esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Comparison and Analysis of Acceleration Response Results
### Comparison of Slope with Adjusted Elastic Modulus
#### 3.3.2 Spectral Acceleration  
Figure 12 shows the spectral acceleration measured at depths in the 1 \(g\) shaking table test and the spectral acceleration obtained from ABAQUS.  
Figure 12: Spectral acceleration for experiment and analysis in slope: (**a–e**) Sine 8 Hz at depth 0.5 m, 0.3 m, 0.2 m, 0.1 m, 0.05 m; (**f–h**) sine 10 Hz at slope 0.2 m, 0.1 m, 0.05 m.  
Figure 12a-e present the spectral acceleration of the slope model for sine 8 Hz. As the depth decreases, the trend and peak value of the spectrum acceleration between the experiment and ABAQUS are fairly close. For the left side of the peak, the numerical value of ABAQUS is greater than the experimental value. Figure 12f-h display the slope model spectral acceleration for sine 8 Hz, the general trend and peak value of the experiment and ABAQUS are very close. As the depth decreases, the spectral acceleration value on the left gradually becomes consistent.  
Figure 13 shows the spectral acceleration measured at different depths in the 1 \(g\) shaking table test and the spectral acceleration obtained from ABAQUS.  
Figure 13: _Cont._  
Figure 13a-e are the spectral acceleration of the slope model under sine 10 Hz. As the depth decreases, the experimental and ABAQUS spectrum acceleration gradually increase. The peak value of the spectral acceleration is always close, and the two tend to coincide. Figure 13f-h are the spectral acceleration of the slope model for sine 10 Hz. The general trend and peak value of the test and ABAQUS are very close. As the depth decreases, the spectral acceleration on the left side of ABAQUS changed little, while the spectral acceleration in the experimental increased, and the gap between the two gradually decreased.  
For slope models with input waves of sine 8 Hz and sine 10 Hz, the acceleration -time history and spectral acceleration are very close to the experiment results. Within the allowable range of the elastic modulus, it is possible to find adjustments to the elastic modulus to make the analysis more accurate. In this case, the acceleration magnification difference in the center of the model is not significant, but the magnification difference is obvious on the slope.
# MBMQA: A Multicriteria-Aware Routing Approach for the IoT 5G Network Based on D2D Communication
## 3 System Model
### Simulation Models  
In the execution of the MBMQA routing scheme and analysis of the performance, several models are to be discussed and the performance of each model could be evaluated. In this study, three models were utilized to estimate the value of energy consumption, mobility and lifetime of devices, and routing calculations. These models have been briefly discussed in the following sub-sections.  
```
1:Source device to \(i\), Destination device to \(j\)
2:For all entries do //source-destination pairs
3:Source device start route discovery
4:Set number of the path to \(E\)
5:Exchange HELLO and Topology Control messages
6:Gather all topology information, including devices (RB, Mobility, and QL)
7:Construct the network graph
8:If \(j\) is the destination device, then
9:Add the entry to the multi-path routing table
10:Else
11:Set the device \(j\) in the topology
12:End if
13:Add the device \(j\) to the device's map
14:For \(k\) equal to \(0\) to \(k\) equal to \(E-1\) do // all paths
15:Initiate the Multipath Dijkstra Algorithm // To evaluate the multiple paths
16:Set the max -weight to device \(j\)
17:For all devices in the device map do
18:Get the link_cost(i, fc) to the next hop devices
19:Renew the weights of devices based on the link_cost(i, fc)
20:Select the next hop device fc with minimum weight
21:Ff the address of fc = the address of \(j\) then
22:Construct the routing entry
23:Add the entry to the multi-path routing table
24:Select optimal path based on MARS metric value
25:Else
26:There is no route found
27:End if
28:Resalculate the cost of the link function
29:End for
30:End for
```  
**Algorithm 1**Route Computation for the MBMQA Algorithm
## 7 Challenges of Edge AI in Early Health Prediction and Future Directions
### Privacy and Security
#### 7.1.1 Privacy  
Here are the key aspects to consider:  
* **Data collection:** Edge AI relies on collecting vast amounts of personal health data, including medical records, biometric data, and lifestyle information. Safeguarding the privacy of this sensitive data is crucial.
