (EBL)--is a precise method that uses a high-energy electron beam to produce patterns over surfaces. As it can produce controlled topographies with a high level of organization and resolution, EBL is less expensive (material and/or equipment): it reduces possible changes in surface chemistry and the risk of electron scattering effect (beam or backscattering). EBL does not require a mask but an electron-sensitive resist, or it can just act directly on the material of interest [111; 112; 113].  
Stereolithography--consists in a 3D-printing-based technology to produce controlled topographies. It requires a computer and a 3D drawing software to design patterns as a template. The incidence of a light or a laser beam with the designed pattern on a liquid photoresist induces photopolymerization in the desired regions. After the first layer formation, then, several turns of coating with liquid resin and illumination produce a 3D pattern with a delimited area [114].  
Laser interference lithography (LIL)--with no need for a mask, this technique is based on laser beams traveling through lenses and filters to reach a mirror perpendicular to the surface of interest. This pathway gives more strength and focus to the beams. When reaching the mirror, the laser tilts to the photoresist, producing lines and or cavities in the polymer. LIL can achieve good resolutions, but fails in producing more versatile patterns [115; 116].
# Development of Enabling Technologies for Ku-Band Airborne SATCOM Phased-Arrays
###### Abstract  
In the paper the development of a fully electronic transmit-receive phased-array antenna system in Ku-band for aircraft communications via satellite is presented. Particular emphasis has been placed in the improvement of the following key elements: a dual-polarization self-diplexing radiating element, a transmit/receive active module with full polarization agility based on a digital vector modulator and a SiGe multinode MMIC. The optimized antenna elements enable a significant improvement towards the realization of a future affordable commercial product for satellite communications.  
active phased arrays; phased array antennas; MMIC; mobile antenna systems; satellite communications +
Footnote †: journal: Electronics
# Energy and Distance Based Multi-Objective Red Fox Optimization Algorithm in Wireless Sensor Network
## 5 Proposed Method  
ED-MORFO was used to establish clustering and routing in this study. The algorithm's searching capabilities were combined with the fitness function values. Four different fitness function parameters (residual energy, distance, network coverage, and the degree of nodes) were taken into account throughout the clustering process [30]. By considering two alternative distance functions, the network's energy usage was considerably reduced. Furthermore, node failure was avoided in the transmission path, by taking into account the nodes' remaining energy. By preventing node failure, packet loss when data are transmitted was minimized. The major goal of this study was to reduce energy depletion, in order to extend the network's lifespan. Figure 2 depicts a general flowchart of clustering and routing.  
The steps for the flowchart are as follows:  
* A clustering technique is designed to distribute the system into groups;
* The nodes are arbitrarily placed in the concerned zone at first, then mobile nodes are indicated as a dynamic that is entirely reliant on the position of the node. ED-MORFO is used to cluster networks in this case. At that moment, the CH is determined, based on the distance between neighbors, residual energy, and the distance to the base station location, among other factors;
* Routing methods generated using the suggested ED-MORFO are used to create the ideal path between the CH and BS;
* Starting with the routing process, an ideal node is identified to provide the definite route from the CH to BS;
* Once the path from the source to the destination is established, the source node sends the data in the destination route. This ED-MORFO calculates the best route by taking into account numerous objective functions such as residual energy, the distance between the CH and BS, and hop count;
* BS is frequently used to observe the residual energy of nodes. To avoid network packet loss, re-clustering/rerouting is performed frequently.
