esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
presented a scalable very-large-scale integration architecture of a robust algorithm for power line interference cancelation in multichannel biopotential recordings [23], and they also proposed an adaptive notch filter to estimate the contents of power line interference with a modified recursive least squares algorithm [41]. Although these filtering technologies showed great performance and robustness, the real time computation load is still high for low-power embedded systems, especially when the sampling frequency is high. Therefore, instead of removing the interferences from the contaminated signal using digital filtering, a robust solution to prevent power line interference from contaminating the biopotential signal during the recording stage would be preferred for long-term healthcare monitoring applications. During biopotential signal acquisition, device movements, wire jitter, or subject involuntary twitching are inevitable, thus affecting the quality of the recorded signals. The standard and commonly applied method for eliminating interferences resulting from electrode lead jitter is to apply high-pass filtering to the recorded signals [42]. This is the case because the frequencies of the signals are usually very low, and the high-pass filter will cut off the frequencies of interest containing useful information in the desired signal. With regards to thechallenges posed by extraneous interferences in biopotential signal recording, the existing hardware-based methods have limited effect while the traditional software filtering techniques inevitably render the target signal distorted after reducing the interferences. If such interferences can be prevented from mixing up with the desired signal before the raw signal gets into the acquisition device, a quality raw signal could be obtained, thus making the subsequent signal processing and analysis tasks accurate and easier. The current study proposes an improved hardware-based shielded technology aimed at effectively attenuating the interferences associated with biopotential signal recordings. The proposed technology attempts to resolve the adverse effects of the distributed resistance and capacitance of shielded cables by introducing a shielded signal to suppress resulting interferences as much as possible, and also to prevent the target signal from being distorted. Subsequently, three different methods for the proposed shielded technology were built and applied individually to ECG, EOG, and EMG recordings obtained in our laboratory. The experimental results showed that the proposed method can effectively suppress power line interference, and also shield the target signal. Furthermore, by recording the EMG signal in a shielded room, it was verified that the proposed hardware-based shielded drive circuit can also eliminate electrode lead jitter interference.
## 2 System Model and Channel Model  
In order to combat the impact of AT and PE on the FSO link performance, a block diagram of a mixed FSO-MIMO system is presented in Figure 1, in which the source node (S) communicates with the destination node (D) through a decode-and-forward (DF) relay node (R). In Figure 1, an ultrasonic and RIS-assisted for hybrid FSO-mmWave massive MIMO of V2X system on a highway is proposed. The vehicle speed has an effect on the performance of the system. The increase in speed leads to a larger error, because the increase in vehicle speed produces Doppler shift that affects the beamforming effect. Considering that the LoS communication between the vehicles and the BS is blocked by obstacles and the high-speed mobility of the vehicle, we introduce the one-bit RIS as a signal reflector to enhance signal transmission in NLOS situations and substantially simplify the physical hardware for DOA estimation. The use of RIS-assisted V2X communication with reflected signals can re-establish the communication between the vehicles and the BS. S-R link is equipped with a single antenna, and the R-D link is a mmWave massive MIMO V2X system model. The node R has both optical and RF signal processing capabilities. We employ non-coherent intensity modulation with a direct detection (IM/DD) receiver at R. After converting the incoming optical signal to an electrical signal, node R utilizes a power splitter to separate the alternating current (AC) and direct current (DC) components. The unsolicited DC component (which is normally filtered out at the receiver) is applied to the energy harvesting unit which supplies the harvested power to the RF transmitter. The information-bearing AC component is given to the decoder circuit, which decodes the information, and demodulated using an RF modulation scheme before it is forwarded through the RF transmitter. Figure 2 illustrates the workflow of DF.
# Future of IoT Networks: A Survey
## 4 Research Challenges of the IoT Network
### Network Security  
IoT devices may obtain visual and the sound information of the user without notice. This phenomenon is not limited to the private sector but also affects the public sector. Devices that monitor car traffic, surveillance, and mass transportation will be capable of communicate with one another. Thus, it is crucial to have a secure environment, in which personal information should not be leaked to other users, even if the user does not have malicious intent. Further, it is necessary to devise a scheme that can protect the devices from malicious attacks.  
In [66], IoT security can be summarized as follows: (i) resilience to attacks that may lead to system failure, (ii) data authentication to defend from forgery attacks such as changing sensor data  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**References** & **Integration** & **Coupling** & **Translation Gateway Required** & **Additional** \\ \hline
[54] & CDMA2000 \({}^{1}\)/WLAN & loosely coupled & O & \\
[55] & 3 GPP \({}^{2}\)/WLAN & loosely coupled & O & \\
[56] & 3G/WLAN & tightly coupled & O & \\
[57] & various protocols & X & X & \\
[60] & non-IP/IP & tightly coupled & O & embedding IPv6 layer \\ \hline \hline \end{tabular}  
* Code-Division Multiple Access 2000 (CDMA2000); 2 3 rd Generation Partnership Project (3 GPP).  
