esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Synchronous Information Transmission Analysis  
### Information Modulation  
As the phase shift angle changes, the inverter output voltage is shown in Figure 4, and the working and resonant frequency of the system is 47.3 kHz. This is consistent with the phenomenon of Figure 2; changing the phase shift angle to change the inverter voltage verified the feasibility of MASK modulation based on PSFB.  
When \(Z_{\text{S}}\), \(Z_{\text{P}}\), \(\omega\), \(R_{\text{L}}\) remain unchanged and the coupling coefficient \(k=0.2\), the change trend of the phase shift angle with output voltage and output power is shown in Figure 5. Experimental and simulation data reference Table 1.  
Figure 4: Inverter output voltage.  
Considering the voltage fluctuation problem, the voltage variation range is selected as 5%, and the corresponding \(\alpha\) is 0-36.5\({}^{\circ}\). Using DSP to generate PWM waves with different phases, 8 ASK, 16 ASK or higher MSK information modulation is performed to increase the signal bit width, thereby increasing the signal transmission rate. In the specified \(\alpha\) range, according to Figure 5, the appropriate signal bit width, phase angle and corresponding voltage change value are selected. This can ensure the rate while reducing the bit error rate as much as possible. Table 2 shows the modulation parameters of 8-ary and 16-ary signal.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Symbol** & **Value** \\ \hline Power supply & \(U_{\text{d}}\) & 24 V \\ Coil resistance & \(R_{\text{P}},R_{\text{S}}\) & 0.1 \(\Omega\) \\ Transmitting coil self-inductance & \(L_{\text{P}}\) & 282.04 uH \\ Resonant capacitor & \(C_{\text{P}},C_{S}\) & 40 nF \\ Load & \(R_{\text{L}}\) & 35 \(\Omega\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental parameters.  
Figure 5: Voltage, power and phase diagram.
# Action of Antimicrobial Peptides against Bacterial Biofilms
## 3 Biofilm Resistance to AMPs
### Synergy of Anti-Biofilm AMPs with Antibiotics  
The anti-biofilm activity of AMPs can be enhanced against biofilms by combining them with antibiotics [125; 126; 127; 128]. Combination strategies are useful since they can target a variety of microbial communities present with different metabolisms cells in low pH, hypoxic or low nutritious environments [129]. AMP-1018 can prevent initial bacterial attachment to surfaces by inhibiting the synthesis of (p)ppGpp [23]. When 1018 was used in combination with ceftazidime, ciprofloxacin, imipenem, or tobramycin, at sub-MIC this combination could inhibit 50% biofilms produced by _P. aeruginosa, E. coli, A._ baumannii, _K. pneumoniae, S._ enterica, and methicillin-resistant _S. aureus (MRSA)_[23]. Similarly, colistin in combination with temperin A (TEMP-A), citropin 1.1 (CIT-1.1) and tachyplesin I (TP-I-L) can eradicate mature biofilms of drug resistant _P. aeruginosa_ and _S. aureus_[130]. AMPs can act synergistically with antibiotics against biofilm following two types of mechanism. Firstly, AMPs-antibiotic combinations can degrade biofilms matrix then AMPs act alone and disperse biofilms embedded cells [131]. AMP-antibiotic combinations can also be used against fungal biofilms [132]. An antifungal plant defensin derived peptide HsLin06_18 acts synergistically with caspfungin against _Candida glabrata_ and _Candida albicans_. HsLin06_18 was shown to act by permeabilization cell membrane which facilitated caspfungin penetration into the fungal cells, inducing death at a sub-inhibitory concentration [132].