* **Informed consent:** Obtaining informed consent from individuals for data collection and usage can be challenging. Clear communication about the purpose, risks, and benefits of using their data in early health prediction is essential.
* **Data anonymization:** Anonymizing health data is critical to protecting individuals' privacy. However, achieving complete anonymity while maintaining data utility for accurate predictions can be a complex task.  
Future research directions to address the privacy challenges in Edge AI for early health prediction include:  
* **Privacy-preserving algorithms:** Developing advanced algorithms that can perform accurate health prediction while preserving privacy is a promising research direction. Techniques like federated learning and differential privacy can help achieve this goal.
* **Consent mechanisms:** Exploring innovative consent mechanisms that empower individuals to have more control over their data and make informed decisions regarding data usage in early health prediction.
* **Privacy-enhancing technologies:** Investigating the use of privacy-enhancing technologies such as secure multi-party computation (SMPC), differential privacy, and homomorphic encryption to enable analysis of sensitive health data without compromising privacy.
* **Policy and regulation:** Establishing comprehensive policies and regulations that govern the collection, storage, and usage of health data in Edge AI systems. This includes ensuring compliance with privacy laws and implementing ethical frameworks.
## 1 Introduction  
Excessive nitrogen discharge from aquaculture, agricultural fertilizers, livestock waste, domestic wastewater, and industrial effluents leads to eutrophication, affecting the balance of natural water ecosystems and impacting human health [1; 2; 3]. Ammonium, nitrate, and nitrite are the principal pollutants in more than 40.0% of surface water, resulting in substandard drinking water in China and elsewhere [4; 5; 6]. Therefore, lowering nitrogen levels in wastewater is important.  
Biological processes, the most efficient and cost-effective method among nitrogen removal methods [7; 8], are the most widely used for wastewater [9; 10]. Traditional microbial domestic wastewater treatment is dependent on aerobic autotrophic nitriving bacteria that convert ammonia to nitrate (NH4\({}^{+}\)\(\rightarrow\)NH2 OH\(\rightarrow\)NO2\({}^{-}\)\(\rightarrow\)NO3\({}^{-}\)), followed by anaerobic denitrifying bacteria that convert nitrate to nitrogen gas (NO3\({}^{-}\)\(\rightarrow\)NO2\({}^{-}\)\(\rightarrow\)NO\(\rightarrow\)N2O\(\rightarrow\)N2) [7; 11]. The main disadvantages of traditional biological nitrogen removal are the complexity of the process, high energy consumption, and the need to add carbon sources. By comparison, simultaneous nitrification and denitrification (SND) processes are effective treatments with low energy consumption [12; 13; 14]. In SND, nitrification and denitrification occur concurrently in the same reaction chamber due to the actions of bacteria capable of heterotrophic nitrification and aerobic denitrification [15], which are continually being discovered [16; 17]. However,the efficient treatment of wastewater with a low C/N ratio (<4:1) is a barrier, making it difficult to meet the treatment requirements for nitrogen removal from domestic sewage under low nutrient conditions such as those found in typical domestic wastewater in China [18; 19]. Therefore, it is necessary to develop novel microbial resources with excellent tolerance of extreme environments to remove nitrogen efficiently under low-C/N conditions, thereby achieving efficient simultaneous nitrification and denitrification supporting nitrogen removal [20; 21].  
In this study, a novel low-C/N- and pH-tolerant efficient aerobic denitrifying bacterium, _Janthinobacterium svalbardensis_ F19, was isolated and characterized. The nitrogen removal performance of the strain was investigated under aerobic conditions, and the influence of various factors was tested. Finally, the application potential of this strain in a low-C/N domestic culture system was explored. This strain exhibited excellent capacity for simultaneous heterotrophic nitrification and aerobic denitrification, making it potentially suitable for nitrogen removal under low-C/N conditions.