\end{table}
Table 4: The comparison of heterogeneity technologies.  
modification, (iii) access control algorithm for information provider to control the access level of data, and (iv) user privacy preservation to protect the issue in which the server easily infers the user's private information through the user's data. With this in mind, the devices themselves should be equipped with a mechanism to protect their operations, and the network itself should be protected. In terms of networking, a security challenge may exist with the tight resource constraints in IoT devices, Distributed Denial-Of-Service attack (DDoS) resistance, and protocol translation and end-to-end security among multiple heterogeneous devices [67]. It is necessary to devise a security scheme that can protect both the devices and the network to provide secure IoT services to the users.
# A Low-Power Read-Decoupled Radiation-Hardened 16T SRAM for Space Applications
## 4 Conclusions  
This paper presented a low-power, read-decoupled, radiation-hardened 16T (LDRH16T) SRAM cell for aerospace applications. The proposed design achieves full recovery from single-event upset (SEU) injected at all sensitive nodes, as validated through dual-current pulse simulations. Notably, the proposed cell is capable of recovering from SEUs with injected charges exceeding 150 fC at all sensitive nodes. In addition, the LDRH16T cell features fewer sensitive nodes than most reference designs, inherently reducing the probability of SEU occurrence. Its layout is optimized to enhance resilience against single-event multi-node upsets (SEMNU) by minimizing charge-sharing effects between adjacent nodes. Beyond its radiation tolerance, the proposed cell exhibits the highest read static noise margin (RSNM) of  
Figure 11: Soft-error recovery when an SEU affects (**a**) node Q and (**b**) node S0.  
Figure 12: Comparison of relative EQM.  
160 mV, representing improvements of 1.19\(\times\), 3.20\(\times\), 2.25\(\times\), 1.80\(\times\), 4.10\(\times\), 8.00\(\times\), and 3.08\(\times\) compared to RHBD10T, SIS10T, WE-QUATRO, QUCCE12T, EDP12T, SEA14T, and SAW16T, respectively. In addition, the hold power consumption (HPWR) of the proposed cell is 31.71 nW, which is 0.48\(\times\), 0.56\(\times\), 0.63\(\times\), 0.59\(\times\), 0.56\(\times\), 0.58\(\times\), and 0.90\(\times\) lower than that of the same reference cells, respectively. The write performance, measured in terms of word line write trip voltage (WWTV), is 301.87 mV, showing improvements of 1.68\(\times\), 1.59\(\times\), 1.01\(\times\), 1.04\(\times\), 1.02\(\times\), and 1.01\(\times\) over all comparison cells except SAW16T. Although the LDRH16T exhibits slightly longer read access time, it delivers the highest overall performance as measured by the electrical quality metric (EQM). Future work should consider incorporating design improvements aimed at reducing the read access time. These results indicate that LDRH16T is a highly promising solution for aerospace systems requiring long-term data reliability with minimal power consumption.  
Conceptualization, S.-J.L. and S.-H.J.; methodology, S.-J.L.; validation, S.-J.L.; data curation, S.-J.L.; writing--original draft preparation, S.-J.L.; writing--review and editing, S.-H.J.; visualization, S.-J.L.; supervision, S.-H.J.; project administration, S.-H.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The EDA Tool was supported by the IC Design Education Center, Republic of Korea.  
The authors declare no conflicts of interest.
# Underwater Object Detection Method Based on Improved Faster RCNN
###### Abstract  
In order to better utilize and protect marine organisms, reliable underwater object detection methods need to be developed. Due to various influencing factors from complex and changeable underwater environments, the underwater object detection is full of challenges. Therefore, this paper improves a two-stage algorithm of Faster RCNN (Regions with Convolutional Neural Network Feature) to detect holothurian, echinus, scallop, starfish and waterweeds. The improved algorithm has better performance in underwater object detection. Firstly, we improved the backbone network of the Faster RCNN, replacing the VGG16 (Visual Geometry Group Network 16) structure in the original feature extraction module with the Res2 Net101 network to enhance the expressive ability of the receptive field of each network layer. Secondly, the OHEM (Online Hard Example Mining) algorithm is introduced to solve the imbalance problem of positive and negative samples of the bounding box. Thirdly, GIOU (Generalized Intersection Over Union) and Soft-NMS (Soft Non-Maximum Suppression) are used to optimize the regression mechanism of the bounding box. Finally, the improved Faster RCNN model is trained using a multi-scale training strategy to enhance the robustness of the model. Through ablation experiments based on the improved Faster RCNN model, each improved part is disassembled and then the experiments are carried out one by one, which can be known from the experimental results that, based on the improved Faster RCNN model, [EMAIL_ADDRESS] reaches 71.7%, which is 3.3% higher than the original Faster RCNN model, and the average accuracy reaches 43%, and the \(F1\)-score reaches 55.3%, a 2.5% improvement over the original Faster RCNN model, which shows that the proposed method in this paper is effective in underwater object detection.  