# Mercury Uptake and Transport by Plants in Aquatic Environments: A Meta-Analysis
## 3 Results and Discussion  
### BCF of Hg in Plants  
Fifty-three plant species belonging to 19 families (\(n=117\)) that were monitored more than twice in a given study were selected from the database, and their ability to accumulate Hg from water was analyzed. The effect sizes (In BCF) of the same family plants from the same aquatic habitat were combined to calculate the mean and 95% CI values. The details were inferred from the simplified forest plot shown in Figure 2. The total average effect size of all species was significantly greater than zero (Laboratory: mean = 8.155, CI: 7.042-9.389; Lake: mean = 3.928, CI: 2.935-4.945; River: mean = 7.774, CI: 6.735-8.739; Sea: mean = 8.833, CI: 8.163-9.448). Furthermore, among the 19 families, Aracaceae in the laboratory showed the highest average effect size (mean = 11.847, CI: 9.277-14.264), whereas Onagraceae in the lake showed the lowest value (mean = -0.219, CI: -0.329---0.113). Additionally, the average effect size of the other 16 families (Cymodocaceae, Cyperaceae, Graciliaraceae, Haloragaceae, Hydrodravitaceae, Iridaceae, Juncaceae, Plantaginaceae, Poaceae, Polygonaceae, Pontederiaceae, Posidoniaceae, Potamogetonaceae, Salviniaceae, Typhaceae, and Ulvaceae) was significantly greater than zero. Therefore, 17 of the examined plant families were found to significantly absorb Hg from water.  
As they grow, aquatic plants absorb nutrients from their surrounding environment. Furthermore, based on their importance with respect to plant growth, heavy metals in the environment are classified as essential and non-essential elements. The essential elements (e.g., Fe, Zn, and Cu) can enter plants through transporters on the cell membrane surface, whereas the non-essential elements (e.g., Hg, Cd, and Pb) can enter plants by binding to carbonyl uronic acid [58]. Hg can inhibit seed germination, reduce root and stem biomass, and retard plant growth [58; 59]. Thus, to cope with the stress induced by heavy metals in the environment, plants utilize a variety of protection mechanisms, such as root retention, cell wall precipitation, and enzyme system protection [59]; these not only protect plants from Hg toxicity but also enable them to accumulate large amounts of Hg. This ultimately leads to the hyperaccumulation of Hg in aquatic environments. Furthermore, the differences in the heavy metal absorption capacities of different plants may be due to the differences in the number and properties of metal-binding sites on the surfaces of plant cells [60].
# Soluble Elements Released from Organic Wastes to Increase Available Nutrients for Soil and Crops
## 3 Results and Discussion
### Correlation between Physical and Chemical Properties and Nutrient Content  
As discussed above, nutrient solubility can be driven by elemental composition and its structural function. Although [PERSON] et al. [43] indicate other factors such as pH, EC and OM may play a role too. Therefore, this paper provides significant correlation coefficients between elemental composition, water extractable nutrients content and physical and chemical properties of each residue (Appendix A. Tables A1-A7). AP and VP (Tables A1 and A7) show a mostly high correlation between the elemental composition of a nutrient and the soluble concentration of that nutrient. Therefore, the presence of soluble nutrients from AP and VP residues is associated with its elemental composition. In Table A1, nutrients' elemental composition versus its soluble forms, on most occasions, shows a significant and negative (\(-\)) linear correlation (\(<\)0.7). Additionally, in Table A1 for AP, a positive (\(+\)) linear correlation is observed between the elemental composition of a nutrient and the elemental composition of another nutrient, which is significantly high (\(>\)0.7). The same correlation applies to water extractable content between nutrients (Table A1). Usually, in Table A7 for VP, correlations are similar to AP (Table A1), but the correlation between the soluble form of one nutrient and that of another nutrient is negative. The nutrients that show a significant correlation between their content of elemental composition and their own aqueous extractable content are K, Ca and Fe (\(-\)) in AP; Na (\(+\)) and Zn (\(-\)) in CP; Mg (\(-\)), Fe (\(-\)) and Mn (\(+\)) in OP; Fe and Cu (\(+\)) in PN; Mg (\(+\)), Fe (\(-\)) and Zn (\(+\)) in PP; K and Zn (\(+\)) in SC; and Ca (\(+\)) in VP. When addressing the importance of physical and chemical properties of the wastes related to elemental composition nutrient content and aqueous extract nutrient content, differences were observed on each of the residues. OM is the most important property for elemental composition because it has more significant correlations in AP (mainly negative), OP (positive and negative) and SC (positive and negative), and for water extraction nutrients in PN (positive and negative). EC is the most correlated property for elemental composition and water-extractable nutrients in PP, SC and VP. Bulk density (\(\rho_{\text{b}}\)) seems to be related to water-extractable nutrients in CP, OP and SC, as well as pH is highly correlated to