# The Boundary Element Method in Acoustics: A Survey
## 5 Extending the Boundary Element Method in Acoustics
### Shell Elements
#### 5.2.1 Derivation of the BEM for Shells  
In this model, \(\varphi\) is the solution of the exterior Helmholtz Equation (3) in the exterior domain, surrounding an open boundary \(\Omega\). The boundary \(\Omega\) is presumed to be an infinitesimally thin discontinuity and so, at the points on the boundary, \(\varphi\) and its normal derivative \(v\) take two values, one at each side of the discontinuity. The two sides of the shell are labelled \"+\" and \"-\": it doesn't matter which way round this is, as long as it remains consistent. Hence, on the shell, four functions are modelled, \(\varphi_{+}(\mathbf{p})\), \(\varphi_{-}(\mathbf{p}),v_{+}(\mathbf{p})\) and \(v_{-}(\mathbf{p})\)\((\mathbf{p}\in\mathbf{\Omega})\), where the normal to the boundary, which orientates \(v_{+}\) and \(v_{-}\), is taken to point outward from the '+' side of the shell. However, rather than working with these functions, it is more straightforward to work with the difference and average functions;  
\[\delta(\mathbf{p})=\varphi_{+}(\mathbf{p})-\varphi_{-}(\mathbf{p}),\] \[\Phi(\mathbf{p})=\frac{1}{2}(\varphi_{+}(\mathbf{p})+\ \varphi_{-}(\mathbf{p})),\] \[v(\mathbf{p})=v_{+}(\mathbf{p})\ -v_{-}(\mathbf{p}),\] \[V(\mathbf{p})=\frac{1}{2}(v_{+}(\mathbf{p})+\ v_{-}(\mathbf{p})),\]  
for \((\mathbf{p}\in\Omega)\) and where, for simplicity, it is presumed that the boundary is smooth.  
The boundary condition is defined in a similar way as in Equation (7), but as there are double the number of unknown function, two boundary conditions are required \((\mathbf{p}\in\Omega)\):  
\[\alpha(\mathbf{p})\delta(\mathbf{p})+\beta(\mathbf{p})v(\mathbf{p})=f(\mathbf{p}), \tag{83}\]  
\[A(\mathbf{p})\Phi(\mathbf{p})+B(\mathbf{p})V(\mathbf{p})=F(\mathbf{p}), \tag{84}\]  
The integral equations that govern the field around the shell discontinuities derived from the exterior direct formulation (70) [73], by taking the limit as the boundary becomes thinner, and they are as follows:  
\[\Phi(\mathbf{p})=\{M_{k}\delta\}_{\Gamma}(\mathbf{p})-\{L_{k}v\}_{\Gamma}(\mathbf{p})\ \ (\mathbf{p}\epsilon\Gamma), \tag{85}\]  
\[V(\mathbf{p})=\{N_{k}\delta\}_{\Gamma}(\mathbf{p};\mathbf{n}_{\mathbf{p}})-\{M_{k}^{\dagger} v\}_{\Gamma}(\mathbf{p};\mathbf{n}_{\mathbf{p}})\ (\mathbf{p}\epsilon\Gamma), \tag{86}\]  
\[\varphi(\mathbf{p})=\{M_{k}\delta\}_{\Gamma}(\mathbf{p})-\{L_{k}v\}_{\Gamma}(\mathbf{p})\ \ (\mathbf{p}\epsilon E). \tag{87}\]  
There are few tests of methods based on these equations in the literature. The only known issue is at the edge, as it is likely that the solution is singular there. Therefore, mesh grading, using smaller and smaller elements close to the edge may improve efficiency.