underwater object detection; improved faster RCNN; Res2 Net101; OHEM; GIOU; Soft-NMS +
Footnote †: journal: Applied Sciences  
doi:10.3390/app13042746
## 3 Results and Discussion
The presence of surface functional groups such as carboxyl influences cation exchange capacity and the efficacy of biochar as an adsorbent of nutrients [98]. These functional groups facilitate ion exchange and nutrient adsorption over the surface of the biochar [23; 34; 99; 100]. The combined high surface area of biochar and its high CEC leads to higher ion exchange between nutrients in the soil solution [23; 101]. The findings provide further evidence to support the utility of biochar as a nutrient source [58; 77; 102; 103; 104; 105], as a soil enhancement to increase soil water holding capacity [58; 66; 77], supply the plant available silicon pool [106; 107], for water retention [58; 77] and as a habitat and refugia for soil microorganisms [66; 77; 105]. Biochar's value as a soil amendment has been widely reported; soil incorporation can increase and enhance crop yields in terms of quality as well as quantity [32; 33; 105; 108; 109; 110; 111; 12]. These findings have been documented in a variety of crops such as corn [108; 109; 111; 113; 114], soybeans [22], sorghum [105], cowpeans and peanuts [111]. In vegetables, benefits have been shown in lettuce (_Lactuca sativa_), cabbage (_Brassica chinensis_) [115], Chinese kale [23], tomato [116] and rice [32; 34; 87; 88; 105]. Growing crops in soil incorporated with biochar resulted in higher crop yields and improved quality [110; 114; 115; 49; 115]. Moreover, the stability of biochar allows it to remain in the soil for a long period. Biochar absorbs and retains plant nutrients from both organic and chemical fertilizers, reducing the leaching of nutrients and potentially reducing the total amount of fertilizers needed, thus reducing production cost [22; 87; 105; 114]. Ultimately, increased productivity and quality of produce will contribute to higher net farm income [43]. Biochar is also an effective soil amendment that can increase soil quality in terms of its physical and chemical composition. Biochar incorporation improves soil aeration and increases the porosity of clay and shale soils [34; 88; 105; 108; 117]. Biochar also enhances soil aggregation. Incorporation of biochar into sandy loam soils also improves pH [34; 88; 66; 88; 104; 115], water holding capacity [88; 110; 117], cation exchange capacity [89; 105; 110; 111; 115] and populations of beneficial soil microorganisms [112]. Moreover, biochar contributes to carbon sequestration [32; 39; 85; 118; 119; 120], both directly by mixing the biomass into the soil [29; 58; 104] and indirect sequestration by retaining carbon stocks within the biomass [23; 49; 55; 76]. Biochar can thus contribute to reducing carbon emissions from agriculture [32; 90; 108; 113; 117; 121].
# The Identification and Assessment of Rail Corrugation Based on Computer Vision
## 5 The Severity Assessment of the Corrugation
### Visual Saliency Feature Extraction and Saliency Map Generation  
Nowadays, there are several ways to extract significant visual features and further generate saliency maps, mainly including spatial-based and frequency-based methods. Among them, the frequency domain-based saliency detection method is increasingly investigated owing to its simple implementation and high real-time performance. In this paper, in terms of the characteristic of corrugation images, the significant visual features are extracted by the frequency-tuned (FT) model [42] and the full resolution saliency map is further obtained. In the first step, the input RGB image is converted into the CIELab color space closer to human vision. After that, the arithmetic mean of the image features is calculated and Gaussian smoothing is performed on the image. Finally, the difference of each pixel is calculated and the saliency map is obtained after the value is normalized. The saliency map \(S\) for a corrugation image \(I\) can be written as  
\[S(x,y)=N(\|L_{G}(x,y)-L_{m}\|) \tag{25}\]  
where \((x,y)\) denote the pixel coordinates. \(L_{m}\) stands for the average image feature vector in CIELab color space. \(L_{G}(x,y)\) is the relevant image pixel vector value in the intermediate image blurred by a \(5\times 5\) Gaussian kernel. \(\|\cdot\|\) is the \(L_{2}\) norm. \(N(\cdot)\) represents the min-max normalization operation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample Image** & **(a)** & **(b)** & **(c)** & **(d)** & **(e)** & **(f)** \\ \hline Frequency (num) & 5 & 4 & 4 & 3 & 5 & 6 \\ Period (pixel) & 102.4 & 128 & 128 & 170.7 & 102.4 & 85.3 \\ Manual result (pixel) & 102 & 128 & 128 & 172 & 102 & 85 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The period estimation results of corrugation displayed in Figure 11.