## 4 Core Algorithm and Implementation
### Structure Design of Convolution Layer
```
w: The width of the matrix to be filled stride: Each edge is filled with dimensions input_matrix: The matrix to be filled out_matrix: The filled matrix
1.for(int i = 0; i < w + 2 \(\times\) stride; i++)
2.for(int j = 0; j < w + 2 \(\times\) stride; j++)
3.if((i >= stride)&&(j >= stride)&&(i < stride + w)&&(j < stride + w))
4. output_matrix[i \(\times\) (w + 2 \(\times\) stride) + j] = input_matrix[(i - stride) \(\times\) w + (j - stride)];
5. else
6. output_matrix[i \(\times\) (w + 2 \(\times\) stride) + j] = 0; \(\quad\)]
```  
**Algorithm 3** Padding. Given the loss of the convolutional output of the \(l\)-th layer, find the loss of the convolution kernel of this layer:  
\[\frac{\partial C}{\partial w^{l}}=\frac{\partial C}{\partial z^{l}}*\frac{ \partial Z^{l}}{\partial w^{l}} \tag{9}\]  
In the equation, \(\frac{\partial C}{\partial w^{l}}\) is the corresponding element loss of the \(l\)-th layer convolution kernel. Substituting Equation (2) can be reduced to:  
\[\frac{\partial C}{\partial w^{l}(a,b)}=\sum\limits_{x}\sum\limits_{y}\beta^{l} (x,y)*\frac{\partial Z^{l}(x,y)}{\partial w^{l}(a,b)} \tag{10}\]Forward propagation into the convolution layer (Equation (1)) can be expressed as:  
\[\frac{\partial\mathcal{C}}{\partial w^{l}(a,b)}=\underset{x}{\sum}\underset{y}{ \sum}\delta^{l}(x,y)*\frac{\partial\sum_{i}\sum_{b}Z^{l-1}(x+ay+b)*w^{l}(a,b)}{ \partial w^{l}(a,b)} \tag{11}\]  
Simplification results in:  
\[\frac{\partial\mathcal{C}}{\partial w^{l}(a,b)}=\underset{x}{\sum}\underset{ y}{\sum}Z^{l-1}(a+x,b+y)*\delta^{l}(x,y) \tag{12}\]  
By comparing Equation (12) with the forward propagation of the convolution layer (Equation (1)), it can be seen that the convolution kernel loss \(\frac{\partial\mathcal{C}}{\partial w^{l}(a,b)}\) of the \(l\)-th layer is equal to the convolution output \(Z^{l-1}\) of the \(l-1\)-th layer and the convolution output loss \(\delta^{l}\) of the \(l\)-th layer. To sum up, the known loss of the convolutional output of the \(l\)-th layer can also be realized through the convolution algorithms above to find the loss of the convolution kernel of this layer. The convolutional layer operation model represented by Equations (1), (8) and (12) is integrated and mapped into the most basic hardware accelerator structure, as shown in Figure 7. By optimizing the basic structure, the overall processing performance of the convolution layer hardware accelerator is further improved. When the software model is transformed into a hardware structure by the HLS tool, the expression of the software model may have an adverse effect on the final transformed hardware accelerator structure. When translated into hardware accelerator implementation, using the pipeline command is a great way to shorten the interval between instructions in a loop, which improves throughput and reduces latency.
## 4 Results and Discussion
### Adsorption Data
#### 4.3.2 Contact Time Effect  
The adsorption efficiency of CPW, APWH, APWN, and APWP was assessed at various time intervals to evaluate the removal efficiency of Cr(VI) and MB for each adsorbent in the time range of 5-350 min. Figure 5a,b show that an increase in adsorption capacities is achieved with increasing contact time of the pollutant with the adsorbent [30]. Figure 5a,b show that APWN showed higher adsorption capacities. In Figure 5a, it was observed that adsorption was fast for 80 min for all adsorbents when Cr(VI) was removed and reached equilibrium in 120 min. The uptake of MB dye is shown in Figure 5b. The graph shows that stabilization and equilibrium were achieved within 60 min for all adsorbents. The rapid adsorption removal observed at the beginning of the process can be explained by the fact that the adsorbent initially had abundant active sites and a larger exposed surface area and pore size [31]. Over time, the pores and surface area became occupied and the active sites depleted, resulting in little to no adsorption [32].