# Software Defined Networking for Improved Wireless Sensor Network Management: A Survey
## 7 Open Challenges
### QoS and Mobility Management  
Performance metrics such as latency, reachability and mobility remain open areas of research in SDN-based management of WSN QoS. There is need for more investigations in the controller placement problems to improve these performance metrics. [PERSON] et al. [142] for example, have proposed a K-means algorithm to optimize controller placement thus minimizing network latency. Statistical analysis and artificial intelligence to predict fault occurrence in the network are also being investigated [24] for management of a robust SDN-based WSN. Investigation on the feasibility of mobility management techniques implemented in cellular networks being used in node mobility management of WSNs based on SDN is also a worthwhile area of research. Back up techniques upon node failure such as function alternation [65] can be investigated further based on leveraging SDN to enable efficient and simpler function alternation. To ensure QoS control and monitoring in the benchmarks and Key Performance Indicators(KPIs) need to integrated in the management framework developed.
# IOb-Cache: A High-Performance Configurable Open-Source Cache
## 3 Results
### Replacement Policy  
The Replacement Policy module is analyzed before the Cache-Memory module since the former is implemented in the latter. The results of the analysis are available in Table 3.  
The test is divided into 2 sections: single cache line and multiple cache lines. It is not possible to synthesize the entire cache with a single cache line, so this is only valid for the analysis of this module.  
The single cache line results show how many LUTs are required to implement the Policy Info Updater and Way Select Decoder. The number of FFs represents the number of bits the Policy Info Memory module needs to store for each set.  
The multiple cache lines results show the current actual amount of resources required to implement each replacement policy. The current implementation of the Policy Info Module (PIM) is register-based, so it requires additional logic (LUTs) to address each set. The number of LUTs is proportional to the total number of bits in the PIM.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**R.Policy** & **Ways** & **Lines** & **LUT** & **FF** \\ \hline \multicolumn{5}{c}{**Single cache line**} \\ \hline LRU & 2 & 1 & 3 & 2 \\ \hline PLRUm & 2 & 1 & 3 & 2 \\ \hline PLRUt & 2 & 1 & 3 & 1 \\ \hline LRU & 4 & 1 & 20 & 8 \\ \hline PLRUm & 4 & 1 & 8 & 4 \\ \hline PLRUt & 4 & 1 & 6 & 3 \\ \hline LRU & 8 & 1 & 81 & 24 \\ \hline PLRUm & 8 & 1 & 22 & 8 \\ \hline PLRUt & 8 & 1 & 13 & 7 \\ \hline \multicolumn{5}{c}{**Multiple cache lines**} \\ \hline LRU & 2 & 16 & 26 & 32 \\ \hline PLRUm & 2 & 16 & 26 & 32 \\ \hline PLRUt & 2 & 16 & 28 & 16 \\ \hline LRU & 4 & 16 & 70 & 128 \\ \hline PLRUm & 4 & 16 & 42 & 64 \\ \hline PLRUt & 4 & 16 & 33 & 48 \\ \hline LRU & 8 & 16 & 196 & 384 \\ \hline PLRUm & 8 & 16 & 77 & 128 \\ \hline PLRUt & 8 & 16 & 56 & 112 \\ \hline LRU & 2 & 128 & 343 & 512 \\ \hline PLRUm & 2 & 128 & 222 & 256 \\ \hline PLRUt & 2 & 128 & 212 & 128 \\ \hline LRU & 4 & 128 & 445 & 1024 \\ \hline PLRUm & 4 & 128 & 297 & 512 \\ \hline PLRUt & 4 & 128 & 259 & 384 \\ \hline LRU & 8 & 128 & 1076 & 3072 \\ \hline PLRUm & 8 & 128 & 452 & 1024 \\ \hline PLRUt & 8 & 128 & 403 & 896 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Replacement Policy resources.  
Since the LRU requires \(N\_WAYS\times log_{2}(N\_WAYS)\) bits per set, initially its size grows fast with the number of ways. In an 8-way set-associative cache with 128 lines, the LRU requires more than twice the amount of LUTs and at least thrice the amount of FFs compared with the PLRU policies. In a 2-way set-associative cache, the replacement policies had the same performance, but the PLRU's PIM requires half the number of FFs compared to the other two.
