esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Distortion Calculation  
When the photodetector is illuminated with light from EOM, the resulting photocurrent is given as  
\[I_{detect}=e^{-\alpha L}\frac{P_{In}R_{d}}{2}\left(1-cos\left(T_{o}+\frac{\pi}{V _{\pi}}v_{rf}(t)\right)\right) \tag{7}\]  
Here \(\alpha\), \(P_{\mathrm{in}}\), \(R_{d}\), \(T_{\mathrm{o}}\) and \(v_{\mathrm{rf}}\)(t) are the waveguide attenuation constant, input optical power, photodetector responsivity, bias point and modulator driving voltage, respectively. Since the output is in terms of cosine, the nonlinearity which gives rise to higher order distortions, in this case, is sinusoidal. The distortion performance of an electro-optic modulator in an optical link can be measured and compared using a commonly employed two-tone test, which applies two equal amplitude signals at two closely spaced RF frequencies (\(\omega_{1}\) and \(\omega_{2}\)). For small signal RF input, the first order terms grow linearly while the third order term grows cubically with the input RF power. Output power in all the orders can be predicted by rewriting Equation (7) using Bessel function expansion while applying input RF signal \(v_{rf}(t)=v_{1}sin(\omega_{1}t)+v_{2}sin(\omega_{2}t)\).  
\[I_{detect}=e^{-\alpha L}\frac{P_{In}R_{d}}{2}\left(1-cos\left(T_{o}+\frac{\pi} {V_{\pi}}(v_{1}sin(\omega_{1}t)+v_{2}sin(\omega_{2}t))\right)\right) \tag{8a}\] \[=Re\bigg{\{}exp(jT_{o})\sum_{n}\sum_{m}J_{n}\left(\frac{\pi v_{1}}{V_{\pi}} \right)J_{m}\left(\frac{\pi v_{2}}{V_{\pi}}\right)exp(j(n\omega_{1}t+m\omega _{2}t))\bigg{\}} \tag{8b}\]  
where n,m = 0, \(\pm 1\), \(\pm 2\)...  
\[=\begin{cases}cos(T_{o})J_{0}\left(\frac{\pi v_{1}}{V_{\pi}}\right)J_{0}\left( \frac{\pi v_{2}}{V_{\pi}}\right)&\text{for n = 0, m = 0}\\ (\pm 1)^{n}2 cos(T_{o})J_{n}\left(\frac{\pi v_{1}}{V_{\pi}}\right)J_{m}\left( \frac{\pi v_{2}}{V_{\pi}}\right)cos(n\omega_{1}t+m\omega_{2}t)&\\ (\pm 1)^{n+1}2 cos(T_{o})J_{n}\left(\frac{\pi v_{1}}{V_{\pi}}\right)J_{m} \left(\frac{\pi v_{2}}{V_{\pi}}\right)sin(n\omega_{1}t+m\omega_{2}t)&\text{ for n = odd}\end{cases} \tag{8c}\]  
The maximum magnitude of the fundamental signal (\(P_{max(i)}\)) that a link can convey without any measurable distortion of a particular order (i) is when the power of modulation-produced distortion of that order is equal to the noise power. This is termed as SFDRi and can be expressed as  
\[SFDRi=P_{max(i)}-N\mid_{dB} \tag{9}\]  
Noise at the detector of an optical link is given as the addition of noise generated at the RF input of the modulator, additional noise at the detector (generally given by shot noise) and relative intensity noise (RIN) of the laser. Noise in dB is given as  
\[N =N_{in}+N_{add}+N_{RIN} \tag{10}\] \[=10 log_{10}\left(G_{i}KT+<i_{shot}>^{2}R_{LOAD}+l_{D}^{2}R_{d}10 ^{\frac{N_{RIN}}{10}}\right)\] \[=10 log_{10}\left(e^{-\alpha L}\frac{P_{in}\pi R_{s}}{2V_{\pi}}R_{ d}Kt+2 qI_{D}R_{LOAD}+l_{D}^{2}R_{d}10^{\frac{N_{RIN}}{10}}\right)\]  
Here Gi is the gain of the link, Rs is the input resistance, K is Boltzman's constant, T is the temperature (290 K), Ip\(=P_{in}R_{d}/2\), RLOAD is the load resistance and N\({}_{RIN}\) is the laser intensity noise mentioned by the manufacturer.
