esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.2.5 Mineral Analysis  
Calibration curve preparation: A 5 mL aliquot of a mixture comprising 28 metals was transferred into a 100 mL volumetric flask. The flask was then filled with 1% nitric acid and shaken vigorously to obtain an intermediate solution with a concentration of 5 mg/L. Subsequently, this intermediate solution was serially diluted with 1% nitric acid to generate 10 calibration curve points. For the preparation of the internal standard solution, a solution containing 6 metal internal standards was diluted from 100 \(\mathrm{\SIUnitSymbolMicro g}\)/mL to 50 \(\mathrm{\SIUnitSymbolMicro g}\)/L using 1% nitric acid.  
Sample Extraction [17]: A 0.5 g sample was precisely weighed and placed into a microwave digestion vessel. Subsequently, 5 mL of nitric acid was added, and the vessel was tightly sealed. Digestion was carried out in accordance with the standard operating protocol of the microwave digestion instrument. The digestion reference conditions are presented in Table 1. Upon cooling, the vessel was carefully removed, and the lid was slowly opened to vent any residual gas. The vessel was then placed on an acid evaporator, and the acid was evaporated until the sample was nearly dry. The inner lid was rinsed with a small amount of water, followed by the addition of 1% nitric acid to adjust the volume to 10 mL. The solution was thoroughly mixed and reserved for further analysis. A blank test was conducted simultaneously.  
Testing: The instrument employs CCT (collision/reaction cell) analysis in the \(\mathrm{He}\)/\(\mathrm{O}_{2}\) gas mode. The principal working parameters and acquisition conditions of the apparatus are detailed as follows [18; 19]: The tuning mode was configured to STD/KED with a residence time of 0.1 s. The sample introduction speed was 40 rpm for a duration of 40 s. The RF (radio frequency) power was set at 1550 W, and the sampling depth was 5.0 mm. The nebulizer gas flow rate was 0.98 L/min, the cooling gas flow rate was 14.0 L/min, and the auxiliary gas flow rate was 0.8 L/min. The nebulizer chamber temperature was maintained at 2.7 \({}^{\circ}\)C. The horizontal and vertical alignments were 0.16 mm and \(-\)0.53 mm, respectively. The He flow rate was 4.55 mL/min, and the \(\mathrm{O}_{2}\) flow rate was 0.3125 mL/min. The voltage of lens \(\mathrm{D}_{1}\) was \(-\)350 V, and that of lens \(\mathrm{D}_{2}\) was \(-\)154 V. This entire procedure was replicated three times. The content of each mineral was computed utilizing the subsequent formula:  
\[\mathrm{Content}=(\mathrm{C}-\mathrm{C}_{0})\times\mathrm{V}\times\mathrm{D}/ \mathrm{Sampling}\mathrm{quantity}\]  
Content unit: \(\mathrm{\SIUnitSymbolMicro g}\)/g; C, concentration of ions in the test sample, unit: \(\mathrm{\SIUnitSymbolMicro g}\)/L; V, fixed volume, unit: mL; \(\mathrm{C}_{0}\), concentration of ions in the blank sample, unit: \(\mathrm{\SIUnitSymbolMicro g}\)/L; Sampling quantity unit: mg; D: dilution factor
## 3 Methodology
### Main Flow and Procedures of the Proposed Approach
Finally, both the ML and DL algorithms were applied to each of the 15 interference conditions (3 levels of attenuation \(\times\) 5 levels of bit-depth) for the different tasks of interference detection and interference identification (see Section 4 for a description of the interferences). The detection task is a binary classification problem (identify samples with the presence of interference from the ones with the absence of interference), while the identification of the type of interference is a multi-class (the three classes are Wide, Gaussian, and Narrow interference) problem.