## 2 Road Traffic Situation Assessment Model
### Urban Road Monitoring Network
#### 2.1.4 Collaborative Monitoring Model of the Road Traffic Situation
If the node \(v_{j}\) is the master node or a non-road cooperative node, the monitoring data \(M_{j}\) uploaded by the node \(v_{j}\) default to a matrix of all \(0\). The monitoring data \(M_{j}\) are expressed as Equation (3). \[M_{j}=\left\{ \begin{array}{ll}\left[m_{j},m_{j}^{1},\ldots,m_{j}^{M}\right],&v_{j}\text{ is the road cooperative node}\\ \left[\left[0,0,\ldots,0\right],&v_{j}\text{ is the road non-cooperative node or master node }v_{0}\end{array} \right.\end{array}\right. \tag{3}\]  
The main node fuses the monitoring information and data according to the cooperative node monitoring dataset D, builds the collaborative road traffic situation monitoring model, analyzes and predicts the road traffic state and traffic trend, respectively, and completes the road traffic situation assessment.
## 3 Results
### Characterization of the Biomass
#### 3.1.2 Ultimate Analysis  
The ultimate analysis of the pea pod waste is presented in Table 5. With these results, the O/C, H/C, and C:N ratios are obtained. The O/C, H/C, and C:N ratios are important parameters in the characterization of organic compounds, including biomass and coal. The H/C ratio is a measure of the degree of saturation of a compound, with higher values indicating greater saturation. The O/C ratio reflects the oxygen content relative to carbon, and the N/C ratio indicates the nitrogen content relative to carbon. These ratios are commonly used in the van Krevelen diagram (Figure 2), which is a graphical representation of the O/C versus H/C ratios of organic compounds [32]. This diagram is useful for classifying and comparing the composition of organic matter, including biomass and coal, and for understanding their origin and evolution. For example, in the case of coal, the H/C ratio shows a sharp decline in bituminous coals and anthracites, reflecting increased aromatization of the lignin structure during coalification. In the context of biomass, the O/C ratio is of great importance, as a high O/C ratio results in a low bulk energy density, highlighting the need to lower the oxygen content for efficient energy conversion [33]. Therefore, these ratios are crucial for understanding the properties and behavior of organic compounds and for optimizing their utilization in various applications.  
Figure 2 shows the characterization of the pea pod waste as biomass, and its C/N ratio shows that it is appropriate to be used for processes of transformation using microorganisms due to the ratio being higher than 24, the ideal microbial diet, and making it have a slower relative decomposition rate. When comparing the results to those found in previous papers, the biggest difference is the lower C/N ratio in previous research and the higher O/C ratio, both due to the nature of the pea pod.
# The YouGovern Secure Blockchain-Based Self-Sovereign Identity (SSI) Management and Access Control
## 5 Experimental Evaluation
### Quantitative Cost Analysis  
To assess the operational feasibility of the YouGovern system, we conducted a quantitative cost analysis focusing on gas consumption, decentralized storage overhead, and backend hosting requirements. Table 5 summarizes the estimated costs for the most com  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Features** & **YouGovern** & **Microsoft ION** & **Sovrin** & **uPort** & **DNS-IdM** \\ \hline Blockchain & BSC & Bitcoin (via & & & \\ platform & BSC & Sideltree protocol) & Hyperledger Indy & Ethereum & Ethereum + DNS \\ \hline Storage layer & IPFS + Filecoin & IPFS & Indy Node Storage & IPFS & On0 chain + DNS \\ \hline DID Standard & Yes & Yes & Yes & Yes & Partial \\ (W3C) & Smart contract + & Key rotation via & Ledger-based & Limited & DNS record \\ Key revocation & DID logic & update & deactivation & Limited & \\ \hline Interoperability & Designed via & & & & \\ DDComm & Partial & Partial & Limited & Low \\ \hline Transaction costs & Low (BSC) & Zero-fee (off-chain & Variable (Indy & & \\  & & ops) & pool fees) & Medium & Variable \\ \hline Decentralization & Medium (PoSA & High (uses Bitcoin & & & \\ level & validators) & base layer) & & & \\  & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of SSI Frameworks.  