# Frequency-Domain Filtered-x LMS Algorithms for Active Noise Control: A Review and New Insights
## 3 Conventional Frequency-Domain ANC Algorithms
### FPBFxLMS Algorithm
\right]\mathbf{F}^{-1} \tag{13}\]  
is the windowing matrix that forces the last \(L\) time-domain elements to zero,  
\[\mathbf{E}(k)=\mathbf{F}[\mathbf{0}_{1\times L},\mathbf{e}^{T}(k)]^{T} \tag{14}\]  
is the frequency-domain error vector with \(\mathbf{e}(k)=[e(kL-L+1),...,e(kL)]^{T}\),  
\[\mathbf{V}_{p}(k)=diag\{\mathbf{F}\mathbf{v}_{p}(k)\} \tag{15}\]  
is the filtered reference signal matrix with \(\mathbf{v}_{p}(k)=\{v[(k-p-2)L+1],...,v[(k-p)L]\}^{T}\), \(\boldsymbol{\Lambda}(k)\) is the PSD matrix of the filtered reference signal which is used to improve the convergence, and the parameter \(m\) is related to the algorithm latency  
\[m=\left\{ \begin{array}{cc}1&D=L\\ 2&D=2L\end{array} \right.. \tag{16}\]  
The PSD matrix \(\boldsymbol{\Lambda}(k)\) can be computed recursively [10; 16]  
\[\boldsymbol{\Lambda}(k)=\lambda\boldsymbol{\Lambda}(k-1)+(1-\lambda)\mathbf{V }_{0}^{H}(k)\mathbf{V}_{0}(k) \tag{17}\]  
where \(\lambda\) is a smoothing factor, \(0<\lambda<1\). To reduce the complexity while retaining good convergence properties, the constraint in (12) can be added periodically [50; 51]. Another way to reduce the complexity of (12) is to use the unconstrained algorithm [13]  
\[\mathbf{W}_{p}(k+1)=\mathbf{W}_{p}(k)-\mu\boldsymbol{\Lambda}^{-1}(k)\mathbf{ V}_{p}^{H}(k-m)\mathbf{E}(k). \tag{18}\]  
The unconstrained PBFDAF exhibits a lower complexity than the constrained version, but the constrained version has a better convergence. Thus, we only consider the constrained FDAF. The frequency-domain FxLMS algorithms with \(D=L\) and \(D=2L\) are presented in Tables 1 and 2, respectively, where \(A\) denotes the number of multiplications of the \(2L\)-point FFT operation. The differences between the two algorithms are twofold. First, the FPBFxLMS II algorithm has a larger delay, which limits its application in broadband ANC. Second, the complexity of the FPBFxLMS IIalgorithm is distributed evenly in one frame, while steps (1)-(7) of the FPBFxLMS I algorithm should be completed in one-sample period. Therefore, the peak complexity of the FPBFxLMS I algorithm is rather high. This means that a strong processor should be used, and a large portion of power is wasted due to the unbalanced complexity [46; 48; 49]. From the perspective of real-time implementation, the FPBFxLMS I algorithm is not practical. A real-time multichannel ANC system using the FPBFxLMS II algorithm was implemented in the Graphics Processing Unit (GPU) in [39], and a comprehensive complexity evaluation was carried out.
## 5 Discussion
### Validation of the Results
#### 5.1.3 Validation of Spectral Response Results  
The spectral response analysis was performed on DFF and ACH, to investigate the performance under seismic load. This was done by obtaining the mass participation factor and calculating the base shear and moment.  
There are no standards regarding the permissible base shear and moment, and the obtained results were only informative data that can be used to ensure the stability of the base of the structure.  
The mass participation factor was compared to EN 1998-1. Based on this standard, the structure is recommended to have a total mass participation of at least 90%. Figure 26  
Figure 25: Deflection limit and the maximum vertical deflection for each frequency mode.  
shows the suggested limit against the mass participation factors obtained for DFF and ACH.  
Both structures failed to comply with this standard. The main reason for this is the stiffness of the elements. Both structures used concrete as the main material, which has a high stiffness. As a result, the mass participation of the structure was governed by the stiffness of the concrete members, resulting in a less dynamic response and a lower mass participation factor. Another reason is related to the standards. The spectral response analysis depends on the spectral curve of the earthquake specified by the standards, and varies from country to country. In both analyses, AS 1170.4 was used, resulting in an underestimation of the results due to limited earthquakes in Australia. This could have been adjusted by accessing UAE's and Germany's spectral curves, which this paper could not access.