## 3 Optically Pumped Probe Design
### Probe Front End
#### 3.1.2 Cesium Absorption Cell Design  
The relatively large size of the cesium absorption cell significantly impacts the overall volume of the probe. Therefore, to miniaturize the cesium optically pumped probe, it is essential to develop technology for manufacturing smaller atomic absorption cells. When the size of the absorption cell cavity is significantly reduced, the actual filling pressure of the absorption cell becomes highly susceptible to the sealing temperature of the cavity, resulting in substantial pressure changes. For each specification of the atomic absorption cell, we precisely measured key parameters, such as intrinsic linewidth and absorption line frequency shift, maintaining the pressure control accuracy of the absorption cells within 10% across different specifications. The model of the cesium absorption cell is shown in Figure 6. The cesium bulb has a cylindrical design, containing a small amount of high-purity cesium element, with 7999.34 Pa of helium used as the buffer gas. Non-magnetic heating wires are wound around the outer wall of the cesium bulb, with a resistance of 46 \(\Omega\), designed as twisted pairs to cancel the additional magnetic field generated by the heating current, thus avoiding interference with magnetic measurements. The outermost layer of the cesium absorption cell is composed of non-magnetic thermal insulation material, designed with high-temperature-resistant insulation cotton, which improves the temperature uniformity inside the cesium bulb, thereby minimizing the resonance signal linewidth of the cesium absorption cell.
## 5 Experimental Results and Analysis
### Comparison Algorithms  
In this study, in addition to implementing SN-VNE, DDRL-VNE, CDRL, and DSCD-VNE are also implemented. In this section, we introduce DDRL-VNE, CDRL, and DSCD-VNE in detail, respectively, and design relevant evaluation indicators.  
1. DDRL-VNE: a latency-sensitive VNE algorithm based on deep reinforcement learning, aimed at the needs of SAGIN resource layout in industrial Internet of Things (IIoT) services, to quickly and effectively arrange network resources and meet users' service quality requirements. This algorithm transforms the resource scheduling problem of SAGIN into a multi domain virtual network embedding problem, and considers the impact of traffic size and hop count on latency. It constructs a learning agent consisting of a five layer policy network and extracts a feature matrix based on SAGIN network attributes as its training environment. At the same time, the algorithm designed a five-layer policy network to infer node embedding probabilities and used breadth first search strategy to complete link embedding [30].
2. CDRL: a reinforcement learning based embedding scheme for continuous decision virtual networks, aimed at solving the problem of static decision mechanisms not adapting to dynamic network structures and environments in VNE problems. The embedding of nodes with the same request is considered a time series problem, modeled using the classic seq2 seq model, and the RNN parameters are updated using the policy gradient algorithm to optimize network resource utilization efficiency. Firstly, extract the state information of the underlying network and form a feature matrix. Then, use the seq2 seq model to output the node embedding result of the current virtual network request. Finally, update the network parameters through the policy gradient algorithm to optimize the embedding strategy [31].
3. DSCD-VNE: a latency sensitive cross domain virtual network embedding algorithm designed to meet the varying latency requirements of QoS in different application scenarios. Unlike traditional two-stage algorithms (mapping nodes first and mapping links), DSCD-VNE adopts a three-stage embedding algorithm of \"node link node\". In the candidate link matrix generation stage, node resource metrics and set constraints are introduced. With the candidate link matrix as the core, Kruskal minimum spanning tree is used to select the link with the shortest delay for embedding. Path segmentation mechanism and K-shortest path algorithm are introduced to improve the utilization rate of underlying links [32].  
For the resource allocation problem, we introduce the following three metrics: (1) VNR acceptance ratio (ACC); (2) long-term revenue(LR); and (3) delay.