mon identity-related operations on the BSC and IPFS (as of April 2025, average gas price \(\sim\)5 Gwei [51]).  
Also, other infrastructure costs may include:  
* IPFS and Storage Overhead:  
* Average document size: 1 MB (e.g., encrypted identity document).
* Estimated IPFS/Filecoin storage cost via Web3.Storage: \(\sim\)50.02/month per document.
* Hybrid backup (institutional fallback node): negligible added cost when co-hosted.
* Hosting Infrastructure:  
* The front-end (React-based) and API gateway (Node.js/Express) are designed for lightweight deployment using serverless or containerized environments (e.g., Heroku, Vercel, AWS Lambda).
* Monthly operational cost for a medium-scale deployment (\(\sim\)10,000 active users) is projected under $50/month when using a decentralized-first strategy with minimal centralized backup reliance.  
These quantitative estimates demonstrate that the cost overhead of deploying and maintaining YouGovern is low enough to support wide-scale adoption, even for low-budget organizations such as municipalities, academic institutions, or NGOs.
# Effects of Torso Pitch Motion on Energy Efficiency of Biped Robot Walking
## 3 Trajectory Planning
### Torso Angle
#### 3.3.2 Tpm  
The absolute angle of the torso is the angle between the torso and the vertical direction, which is positive in the counterclockwise direction. When the biped robot walks periodically, its torso angle also makes a periodic motion. We design this torso angle trajectory as follows: its angle is \(\theta_{0}\) at the switching time of SSP and DSP, reaches an extreme value \(\theta_{1}\) at the middle time of DSP, and reaches an extreme value \(\theta_{2}\) at the middle time of SSP. In other words, torso angle is \(\theta_{0}\) at the beginning of DSP, reaches the extreme value \(\theta_{1}\) at the middle of DSP and then returns to the angle \(\theta_{0}\) at the end of DSP; the torso angle is \(\theta_{0}\) at the beginning of SSP, reaches the extreme value \(\theta_{2}\) at the middle of SSP and then returns to \(\theta_{0}\) at the end of SSP. According to the continuity and smoothness of torso motion, we can know that the value of \(\theta_{0}\) is between \(\theta_{1}\) and \(\theta_{2}\). However, we do not know which one is bigger in \(\theta_{1}\) and \(\theta_{2}\). \(\theta_{0}\), \(\theta_{1}\), and \(\theta_{2}\) are gait parameters that will be determined by optimization. The absolute angles of the torso are designed as two fifth-order polynomials in DSP and SSP, respectively.  