## 2 Experimental Research
### Component Materials and Method
#### 2.2.4 Polypropylene Fiber  
Synthetic monofilament polypropylene fibers 12 mm in length, from the manufacturer Sika Fibers, Switzerland, were utilized for micro-reinforcement. Properties of synthetic fibers as per the manufacturer is provided in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline
**Property** & \multicolumn{2}{c}{**CEM I 42.5R**} \\ \hline \multirow{2}{*}{Grinding fineness} & residue on site 0.09 mm [\%] & 0.2 \\ \cline{2-3}  & Fineness (Blaine) [cm\({}^{2}\)/g] & 3780 \\ \hline \multirow{2}{*}{Density} & loose state [kg/cm\({}^{3}\)] & 900 \\ \cline{2-3}  & compacted state [kg/cm\({}^{3}\)] & 1450 \\ \hline \multirow{2}{*}{Cement setting time} & Initial setting time [min] & 180 \\ \cline{2-3}  & Final setting time [min] & 235 \\ \hline Soundness & (Le-Chatlier) [mm] & 0.5 \\ \hline \multirow{2}{*}{Compressive strength} & 3 Days strength [MPa] & 33.7 \(\pm\) 0.8 \\ \cline{2-3}  & 28 Days strength [MPa] & 59.5 \(\pm\) 1.1 \\ \hline \multirow{2}{*}{Tensile strength} & 3 Days strength [MPa] & 6.1 \(\pm\) 0.1 \\ \cline{2-3}  & 28 Days strength [MPa] & 9.1 \(\pm\) 0.7 \\ \hline \end{tabular}
\end{table}
Table 1: Properties of the cement used according to the manufacturer.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**d (mm)** & **Bottom** & **0.125** & **0.25** & **0.5** & **1** & **2** & **4** & **8** \\ \hline Y (\%) & 0 & 1.8 & 20.4 & 66.7 & 83.4 & 95.9 & 98.2 & 100 \\ \hline \end{tabular}
\end{table}
Table 2: Particle size grading of the aggregate.
## 3 System Model
### Sum Rate Analysis  
The achievable sum rate of the \(i^{th}\) user for downlink SISO-NOMA can be computed as  
\[R_{U_{i}}=\log_{2}(1+SINR_{U_{i}}) \tag{11}\]  
From Equations (10) and (11), \(R_{U_{i}}\) can be written as  
\[R_{U_{i}}=\log_{2}\biggl{(}1+\frac{P_{T}\alpha_{i}|h_{i}|^{2}}{\sum_{j=i+1}^{ N}P_{T}\alpha_{j}|h_{i}|^{2}+\sigma^{2}}\biggr{)} \tag{12}\]  
Therefore, the overall achievable sum rate for the SISO-NOMA downlink can be expressed as,  
\[\begin{split} R_{overall}&=\sum_{i=1}^{N}R_{U_{i}} \\ &=\sum_{i=1}^{N}\log_{2}\biggl{(}1+\frac{P_{T}\alpha_{i}|h_{i}|^{ 2}}{\sum_{j=i+1}^{N}P_{T}\alpha_{j}|h_{i}|^{2}+\sigma^{2}}\biggr{)}\\ &=\sum_{i=1}^{N-1}\log_{2}\biggl{(}1+\frac{P_{T}\alpha_{i}|h_{i}|^ {2}}{\sum_{j=i+1}^{N}P_{T}\alpha_{j}|h_{i}|^{2}+\sigma^{2}}\biggr{)}\\ &\quad+\log_{2}\biggl{(}1+\frac{P_{T}\alpha_{N}|h_{N}|^{2}}{ \sigma^{2}}\biggr{)}\end{split} \tag{13}\]  
To evaluate the SISO-NOMA scheme at higher SNR, the variance \(\sigma^{2}\) tends to 0 (\(\sigma^{2}\to 0\)). Hence, from Equation (13), the achievable sum rate can be approximated as  
\[\begin{split} R_{overall}&\approx\sum_{i=1}^{N-1} \log_{2}\biggl{(}1+\frac{P_{T}\alpha_{i}|h_{i}|^{2}}{\sum_{j=i+1}^{N}P_{T} \alpha_{j}|h_{i}|^{2}+\sigma^{2}}\biggr{)}\\ &\quad+\log_{2}\biggl{(}1+\frac{P_{T}|h_{N}|^{2}}{\sigma^{2}} \biggr{)}\\ &\quad R_{overall}\approx\log_{2}\biggl{(}\frac{P_{T}|h_{N}|^{2}}{ \sigma^{2}}\biggr{)}\end{split} \tag{14}\]
# Novel Semantic-Based Probabilistic Context Aware Approach for Situations Enrichment and Adaptation
## 4 Proposed Approach
### Recommendation Process of Situation Rules
#### 4.2.3 Rule-Based Adaptation Process  
The proposed recommendation approach is applied on limited mobile devices of a user domain in order for the \"selected recommendation rules\" to be checked. In addition, we resolve the problem between resources characteristics (devices that run-rule items) and required characteristics (rule item checked and adapted). Examples of such attributes include CPU charge, sensor availability and memory size. Because of this, the capability context is necessary for maintaining the compatible services to improve the relevancy of the selected item rules and to adapt hybrid recommendation process.  
