esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Solid Phase Analysis  
For grain-size analysis, approximately 15-20 g of fresh sediment was treated with H\({}_{2}\)O\({}_{2}\) (3%) for a minimum of 24 h, to eliminate most of the organic matter. Subsequently, the sediment was wet-sieved through a 2 mm sieve, to remove coarse shell fragments. The resulting \(<\)2 mm fraction was analyzed, using a laser granulometer (Malvern Mastersizer, 2000). Total and organic carbon (C\({}_{\text{tot}}\) and C\({}_{\text{org}}\)) and nitrogen (N\({}_{\text{tot}}\)) content were determined by using an ECS 4010 Elemental Combustion System, Costech. C\({}_{\text{tot}}\) and N\({}_{\text{tot}}\) were determined at a combustion temperature of 1020 \({}^{\circ}\)C and C\({}_{\text{org}}\) at 920 \({}^{\circ}\)C [18], after progressive acidification with 0.1-1.0 M HCl (prepared from 37% HCl, Honeywell Fluka), to remove carbonates.  
The EPA Method 3052 [19] was applied, to determine total metal(loid) (As, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, V and Zn) contents. Three aliquots of PACS-3, a certified reference material (Marine Sediment, NRCC, Whitehorse, YT, Canada), were mineralized with the sediment samples, to verify the accuracy of the procedure. The solutions obtained were analyzed by Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES), using an Optima 8000 Spectrometer (Perkin Elmer, Waltham, MA, USA), equipped with an S10 autosampler. The measurements were conducted by using calibration curves obtained by dilution (range: 0.1-10 mg/L) of the standard solutions for ICP analyses (Sigma-Aldrich, St. Louis, MO, USA). Total Hg (THg) was determined by using a Direct Mercury Analyzer(DMA-80, Milestone, Sorisole, Italy), according to the EPA Method 7473 [20]. The limit of detection (LOD) was approximately 0.005 ng. The accuracy of the methods for analytical determination of trace elements was checked, using the certified reference material (PACS-3), and acceptable recoveries were obtained, ranging between 86 and 98%.  
In addition, a weak extraction with 0.5 M HCl was performed to assess the amount of the elements in association with Fe and Mn oxides and hydroxides [21]. The obtained extracts were analyzed by using a Perkin Elmer (Waltham, MA, USA) NexION 350X Inductively Coupled Plasma-Mass Spectrometry (ICP-MS), working in kinetic energy discrimination (KED) mode, using ultrahigh-purity helium, to minimize polyatomic interference. The instrument was calibrated, using five standard solutions (0.5-100 \(\upmu\)g/L) prepared by dilution from a multi-standard solution (Periodic Table MIX 1, Sigma Aldrich 10 mg/L). The matrix effect was evaluated through the analysis of laboratory-fortified samples, which can be considered standard solutions prepared in the sample matrix. Acceptable recoveries were obtained (77-104%) and attesting to a negligible matrix effect. The precision of the analysis expressed as RSD% was <3%.
# THz Rectennas and Their Design Rules
## 7 Fabrication Aspects
### Current Lithographic Techniques  
The key difficulty to fabricate nano-scale features with high definition lies in the way they are produced. Out of many available lithographic techniques, short wavelength photo-masking has been suggested and is referred to as extreme ultra violet lithography (EUVL) [105]. However, it is  
Figure 12: Polarization-dependent and independent response of the tunneling diode with the values of both components shown in the figure. Diodes are connected to the integrated dipole and bow tie antennas as described in [28]. The linear polarization angle \(\phi\) of the incident radiation is changed, and the DC response of the diode is monitored. A \(10.6\,\mathrm{\SIUnitSymbolMicro m}\) laser was used with other facilities to obtain \(5\times 10^{6}\,\mathrm{W}\,\mathrm{m}^{-2}\) intensity at the antenna under test (Reproduced with permission from [28], Elsevier, 1998).  
hampered by many difficulties relevant to cleaning and repairing [106]. Furthermore, the decreasing feature dimension would lead to increased fabrication costs due to the technological difficulty [107]. Moreover, these technologies are primarily concerned with fabricating the features on silicon, GaAs, etc., substrates, which are the ones used for commercial solar cells. Therefore, the cost is certainly higher just because of using the same substrate. Regardless of the technical details, it is expected that individual etching or writing of the nano features would be more expensive than mass stamping them. This is the motivation for the next fabrication method.