\[\theta^{DSP}(t)=f_{0}+f_{1}t+f_{2}t^{2}+f_{3}t^{3}+f_{4}t^{4}+f_{5}t^{5},\;0 \leq t\leq T_{\text{d}} \tag{16}\]  
\[\theta^{SSP}(t)=g_{0}+g_{1}t+g_{2}t^{2}+g_{3}t^{3}+g_{4}t^{4}+g_{5}t^{5},\;0 \leq t\leq T_{\text{s}} \tag{17}\]  
At first, torso motion should be continuous and smooth in a gait step. Therefore, the following continuity conditions should be satisfied:  
\[\left\{\begin{array}{l}\theta^{DSP}(T_{\text{d}})=\theta_{0}\\ \theta^{SSP}(0)=\theta_{0}\\ \hat{\theta}^{DSP}(T_{\text{d}})=\hat{\theta}^{SSP}(0)\\ \hat{\theta}^{DSP}(T_{\text{d}})=\hat{\theta}^{SSP}(0)\\ \end{array}\right. \tag{18}\]  
Secondly, the following cyclic conditions should be satisfied:  
\[\left\{\begin{array}{l}\theta^{DSP}(0)=\theta_{0}\\ \theta^{SSP}(T_{\text{s}})=\theta_{0}\\ \hat{\theta}^{DSP}(0)=\hat{\theta}^{SSP}(T_{\text{s}})\\ \hat{\theta}^{DSP}(0)=\hat{\theta}^{SSP}(T_{\text{s}})\\ \end{array}\right. \tag{19}\]  
Lastly, the extremum conditions for DSP and SSP are the follows:  
\[\left\{\begin{array}{l}\theta^{DSP}(0.5T_{\text{d}})=\theta_{1}\\ \theta^{DSP}(0.5T_{\text{d}})=0\\ \theta^{SSP}(0.5T_{\text{s}})=\theta_{2}\\ \hat{\theta}^{SSP}(0.5T_{\text{s}})=0\\ \end{array}\right. \tag{20}\]  
The polynomial coefficient \(f_{i}\) and \(g_{i}\)(\(i\) = 0,1,..., 5) is determined by Equations (18)-(20).
Sensors_**2009**, \(9\), 7580-7594; doi:10.3390/s91007580  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**A Community-Based Event Delivery Protocol in**  
**Publish/Subscribe Systems for Delay Tolerant Sensor Networks**  
**[PERSON], [PERSON] *, [PERSON] and [PERSON]**  
Department of Computer Science and Engineering, University of Electronic Science and Technology of China, Cheng du 610054, China; E-Mails: [EMAIL_ADDRESS] (N.L.); [EMAIL_ADDRESS] (J.Z.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-138-8188-0533.  
_Received: 6 August 2009; in revised form: 26 August 2009 / Accepted: 3 September 2009 / Published: 28 September 2009_  
**Abstract:** The basic operation of a Delay Tolerant Sensor Network (DTSN) is to finish pervasive data gathering in networks with intermittent connectivity, while the publish/subscribe (Pub/Sub for short) paradigm is used to deliver events from a source to interested clients in an asynchronous way. Recently, extension of Pub/Sub systems in DTSNs has become a promising research topic. However, due to the unique frequent partitioning characteristic of DTSNs, extension of a Pub/Sub system in a DTSN is a considerably difficult and challenging problem, and there are no good solutions to this problem in published works. To ad apt Pub/Sub systems to DTSNs, we propose CED, a community-based event delivery protocol. In our design, event delivery is based on several unchanged communities, which are formed by sensor nodes in the network according to their connectivity. CED consists of two components: event delivery and queue management. In event delivery, events in a community are delivered to mobile subscribers once a subscriber comes into the community, for improving the data delivery ratio. The queue management employs both the event successful delivery time and the event survival time to decide whether an event should be delivered or dropped for minimizing the transmission overhead. The effectiveness of CED is demonstrated through comprehensive simulation studies.  