# Drone Deployment Algorithms for Effective Communication Establishment in Disaster Affected Areas
## 1 Introduction
A significant concern with the above approach is the large number of drones required to cover the affected region. In addition, it is necessary to ensure that the deployed drones have sufficient leftover energy to serve the region long enough to conduct the rescue operation. Hence, we formulate a novel region-filling algorithm that brings the entire disaster affected area under drone network coverage without creating voids. Our algorithm considers the terrain data (altitude of various points in the region). We deploy the drones at optimal heights in order to maximize the system's average leftover energy. To the best of our knowledge, this approach towards communication establishment in a disaster affected area using stationary drones with maximum average leftover energy while minimizing the number of drones required is the first of its kind to be reported. The main research contributions of the paper can be summarized as follows:  
1. Development of a terrain-aware region-filling algorithm that covers the entire disaster affected area under a cellular network. 2. Minimization of the required number of drones while maximizing the average leftover energy of the system. 3. A proof that our method uses the minimum number of drones possible. Figure 1: An example of a drone-based communication system. Application and evaluation of the above methods using the parameters of a real location in _Pettumudi_, _India_. * A simulation of the region-filling algorithm for the terrain of Pettimudi. The rest of this paper is organized as follows. Section 2 describes the state-of-the-art of the literature on the topic. Section 3 details the various algorithms we formulated. Section 4 explains the experiments and simulations we conducted and their results. Finally, the paper ends with a conclusion section describing our future work.
# Monitoring Brain State and Behavioral Performance during Repetitive Visual Stimulation
## 3 Results
### Results of the EEG Data Analysis in the Source Space  
Setting the bandwidth of interest to \(10\pm 2\) Hz, we contrasted the source power (SP) between the four intervals. The statistical analysis revealed one cluster in the source space with \(p=0.02\) (corrected using the permutation statistics). This cluster included voxels in the right middle temporal gyrus (Temporal Mid R), right superior temporal gyrus (Temporal Sup R), right inferior temporal gyrus (Temporal Inf R), Rolandic operculum (Rolandic Oper R), fusiform gyrus (Fusiform R), and a part of the cerebellum (Cerebellum Crus1 R) (Figure 4A). The maximal \(F\)-value was achieved in the right middle temporal gyrus, while the minimum \(F\)-value belonged to the cerebellum. The averaged SP in this cluster grew from interval 1 (\(-0.22\pm 0.12\) SD) to interval 4 (1.21 \(\pm\) 0.49 SD) (Figure 4B).  
Results of the EEG data analysis in the source space: (**A**) Source plot shows \(F\)-value, reflecting the significant change of the source power (SP) between four intervals on the prestimulus interval \(t\in[-1.5,0]\) s, for \(f\in 8-12\) Hz (\(p=0.02\) via \(F\)-test, permutation-based correction). Legends contain \(p\)-values, CTF coordinates of the voxel with maximal \(F\)-value, and names of anatomical zones according to Automated Anatomical Labeling (AAL); (**B**) Normalized source power, NSP (group mean \(\pm\) 95% CI) in this cluster on four intervals.
## Theory
### Analysis of Displacement Contribution from DRSMs  
The previous analysis shows that the key problem of the sound radiation prediction method based on the DRSMs is to identify the DRSMs and obtain their modal participant coefficients. The identification of the DRSMs can be realized by trial calculation in the finite element simulation and the analysis of the contribution of the structural modes to the total radiated sound power. The participant coefficients of the DRSMs can be obtained by modal decomposition based on the vibration displacement distribution of the cylindrical shell. Therefore, a reasonable modal decomposition method needs to be chosen when analyzing the contribution of each mode to the total vibration displacement.  
For the previous cylindrical shell model, the modal displacement contributed by several modes at a certain point of the cylindrical shell are easily calculated and compared with the total displacement, as shown in Figure 5. Figure 6 shows the comparison of different modal participant coefficients in this case. Figures 5 and 6 show that although the mode (0,1) is the DRSM in the frequency band below 100 Hz, the contribution of this mode to the total displacement of the cylindrical shell is very small and much less than that of partial modes with a large contribution, which means that the extraction of modal participant coefficients will be difficult. In the frequency band near 250 Hz, mode (3,1) is the DRSM and the main component of the vibration displacement of the cylindrical shell. In the frequency band near 320 Hz, mode (2,1) is the DRSM. For the vibration, several other modes contribute significantly to the total displacement, but the displacement contribution of the mode (2,1) still has the same level of contribution as these few other modes. Moreover, a similar situation exists in the frequency band near 550 Hz. Therefore, the acquisition of the modal participant coefficients must fully consider the differences in the contribution of the DRSMs to the total vibration displacement in the corresponding frequency band.