## 2 Methods
### HoRFNet  
Figure 1 presents the HoRFNet structure, which comprises a backbone, a receptive field block, and a covariance pooling module. The basic backbone is a straightforward and efficient plain deep neural network architecture, resembling the typical VGGNet model. A receptive field block (RFB) is introduced in place of the third convolutional layer in the backbone, effectively enlarging the receptive field and enhancing the network's feature discrimination. A covariance fusion module leveraging matrix power normalization (MPN) [32] is embedded ahead of the fully connected (FC) layer to facilitate convergence and capture high-order statistics. In addition, ReLU, a nonlinear activation function, is adopted in HoRFNet to enhance its representational capacity:  
\[f(x)=max(0,x) \tag{1}\]  
Compared with other activation functions, it effectively mitigates the issue of gradient vanishing, which typically arises from the accumulation of multiple derivatives during the backpropagation process. After the 1 st, 2 nd, and 5 th layers, we apply a convolution operation with a 3 \(\times\) 3 kernel and a stride of 2, followed by a max pooling layer after ReLU activation to reduce the feature map resolution and enhance spatial invariance to  
Figure 1: The architecture of the HoRFNet model. The HoRFNet architecture comprises three primary components. First, a simple CNN flow serves as backbone. Next, a receptive field module is applied in the third convolutional layer. Finally, global statistical features of the feature maps are encoded via a second-order pooling block.  
input deformation and translation. We use a Gaussian distribution to initialize the network weights. To improve generalization performance and control overfitting, we add a Dropout layer after the first FC layer, achieving this goal by randomly dropping some neurons.
# New Approach of Blind Adaptive Equalizer Based on Genetic Algorithms
## 3 Methods
### Genetic Algorithm Setting
#### 3.5.4 Selection and Reproduction Operators  
Some qualified individuals are selected for a reproduction operation using the stochastic uniform selection. It is performed by laying out a line in which each parent corresponds to a section of the line of length proportional to its scaled value (evaluated by the objective function described above). The algorithm moves along the line in steps of equal size. At each step, the algorithm allocates a parent from the section it lands on. The first step is a uniform random number less than the step size.  
The genetic reproduction operators are the intermediate crossover and the adaptive mutation. The adaptive mutation randomly generates directions that are adaptive with respect to the last successful or unsuccessful generation. The mutation chooses a direction and step length that satisfy bounds and linear constraints. The intermediate crossover creates children by taking a weighted average of the parents. This range ensures the child to be within the boundaries of the constraint region.  
These operators were chosen because they can handle optimization problems with constraints. An extended explanation of how these operators work can be found at [40].
## 3 Results  
### Verification of VMFDI  
#### 3.1.1 VMFDI vs. Other Dryness Indicators  
The VMFDI was compared with other drought indices, such as the GRACE-DSI and SPEI, as well as the individual variables of PRE, VPD, SM, and SIF, to assess its ability in monitoring regional dryness. The GRACE-DSI is a single drought index based on TWS anomalies, while the SPEI is a two-dimensional drought index derived from the PRE and potential evapotranspiration (PET). In contrast, the VMFDI is a three-dimensional drought index built from VPD, SM, and SIF, offering a more comprehensive representation of drought conditions. The VMFDI peaked in August and was the lowest in March, aligning with seasonal variations in VPD, SM, and SIF (Figures 4f-h and 5). Over 82% of the region had positive correlations between the VMFDI and PRE, SM, and SIF, while nearly 60% showed a negative correlation with VPD (Figure 4i). This confirms that the VMFDI can effectively monitor regional drought conditions. To evaluate the performance of the VMFDI, it was compared with the GRACE-DSI, SPEI01, SPEI03, and SPEI12 using both temporal and spatial correlation analyses (Figure 4a-d, Figure 5, and Figure 6). The VMFDI showed high spatial correlation with these indices, with 65.17%, 97.47%, 94.42%, and 97.99% of the basin positively correlated with the GRACE-DSI, SPEI01, SPEI03, and SPEI12, respectively (Figure 4i). Temporal trends also aligned particularly with SPEI01 and SPEI03 (Figure 5). Correlation analysis revealed significant positive correlations with the GRACE-DSI (R = 0.19, \(p<0.05\)), SPEI01 (R = 0.39, \(p<0.05\)), SPEI03 (R = 0.40, \(p<0.05\)), and SPEI12 (R = 0.17, \(p<0.05\)) (Figure 6). These results indicate that the VMFDI is highly consistent with other drought indices, and it can be used to evaluate drought. Additionally,according to the records of the Bulletin of Flood and Drought Disasters in China, 64 months of drought occurred in the basin from 2003 to 2020. The VMFDI identified 37 months of drought from 2003 to 2020, outperforming the GRACE-DSI (10 months), SPEI01 (26 months), SPEI03 (31 months), and SPE112 (31 months) (Figure 6). This suggests that the VMFDI is more sensitive in detecting drought events. When compared to individual variables, VPD, SM, and SIF identified only 19, 22, and 22 drought months, respectively, underscoring the superiority of the VMFDI in capturing drought conditions.  