# Collaborative Caching for Implementing a Location-Privacy Aware LBS on a MANET
## 1 Introduction
Although some research has explored MANETs with cellular network infrastructure [1; 9] or distributed query processing [10], they have not addressed the critical aspects of cache management and privacy preservation in a fully distributed environment. Although recent studies have investigated distributed approaches to privacy protection [2; 6], they continue to rely on stationary infrastructure at fixed locations, limiting their applicability in truly mobile scenarios. The main contributions of this study are outlined below:  
* We present a novel mathematical model and metric (APL) to quantify location privacy loss in both centralized LBSs and our proposed MANET-based system. Our theoretical framework provides a rigorous foundation for evaluating privacy benefits, incorporating factors such as data exposure patterns, node mobility, and cache dynamics. * We propose a location-aware LBS architecture on MANETs that enables users to resolve spatial queries through peer collaboration before accessing centralized servers. Our experimental evaluation demonstrates significant reductions in LBS queries while maintaining acceptable response times and system overhead. * We develop spatial query processing strategies based on two-tier caching: local caching maintained by each user and neighbor caching based on proximity. Our comprehensive comparison shows this approach substantially enhances user privacy by minimizing the exposure of location data to centralized entities. * We introduce two location-based data storage strategies that maximize the proximity of stored data to their corresponding geographic locations. These strategies effectively balance communication costs against redundancy, ensuring relevant data are cached only in users located within the referenced geographic area. * We provide the first comprehensive analysis of data obsolescence impact on cache performance and privacy loss in distributed LBS systems, including mechanisms for identifying and removing outdated information to maintain cache relevance and accuracy. Our approach is particularly valuable in scenarios where central infrastructure is unreachable or untrusted, such as during a disaster response, in crowded public gatherings, or in areas with limited network facilities. By giving users greater control over their information while increasing awareness of privacy risks, our method advances the development of privacy-respecting location-based services that maintain utility without compromising user privacy. The remainder of this paper is structured as follows. Section 2 presents related work and positions our contributions within the existing literature. Section 3 defines key terms and introduces the system architecture. Section 4 details our implementation approach including data obsolescence handling and privacy loss metrics. Section 6 presents experimental results and performance evaluation, and Section 7 concludes with key findings and future work directions.
## 5 Engineering Applications
### Numerical Simulation of Slopes
The sliding scale is small and the sliding position is affected by Strong terrain control. In addition, the soil moisture content in the natural state is greater than its cohesion peak moisture content (10%), which means that the shear strength of the rock and soil mass will only decrease under the condition of rainfall. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Sand and Soil Surface**} & \multicolumn{3}{c}{**Strongly Weathered Andesite**} \\ \hline Water content (\%) & 6 & 10 & 20 & 25 & 6 & 10 & 20 & 25 \\ \(c\) (kPa) & 8 & 85 & 17 & 9 & 215 & 195 & 151 & 130 \\ \(\varphi\) (\({}^{\circ}\)) & 30 & 25 & 25 & 17.9 & 15.6 & 14.6 & 12.7 & 11.9 \\ \(\gamma\) (kN/m\({}^{3}\)) & 13.2 & 13.75 & 15 & 15.63 & 15.75 & 16.35 & 17.82 & 18.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Shear strength of rock mass varying with water content. Figure 8: (a) Locations of the Pile AA001–AA004 and the monitoring slope. (b) Stratigraphic model of the monitoring regional landslide. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Water Content (\%)** & **Radius of Dangerous** & **Sliding Force** & **Anti-Sliding** & **FOS** \\
**(\%)** & **Sliding Surface (m)** & **(kN)** & **Force (kN)** & **FOS** \\ \hline
6 & 102.9 & 1236 & 1802 & 1.458 \\
10 & 184.53 & 76,345.938 & 145,378 & 1.904 \\
25 & 102.91 & 1458.175 & 1677.549 & 1.150 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Prediction results of FOS.