## 3 Results and Discussion
### Xps
#### 3.1.1 Dependence of XPS Spectra on Scratching Environments and Temperature
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Environments**} & \multicolumn{4}{c}{**Peak 1**} & \multicolumn{2}{c}{**Peak 2**} \\ \cline{2-6}  & **Temperature** & **Peak Intensity** & **Binding** & **Peak Intensity** & **Binding** \\  & **(°C)** & **(10\({}^{3}\) counts/s)** & **Energy (eV)** & **(10\({}^{3}\) counts/s)** & **Energy (eV)** \\ \hline \multirow{4}{*}{Air} & 25 & 2.18 & 55.1 & 1.50 & 51.7 \\  & 200 & 3.01 & 54.9 & & \\  & 339 & 4.80 & 54.8 & & \\  & 25 & 2.63 & 53.9 & 2.44 & 52.2 \\ \multirow{4}{*}{Benzene} & 200 & 3.60 & 54.6 & & \\  & 339 & 3.60 & 54.9 & & \\  & 25 & 3.00 & 54.5 & 2.63 & 52.3 \\ \multirow{4}{*}{Cyclohexane} & 200 & 3.83 & 54.8 & & \\  & 339 & 5.00 & 54.8 & & \\  & 25 & 4.00 & 55.2 & 1.55 & 52.1 \\ \multirow{4}{*}{Water} & 200 & 3.83 & 55.2 & & \\  & 339 & 5.00 & 55.1 & & \\  & 25 & 4.25 & 54.7 & 3.63 & 52.6 \\ \multirow{4}{*}{Methanol} & 200 & 5.82 & 55.0 & & \\  & 339 & 5.40 & 55.0 & & \\  & 25 & 4.25 & 54.4 & 4.42 & 52.5 \\ \multirow{4}{*}{Ethanol} & 200 & 5.40 & 54.9 & & \\  & 339 & 6.21 & 55.1 & & \\  & 25 & 4.19 & 54.8 & 3.34 & 52.5 \\ \multirow{4}{*}{Acetone} & 200 & 4.80 & 54.9 & & \\  & 339 & 6.01 & 55.1 & & \\ \hline \multicolumn{2}{c}{Average} & \multicolumn{4}{c}{54.8} & \multicolumn{2}{c}{52.5} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The intensities and binding energies of Peaks 1 and 2 observed in Fe 3\(p\) spectra at three temperatures for iron surfaces scratched in different environments. Figure 2: Temperature dependence of Fe 3\(p\) spectra of real iron surfaces scratched in different environments: air, (2) benzene, (3) cyclohexane, (4) water, (5) methanol, (6) ethanol, and (7) acetone. The O 1s spectra are shown in Figure 3. The curve resolutions of these spectra are described later.
###### Abstract  
Microalgae harvesting by coagulation can use coagulant agents such as alum, synthetic polymers or biocoagulants. Biocoagulants have attracted the attention of researchers because they are natural, biodegradable, and promote high microalgal harvesting efficiencies. This study aims to optimize the harvesting of _Chlorella vulgaris_ based on the dosage of the _Opuntia cochenillifera_ extract and the choice of eluent for biopolymer extraction. The outdoor cultivation of _C. vulgaris_ achieved a specific growth rate of 0.455 d\({}^{-1}\) and a maximum biomass concentration of 1.28 gbw L\({}^{-1}\). In order to harvest the microalgal biomass, the polymer present in the nucalage of _O. cochenillifera_ was extracted using NaOH and HCL. Coagulation and sedimentation assays were performed with different coagulant dosages: 3.5, 5.9, and 8.2 g L\({}^{-1}\). The maximum harvesting efficiencies using the acid and alkaline extract coagulant solutions were 80.8% and 99.5%, respectively, with a dosage of 3.5 g L\({}^{-1}\). According to the results, the _C. vulgaris_ biomass can be harvested with the nucalage from _O. cochenillifera_ in acid and alkalineuents. The application of this biocoagulant constitutes a sustainable solution for microalgal harvesting.  
biocoagulants; _Chlorella vulgaris_; microalgal harvesting; _Opuntia cochenillifera_; process optimization