Figure 4: Significant temporal correlations between VMFDI and **(a)** SPEI01, **(b)** SPEI03, **(c)** SPEI12, **(d)** DSI, **(e)** PRE, **(f)** VPD, **(g)** SM, and **(h)** SIF (\(p<0.05\)). In **(i)**, R \(>0\) means that VMFDI results are consistent with those of SPEI01, SPEI03, SPEI12, GRACE_DSI, PRE, VPD, SM, and SIF.  
Figure 5: A comparison of the drought monitoring ability of different drought indices. In this Figure, the red, light gray, and purple dashed lines are the drought thresholds for the GRACE-DSI, SPEI, and VMFDI, respectively (classified by Table 2). The light pink columns represent the actual observed drought events in the Yellow River Basin recorded in the Bulletin of Flood and Drought Disasters in China.
###### Abstract
(2018) investigated the fabrication of activated carbon from waste tea by physical activation using steam with a specific surface area of about 995 m\({}^{2}\)/g at 800 \({}^{\circ}\)C with a water flow rate of 0.075 g/min and a constant hold time of 0.5 h [24]. [PERSON] et al. (2020) synthesized activated carbon synthesized from _Bambusa vulgaris_ striata with two-step chemical activation and thermal activation at 800 \({}^{\circ}\)C [25]. These complicated preparation processes add more cost to the final adsorbent product, resulting in losing the advantage of biomass as a cheap adsorbent. The reason for this additional cost is related to applying a high temperature of around 800 \({}^{\circ}\)C for carbonization and/or activation [26; 27; 28; 29]. However, in this work, the adsorbent preparation process involved impregnation at room temperature and drying at 105 \({}^{\circ}\)C, which saved energy during fabrication procedures. The aims of this research were to apply simple nitric acid treatment at room temperature to prepare a biomass-derived adsorbent and to characterize the adsorbent by scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), X-ray diffraction (XRD), surface area analysis and Fourier transform infrared (FTIR) spectroscopy to assess the morphology and surface properties. Furthermore, the application of the prepared adsorbent to extraction of Cr(III), Fe(III), Co(II) and Ni(II) from food sample extracts was investigated using dispersive solid-phase extraction (DSPE), and the DSPE process was optimized to achieve efficient and repeatable analytical performance.
# CubeSat Imaging Payload Design for Environmental Monitoring of Greenland
## 1 Introduction
The DISCO-2 satellite incorporates a range of modules, including the On Board Computer (OBC), which is responsible for system flight control, and the Power Distribution Unit (PDU), which orchestrates the operation of the other modules by power management [12]. The satellite includes an accurate Attitude Determination and Control System (ADCS) for orientation by using reaction/momentum wheels to counter-rotate through the conservation of angular momentum [13]. The ADCS is able to de-saturate the wheels by using magnetic coils, termed magnetorquers, to push against the Earth's magnetic field. Solar panels and maximum power point tracker modules are used for power generation, a battery system for storage, and a power conditioning and distribution unit (PDU) is used for electricity management. The satellite has two radio modules, a UHF module for Telemetry Tracking and Command (TTC), and an S-Band radio for data transmission. Finally, the payload module is used for image capture processing and analysis [14]. This paper aims to present all the steps to design an imaging payload with the requirements needed to be deployed in a CubeSat. Therefore, we describe all components involved in deciding the cameras and the processor unit according to their specific tasks. Then, we describe all the functional requirements for benchmarking cameras and the process unit to find a suitable combination. As the electronics are designed to work in the harsh environment of Low Earth Orbit (LEO), we explore different hardware configurations to provide a redundant system. We include the capability to execute accelerated ML model inference onboard the satellite, with a sample use case of a model to discriminate ice or clouds in images, dismissing those below a quality threshold. Finally, the payload must interface with the other electronics in charge of operating the satellite and communicate with the ground station.