# Influence of Polymer Types on the Mechanical Properties of Polymer-Modified Cement Mortars
###### Abstract  
This is an experimental study showing the effect of four types of polymers (acrylate polymer (AC), polyvinyl alcohol (PVA), styrene-butadiene rubber (SBR), and ethylene-vinyl acetate (EVA)) on the mechanical properties of polymer-modified cement mortars (PCMs). One polymer dosage was used in this study (3%), and the effect of this dosage on PCMs was compared with a control mortar mix with 0% polymer. The compressive, flexural, and pull-off bond strengths were measured and compared with previous results in works of literature. The effect of polymer addition on improving the mechanical properties of PCMs was clarified, and this effect was more obvious on the flexural strength than that on the compressive strength. The PCMs containing EVA showed the best performance, with up to 33% and 63% increases in compressive and flexural strengths after 28 days, respectively. In comparison, AC, PVA, and SBR produced smaller (16%-46% compared to control) improvements in the flexural strength after 28 days. In general, PCMs containing EVA showed the best mechanical properties.  
cement mortar; polymers; compressive strength; flexural strength; pull-off bond strength; failure modes
# Enhancements and Challenges in CoAP--A Survey
## 3 Applications of CoAP
### Cloud Computing Services  
CoAP Integration with Cloud  
The IoT data transfer implementation between raspberry pi and the cloud is achieved by [PERSON] et al. in [18], based on CoAP. The proposed system consists of (i) sensors, (ii) raspberry pi, (iii) CoAP-based gateway and (iv) cloud platform. The CoAP is implemented in transferring the data from sensors, which act as IoT nodes, to cloud platform. Humidity and temperature sensors are used in the system and their data formatted in JavaScript Object Notation (JSON) is periodically transmitted to ThingsBoard cloud endpoint using CoAP POST messages.
## 3 Datasets and Evaluation Metrics
### LPIPS  
The Learned Perceptual Image Patch Similarity, a full-reference metric, measures perceptual similarity by calculating the distance between feature maps extracted by multiple layers of pretrained deep neural networks (like VGG or AlexNet) applied to both the reference and distorted images. The process involves extracting these features via the network and aggregating the distances between them, not being reducible to a simple mathematical formula [101]. Lower LPIPS values indicate greater visual similarity (better quality).
###### Abstract  
Previously conducted studies have established that all-light ceramsite concrete has the characteristics of light weight and high strength. It is a good material for strengthening and has a good application prospect when strengthening existing RC columns by the enlarged cross-section method. Additionally, a good combination of new-to-old concrete is crucial to the best performance of RC columns strengthened by the enlarged section method. Therefore, to investigate more comprehensively the axial compression reinforcement effect of RC short columns strengthened with all-light ceramsite concrete, one unreinforced short column, one RC short column strengthened by ordinary concrete, and four RC short columns strengthened by all-light ceramsite concrete with different interface treatments were produced. Moreover, axial pressure tests were conducted on them. The findings showed the following: (i) Short-column specimens strengthened by all-light ceramsite concrete had obvious ductile damage characteristics. (ii) The type of bonding surface greatly affected the strengthening effect. The order of ultimate load-bearing capacity improvement was post-installed rebar + epoxy resin interface agent, epoxy resin interface agent, post-installed rebar, and chiseling. (iii) When post-installed rebar + interface agent was used, the epoxy resin interface agent played the primary role during the pre- and mid-loading period. The post-installed rebar started to function only in the mid- and late-loading period. (iv) Strain growth of the existing longitudinal bar and strengthening longitudinal bar was not synchronized in the late-loading period. The strain in the existing longitudinal bar grew rapidly and nonlinearly, while the strengthening longitudinal bar still grew linearly. (v) The calculation formula in specification GB50367-2013 is also applicable to ceramicstic concrete, and the correction factor can still be 0.8. When the interface treatment method is post-installed rebar + epoxy resin interface agent, the correction factor can be 0.9.  
reinforced concrete column; enlarging section method; structure reinforcement; all-light ceramsite concrete; new-to-old concrete bonding interface
# PIFNet: 3D Object Detection Using Joint Image and Point Cloud Features for Autonomous Driving
## 5 Conclusions  
In this research, we have proposed a two stream Point-Image Fusion network (PIFNet) for 3D object detection to solve the problem of inconsistency between the localization and classification. We focus on fusing the depth information from point clouds generated by a Lidar and the semantic information from images generated by a camera. In order to generate the multiple scale feature maps, the Encoder-Decoder Fusion module has been designed to effectively extract image features, such as color and texture features. In addition, we align different modalities using a fusion module to generate accurate 3D proposals.  
