esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Migration of Ca Ca Cations
Although this study is primarily computational, the insights that could be derived from the simulations are highly valuable for directing future experimental research. The findings from this study can support the preparation of materials with tailored properties by predicting how different cations influence the structural stability in clinoptilolite. Figure 8: Ca-dominant structures: the structure in (**a**) shows the avoidance of Al1 if Al3 is present, from the a-direction; the configuration in (**b**) shows the movement of Ca atoms in channels A and B toward channel C and the Ca’s strange avoidance of Al3, and the high-energy configuration in (**c**) portrays how Ca1 distances itself from Al1, which does not happen if Al2 atoms are present in the 10 MR of channel A: the different configurations showed how unstable Ca movement becomes when it is placed in channel A. A key challenge in natural zeolites such as clinoptilolites is the coexistence of different cations at the same site and the limited investigations into zeolite synthesis, which contribute to the incomplete understanding of diffusion and other processes in clinoptilolites. The presence of various cations at the one site limits and sometimes blocks the diffusion of even small molecules such as CO\({}_{2}\). Therefore, investigating monocationic systems, such as Na-clinoptilolites and Ca-clinoptilolites, provides a valuable starting point for experimental studies. For instance, calcium-exchanged clinoptilolite showed a promising separation of nitrogen/methane mixtures [27] via pressure swing adsorption (PSA) which, when used with a 4A molecular sieve, showed desirable results under very limited conditions. This is because the maximum performance of such a method depends on the adsorption characteristics of the sorbent material. Therefore, such investigations could not only enhance the understanding of cation-specific interactions but also contribute to optimizing clinoptilolite-based materials for advanced separation, adsorption applications such as PPCPS removal from different water environments, effective ion exchange processes, etc. Furthermore, by integrating computational results with the experimental data, researchers can refine theoretical models, improving their accuracy and thus deepening the understanding of such complex systems.
# Dynamic Response of Dual-Disk Rotor System with Uncertainties Based on Chebyshev Convex Method
## 2 Dynamic Equation of Motion of the Rotor System
### Modeling the Elastic Shaft Element  
The main response mode of rotor system is bending vibration. Hence, the influence of the axial force and torsional vibration of the elastic shaft is not considered. The elastic shaft can be divided into multiple Euler-Bernoulli beam elements that do not consider the influence of the shear deformation. Figure 1 shows an elastic shaft element. The nodal displacement vector can be defined as \(\{U_{s}\}=\{x_{A},-\theta_{xA},y_{A},\theta_{yA},x_{B},-\theta_{xB},y_{B},\theta _{yB}\}^{\text{T}}\) in fixed coordinates frame.  
Based on Lagrange equation, the rotor motion equation can be established by FEM. The differential equation motion of elastic shaft element is  
\[[M_{s}]\Big{\{}\ddot{U}_{s}\Big{\}}+\Omega[J_{s}]\Big{\{}\dot{U}_{s}\Big{\}}+[ K_{s}]\{U_{s}\}=[F_{s}] \tag{1}\]  
where, \([M_{s}]\) is the uniform mass matrix, \(\Omega\) is the rotate speed of the shaft element, \([J_{s}]\) is the rotary matrix and \([K_{s}]\) is the rigidity matrix, \([F_{s}]\) is the generalized force matrix.  
It should be noted that the different coordinate order will cause different \([M_{s}]\), \([J_{s}]\) and \([K_{s}]\), but the same result of \(U\) can be obtained after transformation.
## 3 Results  
### Data Selection  
In this study, 40 m long tunnel point-cloud data from an operational subway shield-tunnel section were selected to validate the denoising effect of the proposed method. The shield-tunnel segment in this section has a width of 1.5 m, an internal radius of 2.7 m, and an external radius of 3 m.  
In this study, the Amberg Clearance GRP 5000--Profiler 6012 mobile 3D laser scanning system was used for data acquisition. The system includes an Amberg Profiler 6012 high-speed phase laser scanner, a Distance Measurement Instrument (DMI), a high-precision inertial navigation unit, an industrial-grade laptop computer, and a composite frame accessory, as shown in Figure 11.  