# Intrusion Detection and Prevention in CoAP Wireless Sensor Networks Using Anomaly Detection
###### Abstract  
It is well recognized that security will play a major role in enabling most of the applications envisioned for the Internet of Things (IoT). We must also note that most of such applications will employ sensing and actuating devices integrated with the Internet communications infrastructure and, from the minute such devices start to support end-to-end communications with external (Internet) hosts, they will be exposed to all kinds of threats and attacks. With this in mind, we propose an IDS framework for the detection and prevention of attacks in the context of Internet-integrated CoAP communication environments and, in the context of this framework, we implement and experimentally evaluate the effectiveness of anomaly-based intrusion detection, with the goal of detecting Denial of Service (DoS) attacks and attacks against the 6 LoWPAN and CoAP communication protocols. From the results obtained in our experimental evaluation we observe that the proposed approach may viably protect devices against the considered attacks. We are able to achieve an accuracy of 93% considering the multi-class problem, thus when the pattern of specific intrusions is known. Considering the binary class problem, which allows us to recognize compromised devices, and though a lower accuracy of 92% is observed, a recall and an F_Measure of 98% were achieved. As far as our knowledge goes, ours is the first proposal targeting the usage of anomaly detection and prevention approaches to deal with application-layer and DoS attacks in 6 LoWPAN and CoAP communication environments.  
intrusion detection; anomaly detection; 6 LoWPAN; CoAP; internet-integrated sensor networks +
Footnote †: journal: Sensors
## 5 Conclusions and Prospects  
### Conclusions  
This paper conducted a comprehensive study on the development of city-industry integration under the background of new urbanization. To fully reflect the development of city-industry integration, social services, ecology and environment, and science and technology were integrated into the discussion of city-industry integration. We combined web crawler with literature research to build an index system based on multi-source data [49]. Because AHP cannot improve consistency after a given judgment matrix, the PSO algorithm was introduced to optimize its weights [57], and the EM model was added to obtain real and credible weights. GRA-TOPSIS was used to evaluate the level of city-industry integration from multiple attributes. Finally, to explore the degree and source of unbalanced development, the Gini coefficient and Theil index were used for calculations. Taking 18 prefecture-level cities in Sichuan Province from 2010 to 2019 as an example, the main conclusions are as follows:  
(1) By comparing the consistency index function values obtained by PSO-AHP and AHP, it can be concluded that the PSO-AHP model can achieve the optimization of the weight values when the judgment matrix has been determined [55]. (2) At present, although the overall level of city-industry integration development in Sichuan Province is still relatively low, it shows an upward trend. Only Chengdu's development level is much higher than the average [14], and the index scores of each city fluctuate up and down. (3) Dividing Sichuan province into different economic zones, from the scores of each subsystem and the relative changes of scores, there are certain regional differences in the development of city-industry integration. The overall imbalance of the development level of city-industry integration in Sichuan is small. The main reason for the imbalance is the significant differences within the Chengdu Plain Economic Zone. The methods and conclusions proposed in this study enrich the theory of city-industry integration and play a supporting role in the formulation of related policies.
## 5 Steady-State Results
### Nitrogen Oxides  
Nitrogen oxide reduction occurs if adequate concentrations of CO and H\({}_{2}\) are achieved inside the catalyst. The global reactions describing this process in the TWC are:  
\[CO+NO\to CO_{2}+\frac{1}{2}N_{2}\]\[H_{2}+NO\to H_{2}O+\frac{1}{2}N_{2}\]  
\[H_{2}+2 NO\to H_{2}O+N_{2}O\]  
\[CO+2 NO\to CO_{2}+N_{2}\]  
As displayed in Figures 6c, 7c and 8c, at all engine load values, nitrogen oxide conversion efficiency quickly dropped as soon as \(\lambda\) exceeded unity. As the mixture approached even slightly lean conditions, TWC conversion efficiency was approximately zero, and NO tailpipe concentration remained identical to engine-out values. The predicted values suitably reproduced the detected behavior, with some minor inaccuracies at AFR values in the range between 1.01 and 1.04. Taking into account the extreme sensitivity of the converter with respect to these species' concentrations and the related experimental uncertainties, possible improvements to the model could be achieved to collect a higher number of experimental data within the indicated \(\lambda\) range.  