Extensive experiments demonstrated that our PIFNet can achieve good performance. Particularly, PIFNet has achieved an average mAP of 85.16% on the KITTI dataset, which is an 1.97% improvement over the previous best performance. In future works, we plan to explore more accurate methods for real-time 3D object detection solutions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**\#Fusion Modules** & **3D Detection mAP** & **Training Time (Hour)** \\ \hline
3 & 83.43 & 51 \\ \hline
4 & 85.16 & 73 \\ \hline
5 & 85.21 & 116 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Optimization of the number of fusion modules.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Modality**} & \multicolumn{3}{c}{**3D Detection**} & \multicolumn{3}{c}{**BEV Detection**} \\ \cline{3-8}  & & **Easy** & **Mod.** & **Hard** & **Easy** & **Mod.** & **Hard** \\ \hline PIFNet + Fusion & L + R & 89.22 & 81.79 & 79.15 & 91.73 & 87.41 & 87.00 \\ \hline PIFNet + EDF & L + R & 91.45 & 82.28 & 79.46 & 93.01 & 87.96 & 87.13 \\ \hline PIFNet + Fusion + EDF & L + R & 92.53 & 83.03 & 79.92 & 95.89 & 89.08 & 87.91 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Effectiveness of EDF module and fusion module.  
**Author Contributions:** [PERSON] developed the idea and implemented the experiments. [PERSON] prepared the KITTI dataset and participated in ablation experiments. [PERSON] participated in ablation experiments. [PERSON] and [PERSON] supervised the research and performed revisions and improvements. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea) under 343 Industrial Technology Innovation Program (10080619).  
The data in this study can be requested from the corresponding author.  
This research was funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea) under 343 Industrial Technology Innovation Program (10080619).  
The authors declare no conflict of interest.
## 2 Measurement and Roughening Methods
### Roughening Methods
#### 2.2.1 Grinding Method
[42] observed that the surface roughness of coal particles increased from 23 nm to 57 nm as the milling duration grew from 2 min to 16 min. They also noted that the roughness of the quartz particles varied from 3.12 \(\upmu\)m to 4.02 \(\upmu\)m when ground for 60 min and 240 min, respectively [80]. In contrast, [PERSON] et al. [81] found that the surface roughness of low-rank coal particles decreased from about 1.15 to 1.08 (dimensionless), measured using the BET method, as the grinding time was extended from 2 min to 4 min. Numerous studies indicate that adjusting the mill equipment, grinding parameters, grinding environment, and grinding time can effectively increase surface roughness. However, some researchers have reached contradictory conclusions, which is not a contradiction due to the distinct structural characteristics of different minerals and the various grinding mechanisms of different mills. For example, ball mills and rod mills primarily use impact crushing and abrasion, while autogenous mills rely on abrasion [32]. In contrast, stainless steel disk mills utilize shear and compression [3; 37]. In disk and rod mills, particles are primarily crushed through surface contact, whereas in ball mills, the communication occurs mainly through point contact, potentially producing rougher particles than those created by  
Figure 2: AFM images of hematite and magnetite particles: (**a**,**e**) rod-milled hematite particles; (**b**,**f**) ball-milled hematite particles; (**c**,**g**) ball-milled magnesite particles; (**d**,**h**) stainless steel disk-milled magnesite particles; (**a**–**d**) two-dimensional images; and (**e**–**h**) three-dimensional images [3; 39; 37]. surface contact [3; 37]. Different grinding methods lead to various changes in the structure, enthalpy, and surface energies of solid particles during the comminution process, affecting the surface roughness [76]. The extent of these changes is strongly influenced by the material or mineral itself, the milling equipment used, the grinding parameters, and the grinding environment [44; 82]. Each material, or even the same material, behaves differently during various milling processes, resulting in distinct grinding characteristics [33] and potentially conflicting conclusions regarding surface roughness. To better understand the effects of grinding methods, grinding media, and grinding parameters on surface roughness, it is crucial to conduct systematic studies. Moreover, since grinding methods can alter particle shapes and sizes while producing a different surface roughness, the particle shapes and sizes should be as uniform as possible. This can be achieved by categorizing particles into narrow grain sizes, which will help in studying the influence of surface roughness on flotation behavior, wettability, and bubble-particle interactions.