The distance measurement principle of this model of scanner is phase-based interferometric ranging, which determines distances by analyzing the phase shift of reflected laser signals. The scanner operates at a maximum frequency of 200 Hz, with a scanning range covering a 360\({}^{\circ}\) horizontal field of view and a vertical field of view of 120\({}^{\circ}\) or greater. It supports a maximum measuring distance of 119 m and a maximum sampling rate of 1.016 million points per second. The ideal measurement accuracy is 0.1 mm. In actual scanning operations, the device moves forward at a speed of 4 km/h and collects 5000 points per profile. The device can operate stably in environments ranging from \(-\)10 \({}^{\circ}\)C to 50 \({}^{\circ}\)C, generating tens of thousands to millions of point-cloud data per second. The device supports multiple file output formats such as LAS and ASCII. The point-cloud data from actual tunnel scans are shown in Figure 12.  
Figure 11: Amberg Clearance GRP 5000—Profiler 6012 mobile 3D laser scanning system diagram.  
In this study, to ensure that each cross-section has enough point clouds for ellipse-fitting while maintaining an appropriate cross-sectional thickness, one cross-section is extracted every 0.1 m along the z-axis for the transformed point cloud (where the tunnel axis direction is parallel to the z-axis). For each extracted cross-section, the Huber loss function is used for ellipse-fitting, and the original point cloud is projected based on the minimum radial distance to achieve denoising. The denoising effects for individual cross-sectional point clouds and the entire-tunnel point cloud are shown in Sections 3.2 and 3.3.
# Covert Anti-Jamming Communication Based on Gaussian Coded Modulation
## 5 Implementation Problems
### Computational Complexity and Power Consumption
To satisfy the power constraints of real-time applications, hardware solutions such as FPGAs and ASICs can be utilized. FPGA implementations [46; 47] can process the SSR algorithm sufficiently fast, within the order of 10 \(\upmu\)s. The implementations in [46; 47] run the SSR algorithm for \(N=128\) within 18.3 \(\upmu\)s and 27.0 \(\upmu\)s, respectively. If we assume \(N=128\) and \(L=4\), the throughputs of the implementations become 218.6 and 148.1 kbps, respectively. ASICs can be used to further increase the throughput. A 65-nm complementary metal-oxide-semiconductor ASIC design [48] can process the GSJE algorithm for \(N=256\) within 591.36 ns. By assuming \(N=256\) and \(L=8\), a throughput of 13.53 Mbps can be achieved. It may appear that adding an ASIC for the sole purpose of eliminating jamming is too expensive. However, it must be noted that hardware costs decrease rapidly each year. Furthermore, it should be noted that achieving covert communication capability with anti-jamming protection in any battlefield situation is highly desired. Imagine a situation in which the entire communication link between allied forces is disabled and destroyed by hostile jamming. Multiple studies emphasize how detrimental it is to have even a minor information loss in mission-critical military communications [49; 50].
# A Survey on Hardware Accelerators for Large Language Models
## 2 Computational and Energy Requirements
### Computational Complexity in Inference  
While it is often presumed that the computational complexity of LLMs is predominantly a concern during the training phase, the inference phase also demands significant computational resources:  
* **Model size and memory footprint:** The large number of parameters in LLMs translates to a substantial memory footprint, even during inference. Managing and accessing these parameters efficiently requires considerable computational resources, especially when processing complex queries or large volumes of data.
* **Real-time processing requirements:** For applications requiring real-time responses, such as chatbots or interactive tools, the LLM must process input and generate output rapidly. This necessitates the allocation of significant computational resources to ensure low latency and high throughput.
* **Contextual and nuanced language processing:** The ability of LLMs to understand context and nuance in language, although less computationally intensive than training, still requires a considerable amount of processing power. This is particularly true for tasks involving long context windows or complex linguistic structures.  
In conclusion, both the training and inference stages of LLMs are characterized by substantial computational complexity. While the nature and scale of this complexity may differ between the two phases, both necessitate significant computational resources to achieve the high levels of performance and accuracy expected from state-of-the-art language models. This underlines the importance of ongoing research into more efficient model architectures and computational strategies to optimize both the training and application of LLMs.