Towards this direction, a list of rule items of conceptually identical tags is identified, using the SWRL language [18], which is an inference engine of the determination of compatible rules. Simply run, the SWRL predicates compares the features service and the features device by comparing the feature value. If they are similar and its values, we inject rule item in user's agenda (see Algorithm 4). Specifically, we assume two available mobile devices of the current user domain were examined: one device having CPU speed equal to high (device 1) and another equal to low (device 2), resulting in 2.1 MHz and 0.5 MHz, respectively. A network of device 1 is {WiFi-3G}, while a device 1 supports: {WiFi-3G-5G}. To automatically check the available devices for running rule services correctly on the user domain, the SWRL rule (Algorithm 4) was employed. The devices requirements which have CPU speed, RAM size and network were checked with rule services features and then inject into the user's agenda when both service and rule items features are semantically matched.  
```
User_Profile (?u) \(\wedge\) User_Domain (?u,?d) \(\wedge\) hasCPUReq_device (?d,?CPUSpeed_device) \(\wedge\) hasRAMReq_device (?d,?RAMSize_device) \(\wedge\) hasNetworkReq_device (?d,?network_device) \(\wedge\) Situation_Rule (?r, \"Home_Intrusion_Alarm\") \(\wedge\) hasCPUReq_rule (?rule,?CPUSpeed_rule) \(\wedge\)hasRAMReq_rule (?d,?RAMSize_rule) \(\wedge\) hasNetworkReq_rule (?d,?network_rule) \(\wedge\) greaterThan(?UPSOed_device,?CPUSpeed_rule) \(\wedge\) greaterThan (?RAMSize_device,?RAMSize_rule) \(\wedge\) greaterThan (?network_device,?network_rule) \(\rightarrow\) agenda(?u,?r)
```  
**Algorithm 4**Check Device's Requirements with Situation Rule.
## 3 Results and Discussion
Further, the precursor fragments at M/Z = 215.14, M/Z = 189.12, M/Z = 281.11, and M/Z = 229.15 were identified as prolyl-valine (Pro-Val or PV), glycolyl-leucine (Gly-Leu or GL), aspartyl-phenylalanine (Asp-Phe or DF), and proline-leucine (Pro-Leu or PL), respectively, using the same analytical approach (Figure 1B-E). The five peptide standards were analyzed using HPLC-Q-TOF-MS/MS under the same conditions. The retention durations of the precursor fragments matched the previously observed ion fragment cleavage peaks. In a previous study, [PERSON] [19] utilized the UPLC-Q-FTP-MS technique to forecast the structure of the peptide. The ion fragments detected had a mass-to-charge ratio (M/Z) of 272.1654, with M/Z = 175.1185 corresponding to the y\({}_{1}\) ion, perhaps indicating the presence of Arginine with an additional hydrogen ion (H+). The M/Z value of 98.0343 is  
Figure 1: MS/MS spectrum of five peptides fractions. (**A–E**) represents the peptides Pro-Ala (PA), Pro-Val (PV), Gly-Leu (GL), Asp-Phe (DF), and Pro-Leu (PL) MS/MS spectrum respectively.  
believed to correspond to the b\({}_{1}\) ion, whereas the parent ion with an M/Z value of 272.1654 is identified as the peptide PR.