**Keywords:** event delivery; publish/subscribe; queue management; community
# Signal Preprocessing for Enhanced IoT Device Identification Using Support Vector Machine
## 3 Results
### SVM-Based Classifier with Min-Normalized Raw Signals  
The confusion matrix in Table 6 demonstrates that min normalization of raw Bluetooth signals yields a substantial improvement over the unnormalized case, with diagnostical accuracy climbing to 83.63% and effective accuracy reaching 95%. Among the four standalone wearables from the Rusins dataset (Classes 1-7 and 22), three devices achieve perfect classification after min normalization: Class 2, Class 3, and Class 18 each score 1.00 on the diagonal, while Class 1 attains 0.99, indicating near-flawless separability and complete resolution of the earlier mutual confusion. Within the Uzundurukan smartphone twins, several classes also benefit markedly. Classes 8 and 16 both achieve 1.00 on the diagonal, and Class 11 and Class 12 perform strongly with scores of 0.73 and 0.80, respectively, exhibiting only minor reciprocal confusion (0.03-0.07). Class 9 reaches 0.80, with a small spill-over into Class 12 (0.07). Nonetheless, a few classes remain challenging. Class 5 records only 0.17 on its diagonal and shows significant confusion with Class 14 (0.20), suggesting that its intrinsic signal features are still insufficiently distinctive under min normalization. Class 7 scores 0.90 but has a minor misclassification into Class 9 (0.03), while Class 14 attains 0.83 with a small 0.10 confusion toward Class 8. Overall, min normalization produces highly robust classification for most classes--completely resolving separability for all Rusins devices and many Uzundurukan twins--yet highlights a few persistent ambiguities (e.g., Class 5) that may require further feature engineering or classifier tuning to fully eliminate.
## 4 Overview of the Type of Classifiers Used for MR  
The step after feature extraction in AMC is the identification of the type of received signal [7]. Several common approaches, such as decision tree (DT), artificial neural networks (ANNs), support vector machine (SVM), k-nearest neighbor, and combinations of artificial intelligence techniques, have been used for classification. ANN and SVM are supervised machine learning, whereas clustering classifier belongs to unsupervised machine learning [83; 84; 85]. Researchers have used optimization techniques to select the dominant or significant features from the extracted features for improving the recognition accuracy of classifiers. They have considered AWGN to be the noise for AMC (because of its simplicity). However, few researchers have considered fading channels in AMC. The details regarding each classifier are provided in the subsequent section.  
\begin{table}
\begin{tabular}{||p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}||} \hline
**Reference(s)** & **Key features** & **Modulation Set** & **Advantages** & **Disadvantages** \\ \hline
[78; 79] & Cyclic frequency domain & 2 FSK, 4 FSK, 8 FSK & \(\bullet\) & Good discrimination between the M-FSK & \(\bullet\) & Poor discrimination of M-FSK at low SNR \\ \hline
[81] & Cyclostation-ary & M-QAM & \(\bullet\) & Robust to timing, phase, frequency offsets, and phase noise & \(\bullet\) & Discriminate only three types of M-QAM High identification of modulation schemes at high SNR \\ \hline
[62] & FFT & 2 FSK, 4 PSK, 8 FSK, 32 FSK & \(\bullet\) & Good discrimination of M-FSK at low SNR & \(\bullet\) & Cannot discriminate additional modulation types from the frequency spectrum \\ \hline
[63] & FFT & 2 FSK and 4 FSK & \(\bullet\) & High discrimination between M-PSK at lower SNR & \(\bullet\) & Requires many samples \\ \hline
[65] & WT & BASK, BPSK, and BPSK & \(\bullet\) & Robust at low SNR & \(\bullet\) & Discriminate a small set of modulation schemes \\ \hline
[66] & WT & PSK, QAM, FSK and ASK & \(\bullet\) & Good discrimination of modulation schemes & \(\bullet\) & Requires high SNR \\ \hline
[70] & WT & 4 QAM, 16 QAM, and 64 QAM & \(\bullet\) & Good discrimination of M-QAM & \(\bullet\) & Recognition rate is increased with increasing SNR and number of symbols \\ \hline
[84] & Constellation & 4 QAM, 16 QAM, 32 QAM, 64 QAM, 228 QAM, 256 QAM & \(\bullet\) & Shows good discrimination of M-QAM & \(\bullet\) & Cannot effectively recognise the higher-order QAM signal at lower SNR \\ \hline
[85] & Constellation & 16 QAM, 32 QAM, and 64 QAM & \(\bullet\) & Shows good discrimination of M-QAM & \(\bullet\) & Constellation identification normally requires high SNR \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._