## 3 Results and Discussion  
### Structural Properties of RD-DF  
#### 3.1.1 Ssm  
The particle morphology of RB-DF at different processing stages was observed by SEM (Figure 1). D-RB showed a laminated fiber structure, with granular particles and smooth spheres that were likely composed of protein polymers and small starch granules. Following amylase and protease digestion, the number of granular particles and smooth spheres on the surface of RB-DF was significantly reduced, showing an irregular, large, dense, and compact laminated microstructure. Subsequently, cellulase hydrolysis led to changes in the RB-DF laminated structure (Figure 1B,C). This result is consistent with the study by [24], wherein cellulase treatment is shown to be beneficial for the formation of a rough and porous structure in DF. HPH treatment uses water as transmission medium, with its powerful mechanical force breaking the regular fiber structure and forming deep grooves [25]. Thus, the loose fiber structure with small particles and irregular flakes was attributed to the combination effect of cellulase hydrolysis and HPH treatment (Figure 1D).
## 5 Discussion
### Compliance and Pertinent Legislation
#### 5.1.1 Standards by Sector  
Nonetheless, the United States has a data protection framework for various economic sectors, as outlined in Table 5. This table also details the number of breaches in the dataset that are associated to each law or standard.  
It is worth noting that the U.S. has additional laws regulating data protection, but these pertain to data categories that fall outside the scope of the dataset under study. For instance, laws like the Family Educational Rights and Privacy Act (FERPA) pertain to students' data, the Driver's Privacy Protection Act (DPPA) relates to driver's records, and the Federal Information Security Modernization Act (FISMA) applies to government data. Furthermore, laws such as the Children's Online Privacy Protection Act (COPPA) protect children's data. However, the dataset does not provide sufficient information to determine whether these laws were infringed in the reported breaches.
## Appendix A The Derivation of \(O_{D}\)  
For better qualifying and quantifying the considered MF-DFM, we derive the offset of the Doppler frequency (i.e., \(O_{D}\)) in the following. The bistatic Doppler frequency can be expressed using the target's bistatic velocity, carrier frequency, and angle components [37]  
\[\begin{split} f_{d,m}&=\frac{2V_{b}}{\lambda_{m}} cos(\beta/2)\\ &=\frac{2f_{m}V_{b}}{c}cos(\beta/2),\end{split} \tag{1}\]  
where \(V_{b}\) is the bistatic velocity of the target, and \(\beta\) is the bistatic angle. Here, we consider a case, where two frequency-separated frequency channels are used for fusion. For the clarity of notation, we define the frequency separation as follows:  
\[\Delta f_{c}=f_{d,m}-f_{d,1}. \tag{2}\]  
According to [37], the Doppler frequency resolution of CAF is defined as follows:  
\[\Delta f_{d}=\frac{1}{T_{int}}. \tag{3}\]  
As a result, the offset of bistatic Doppler frequency between two frequency-separated channels, i.e., \(O_{D}\), can be defined as follows:  
\[O_{D}=\frac{f_{d,m}-f_{d,1}}{\Delta f_{d}}. \tag{4}\]  
Substituting Equations (1)-(3) into (4), we can obtain the expression of \(O_{D}\) as the equation in (10).
## 7 Results Obtained from the Systematic Search  
Studies carried out in different parts of the planet on the use of IoT sensors are presented. However, most of the published projects are related to the application and control of water in drinking water networks and its capture by means of fog catchers, without highlighting the use of Internet of Things (IoT) sensors (Table 3).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Title** & **Ref.** \\ \hline Recording Wastewater Treatment Plant Outlet Water Discharge Using Google Sheets & [17] \\ \hline Prototipo functional IOT para determinar la viabilidad de instalacion del modelo atrapanieblas tipo chileno & [20] \\ \hline \multirow{4}{*}{Sistema IoT para el analisis de calidad de agua} & [4] \\ \cline{2-
## 2 Materials and Methods
### Architectures
#### 2.2.1 Region Selection Network  
The region selection network seeks to detect the region of the image that contains the marker band of the catheter. To select the target region, the images are first divided into \(n\) columns and \(n\) rows. Since the model is supervised, it needs the ground truth positions of the targets in the new splitting system. Therefore, the target positions are converted to the region number in which it resides. Thus, the model output is an \(n\times n\) vector with a softmax activation, with each element representing the probability of the target point being in that region.  