Certainly, as mentioned, hydrogen participation in the reaction mechanism should be further investigated because the assumption of Figure 3 may not be respected in all the temperature ranges, especially in specific operating conditions, like dynamic ones. In spite of these considerations, the kinetic scheme, firstly calibrated at full engine load conditions, generally provided a good numerical/experimental agreement even at partial loads.  
Finally, around \(\lambda\approx 0.94\)-0.96, measured emissions showed a small NO\({}_{x}\) spike of a few ppm. The numerical model was not capable of capturing such phenomenon, which should be better clarified through additional investigations.
## 2 Acoustic Link Budget  
The passive sonar equation [13] is used to analyze the energy consumption over an acoustic link. If a sensor node transmits an acoustic signal with power level _SL_ (in dB re \(\upmu\)Pa), then the received signal to noise ratio (_SNR_) per bit at a receiving node can be expressed as  
\[\textit{SNR}=\textit{SL}-\textit{TL}-\textit{NL}-\textit{DI}\quad\textit{(dB)} \tag{1}\]  
where _TL_, _NL_, and _DI_ are the transmission loss over the acoustic link, the noise power level at a receiving node and the directivity index of transmitting antenna, respectively. It is assumed that sensor nodes are equipped with omni-directional antennas, and therefore \(DI\) is considered to be zero. Similarly, the transmission loss with unit normalizing constant \(A_{0}\), which includes fixed losses over distance \(d\) (meter), is taken as [14]  
\[TL=A_{0}+10\times k\times\log_{10}(d)+\alpha(f)\times d\times 10^{-3} \tag{2}\]  
where the spreading factor \(k\), which defines the geometry of propagation, is equal to 1.5 for practical scenario and \(\alpha(f)\) is the absorption coefficient in dB/km. For frequency \(f\) (kHz), the absorption coefficient \(\alpha(f)\) is expressed empirically using the Thorpe formula as [15],  
\[\alpha(f)=\frac{0.11f^{2}}{1+f^{2}}+\frac{44f^{2}}{4100+f^{2}}+2.75\times 10^{- 4}\times f^{2}+0.003 \tag{3}\]  
Underwater turbulence, shipping activity, wave, and thermal noise are the main sources of noise in an underwater channel. However, for practical applications, the noise power level \(NL\), in dB re \(\upmu\)Pa per Hz, can be approximated as [15]  
\[NL=50-18\times\log(f) \tag{4}\]  
Similarly, a node requires power \(P\) (watt) to transmit an acoustic signal with intensity \(I\) at a distance of 1 m in the direction of the receiver, which can be expressed as [16]  
\[P=2\pi\times H\times I=2\pi\times H\times 10^{S_{NL/10}}\times 0.67\times 10^{-18} \tag{5}\]  
where \(H\) is the depth in meters. By solving Equations (1) and (5), the expression for the received \(SNR\) can be written as  
\[SNR=10\times\log(P)-10\times\log(2\pi\times H\times 0.67\times 10^{-18})-TL-NL \tag{6}\]  
It is observed that acoustic channel is prone to the multipath effect. There are two well-known causes of multipath formation in an acoustic channel [17]:  
1. Sound reflection at any objects or bottom and surface of ocean
2. Sound refraction in water owing to spatial variability of sound speed  
Therefore, a transmitted acoustic signal is found to be severely faded by multipath in an underwater environment. This fading of acoustic signal is a random process that can be modeled as a Rayleigh fading. If a binary-phase-shift-keying (BPSK) modulated acoustic signal is transmitted, then the average bit error probability \(P_{b}\)is expressed as [18]  
\[P_{b}=\frac{1}{2}-\frac{1}{2}\sqrt{\frac{10^{{}^{\mathit{SNR}/10}}}{1+10^{{}^ {\mathit{SNR}/10}}}} \tag{7}\]  
Accordingly, packet error rate (PER) for packet length \(L\) (bits) can be computed as  
\[PER=1-(1-P_{b})^{L} \tag{8}\]
# Decision Tree-Based Adaptive Reconfigurable Cache Scheme
## 5 Results and Analysis
### Comparison  
To evaluate the performance improvement of the proposed DTARC, we compared the following three caches in the multicore system:  
* DTARC: The proposed adaptive reconfigurable cache is based on the decision tree algorithm.