Figure 1: Variability of catheter images. (**A**) Different positions and orientations of the catheter. (**B**) Different orientations of the marker band. Catheter outer diameter is 4.6 mm for scale.  
The network consists of a VGG-16 [71] backbone followed by three convolution layers and three fully connected layers. The first convolution layer consists of a 3 \(\times\) 3 kernel with 1024 channels, followed by a rectified linear unit (ReLU) activation function. The second layer includes a 3 \(\times\) 3 kernel, 512 channels, and a ReLU activation function. The final layer consists of a 1 \(\times\) 1 kernel, 9 channels, and a sigmoid activation function (Figure 2). Afterward, three fully connected (FC) layers are implemented. These layers have 128, 16, and \(n\times n\) (representing the total region number) neurons. The FC layers have a dropout probability of 0.20 and 0.05 as shown in Figure 2.  
The input images were originally 512 \(\times\) 512 pixels. However, as the input to a VGG network [71] is an image with dimensions 224 \(\times\) 224 \(\times\) 3, we initially resized our images to be in accordance with the VGG dimensions. For transfer learning purposes, the weights of all layers were frozen with weights pretrained on ImageNet [72], whilst the remaining layers of the network were fine-tuned on our dataset. Training was performed with 80 epochs per 5-fold split (where the highest accuracy was achieved), with the longest training occurring with 400 epochs. The Adam optimizer was used with a 0.00001 constant learning rate, and we used a cross-entropy loss function.
## 7 Cooperative UAV-UGV Docking with Task Prioritization
### Quadcopter Dynamics  
The six-degree-of-freedom (DoF) rigid body dynamics with mass (\(m_{q}\)) and moment of inertia (\(J_{q}\)) of the quadcopter are represented in (60). The model's states are denoted as \(\mathbf{x}_{q}=\left[\begin{array}{cccc}\mathbf{p}_{q}^{T}&\mathbf{v}_{q}^{T}&\mathbf{\theta }_{q}^{T}&\mathbf{w}_{q}^{T}\end{array}\right]^{T}\in\mathbb{R}^{12}\), where \(\mathbf{p}_{q}={}_{1}\mathbf{p}_{q}\in\mathbb{R}^{3}\), \(\mathbf{v}_{q}={}_{1}\mathbf{v}_{q}\in\mathbb{R}^{3}\), \(\mathbf{\theta}_{q}\in\mathbb{R}^{3}\) and \(\mathbf{w}_{q}={}_{B}\mathbf{w}_{q}\in\mathbb{R}^{3}\) are inertial position, inertial velocity, Euler angles, and angular velocity, respectively. Given the Euler angles, the rotation matrix for the quadcopter is denoted as  
\begin{table}
\begin{tabular}{l l} \hline \(\mathcal{F}_{B}\) & body fixed reference frame (quadcopter) \\ \(m_{q}\) & mass of the quadcopter \\ \(\mathbf{J}_{q}\) & moment of inertia of the quadcopter \\ \(x_{q}\) & state vector of quadcopter \\ \({}_{1}C_{q}(\mathbf{\theta}_{q})={}_{1}C_{q}\) & rotation matrix for quadcopter \\ \(f_{q}\) & total thrust generated by motors in the body frame \\ \(\mathbf{t}_{q}\) & moments generated on the body \\ \(\mathbf{u}_{q}\) & input vector for the quadcopter model \\ \(g\) & acceleration due to gravity \\ \([{}_{1}\mathbf{u}_{2}\mathbf{u}_{3}]^{T}\) & unit vectors representing the body frame \\ \(m_{r}\) & mass of the rover \\ \(J_{r}\) & moment of inertia of the rover \\ \(\mathbf{x}_{r}\) & state vector for rover dynamics \\ \({}_{1}C_{r}\) & rotation matrix of the rover \\ \(\mathbf{u}_{r}\) & inputs to the rover \\ \hline \end{tabular}
\end{table}
Table 2: Summary of key variables used for description of the quadcopter and rover kinematics and dynamics.  
Figure 15: \(\mathbf{r}_{B}\) plot along with leg motion with respect to \(\mathcal{F}_{l}\).  
\({}_{t}\mathbf{C}_{q}(\mathbf{\theta}_{q})\ =\ _{t}\mathbf{C}_{q}\). The inputs of the system are denoted as \(\mathbf{u}_{q}=\left[\begin{array}{cc}f_{q}&t_{q}^{T}\end{array}\right]^{T}\in \mathbb{R}^{4}\), where \(f_{q}\) is the total thrust generated by the motors on \(\mathcal{F}_{B}\) and \(\mathbf{t}_{q}\in\mathbb{R}^{3}\) is the column matrix of moments generated on the body defined in \(\mathcal{F}_{B}\). \(\mathbf{E}_{q}(\mathbf{\theta}_{q})\) is the mapping between \(\mathbf{w}_{q}\) and \(\hat{\mathbf{\theta}}_{q}\) such that \(\mathbf{w}_{q}=\mathbf{E}_{q}(\mathbf{\theta}_{q})\hat{\mathbf{\theta}}_{q}\) for pre-defined rotation sequence. The quadcopter properties are taken from the work [45].  
\[\dot{\mathbf{x}}_{q} = \left[\begin{array}{c}\dot{\mathbf{p}}_{q}\\ \ddot{\mathbf{v}}_{q}\\ \dot{\mathbf{e}}_{q}\\ \ddot{\mathbf{w}}_{q}\end{array}\right]\ =\ \mathbf{f}(\mathbf{x}_{q},\mathbf{u}_{q}) \tag{60}\] \[= \left[\begin{array}{c}\mathbf{v}_{q}\\ \dot{\mathbf{w}}_{3}-(1/m_{q})_{t}\mathbf{C}_{q}f_{q}\mathbf{u}_{3}\\ \mathbf{E}^{-1}(\mathbf{\theta}_{q})\mathbf{w}_{q}\\ \mathbf{J}_{q}^{-1}(-\mathbf{w}_{q}\times\mathbf{I}_{q}\mathbf{v}_{q}+\mathbf{t}_{q})\end{array}\right]\]
## 4 Discussion
### AMS Checklist Implementation  
The initial results obtained from the use of our AMS checklist across 19 sites revealed large variations in the AMS's capacities and local needs regarding support. Several healthcare sites had available expertise on infection management and stewardship, education and practical training, up-to-date recommendations for infection management, national antimicrobial prescribing guidelines, and continual monitoring and surveillance. Comparatively few sites, however, reported the presence of senior management leadership towards AMS, published AMS protocols, accountability structures, regular reporting and feedback, and routine ward rounds focused on infection and antimicrobial prescribing.  
The results on the use of the checklist post-CwPAMS programme, including a range of interventions [9; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30], indicate that the greatest improvements were observed in thecore elements relating to senior AMS leadership, accountability, and responsibility. Smaller improvements were reported with regard to the availability of AMS expertise, education and practical training, monitoring and surveillance, and other AMS actions. The standout items included the availability of formal organisational multidisciplinary structures responsible for AMS and the conduct of point-prevalence surveys (79% and 71% improvements, respectively). At the other end of the spectrum, there were the availability of laboratory and imaging services and the presence of financial support for AMS activities, which only showed 7% and 14% improvements, respectively. The post-intervention checklist also demonstrated a better integration of pharmacists, nurses, and all clinical staff groups in AMS committees across the project sites. Local variations in improvements could be attributed to several factors, including a political and administrative will, workforce capacity and, importantly, funding.  
The findings obtained through our contextualised AMS checklist mirror the published AMS reports in LMICs, which highlight the presence of national antimicrobial prescribing guidelines in the countries where the sites are located [21; 31; 32; 33] but identify the challenges in AMR-specific education and training, diagnostic facilities, regulation of safety and efficacy of medications, and shortages of healthcare personnel and expertise [34; 35; 36; 37]. Although these studies highlight the overall gaps in AMS implementation in LMICs, the differences observed across the regions, hospitals, and sites also demonstrate the need for case-by-case evaluations of AMS programmes for the development of appropriate and sustainable solutions. Our study suggests that the newly developed AMS checklist will enhance such evaluations across the wider region of Sub-Saharan Africa.