* MRARC [6]: The adaptive control algorithm works by the following process. If the miss rate is beyond the system threshold, the associativity is tuned up one or two levels. We re-implement this approach in the multicore system.
* Basic cache: A fixed 16-way set-associative cache. According to Figure 12, this associativity appears most frequently across all applications.  
Figure 16 shows the miss rate results in different cache schemes under the optimal reconfiguration period. The L2 cache miss rate of the proposed adaptive reconfigurable cache in this paper is lower than that of the fixed 16-way set-associative cache in most instances except for in the case of the application of Barnes and Blackscholes. Compared with the fixed 16-way set-associative cache, the miss rate of the DTARC system is reduced by 60.97% on average across all applications, while the miss rate of MRARC [6] is reduced by 47.16% on average.  
We compare the AMAT of different caches under the optimal reconfiguration period in Table 2. The performance improvements compared to the basic cache are in parentheses. Although the adaptive reconfigurable cache based on decision tree proposed in this paper compromises the performance of the applications of Barnes and Blackscholes, it provides performance improvements for other applications. Compared with the fixed 16-way set-associative cache, the DTARC obtains an AMAT reduction of up to 12.59% (in the application of Streamcluster) and 4.69% on average across all applications. By contrast, the MRARC in [6] only improves the performance by 2.50% compared with the fixed 16-way set-associative cache. Our reconfigurable adaptive cache scheme based on the decision tree outperforms the MRARC [6] and fixed 16-way set-associative cache. It can also be seen that although the miss rate of the L2 cache is significantly reduced, the resulting AMAT improvement is not as significant. This is because the L2 _Hit Time_ of the system \"dilutes\" the performance improvement benefit from the miss rate reduction, as shown in Equation (3).  
From the above analysis, it can be seen that for most applications, the miss rate and average memory access time of the proposed DTARC outperform the fixed 16-way and MRARC cache. The adaptive reconfigurable cache based on the decision tree achieves the design purpose.  
The hardware cost of the proposed scheme under the TSMC 28 nm process is 2902 um\({}^{2}\), which is comparable to that of the MRARC in [6] with an area of 3047 um\({}^{2}\).
# Enhancing Chatter Stability for Milling Thin-Walled Blades by Designing Non-Uniform Allowance
## 1 Introduction
The findings demonstrate that the technique expertly suppresses the chattering of thin-walled workpieces and attains a high stability limit [35]. Ultimately, the procedure for adjusting the process stiffness of thin-walled components is achieved by applying the method with non-uniform allowances [36; 37; 38; 39]. Built upon the above studies, this thesis centers around how the traditional design of thin-walled parts can produce a certain inhibitory effect. While examining current research, it is found that there is limited studies on the inhibitory effects of complex thin-walled structural parts, and the current distribution method remains relatively inaccurate. From the literature reviewed, it is evident that several studies have focused on enhancing the machining stability of those regular thin-walled components. However, the machining stability of such complex aerospace blades deserves further in-depth study, especially through the non-uniform allowance design to improve the machining stability. Built upon the sensitivity process stiffness enhancement theory, a more accurate method optimized for non-uniform allowance process in the milling process of thin-walled parts is proposed herein. In the following analysis, Section 2 focuses on analyzing the effect of allowances distribution on the dynamic characteristics of thin-walled parts blade, and then presents the optimal design theory to address stiffness in light of the sensitivity theory. On the basis of the idea of finite element combined with the optimal design theory, it further seeks for a non-uniform allowance distribution design for the blade. Section 3 starts with an example of the typical blade to carry out design and experimental verification of the non-uniform allowance distribution. Results prove that the non-uniform allowance design based on sensitivity design proposed herein is effective to achieve stability during the milling process of blades of typical thin-walled parts used in aviation.