## 3 Results and Discussion
### Identification of Components of Zanthoxylum bungeanum Essential Oil  
The total ion flow diagram of _Zanthoxylum bungeanum_ essential oil obtained by GC-MS is shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Essential Oil** & **Inhibition Rate\%** & **Essential Oil** & **Inhibition Rate\%** \\ \hline _Litsea cubea_ & 4.66 \(\pm\) 3.27 & _Artemisia argyi_ & 6.96 \(\pm\) 2.75 \\ _Magnolia liliflora_ & 9.86 \(\pm\) 4.82 & _Nepeta cataria_ & 3.92 \(\pm\) 1.05 \\ _Olibanum_ & 9.00 \(\pm\) 4.01 & _Ligusticum chuanxiong_ & 10.49 \(\pm\) 6.01 \\ _Houttuynia cordata_ & 9.58 \(\pm\) 6.47 & _Forsyththia suspenas_ & 2.51 \(\pm\) 0.58 \\ _Centella asiatica_ & 6.33 \(\pm\) 2.74 & _Acorus tatarinovii_ & 3.21 \(\pm\) 0.76 \\ _Panax quinquefulus_ & 9.98 \(\pm\) 1.15 & _Synting ablation_ & 4.41 \(\pm\) 1.56 \\ _Schisandra chinensis_ & 6.99 \(\pm\) 2.35 & _Cuminum cyninum_ & 10.79 \(\pm\) 0.53 \\ _Zanthoxylum bungeanum_ & 57.10 \(\pm\) 0.30 & _Cirus reticulata_ & 2.97 \(\pm\) 0.39 \\ _Angelica sinensis_ & 5.56 \(\pm\) 1.27 & _Altractyldoles lanca_ & \(-\)0.07 \(\pm\) 0.70 \\ _Pogostemon achlin_ & 12.33 \(\pm\) 3.25 & _Panax ginseng_ & 9.37 \(\pm\) 2.25 \\ Blank & 0.90 \(\pm\) 0.71 & Acarbose 40 ug/mg & 88.97 \(\pm\) 2.58 \\ PEG2000 & 6.81 \(\pm\) 1.99 & Acarbose 80 ug/mg & 88.80 \(\pm\) 0.41 \\ \hline \end{tabular}
\end{table}
Table 2: The inhibition of \(\alpha\)-glucosidase due to the activity of plant essential oils (x \(\pm\) s).  
The names of the components of _Zanthoxylum bungeanum_ essential oil and their relative contents are shown in Table 3.  
The top five components were (-)-terpinen-4-ol (13.13%), (-)-\(\beta\)-pinene (11.17%), \(\gamma\)-terpinene (9.45%), terpinyl acetate (9.36%), and \(\alpha\)-terpineol (5.40%). According to the literature, the main components of Shaanxi red _Zanthoxylum bungeanum_ essential oil were (-)-terpinen-4-ol with a content of 19.7% [24], \(\beta\)-pinene with a content of 14.31% [25], limonene with a content of 21% [26], terpinyl acetate with a content of 5.40% [27], \(\alpha\)-terpineol with a content of 5.83% [28], and linalool with a content of 29% [29]. It was shown that the components of _Zanthoxylum bungeanum_ essential oil identified in this paper were basically the same as those in the literature except for limonene and linalool. Although linalool and limonene were not detected in this study, they are still important components of _Zanthoxylum bungeanum_ essential oil according to the literature. Therefore, the molecular structures of seven components ((-)-terpinen-4-ol, (-)-\(\beta\)-pinene, \(\gamma\)-terpinene, terpinyl acetate, \(\alpha\)-terpineol, linalool, limonene) were used for molecular docking in the subsequent molecular docking in this paper.
## 2 Related Work and Research Methods
### Lightweight Network  
The term \"Lightweight network\" refers to a specialized technology wherein high precision is maintained in neural networks while reducing computational complexity. In  
Figure 1: YOLOv7 target detection algorithm module diagram. (Conv is the convolution operation, ELAN is an efficient grid structure, MP is maximum pooling operation, CAT is splicing operation, UP is upsampling operation, and REP is detection head network, Conv*4 is the input tensor passing through four convolutional layers continuously).  
these networks, efforts are made to minimize complexity and computational costs. Prominent lightweight networks, such as ShuffleNet [17], GhostNetV2 [18], and FasterNet [19], extract image features efficiently, enabling the accomplishment of various visual tasks with decreased computational resources. Addressing the total number of channels in GhostNetV2 involves rearranging them within the block using a small number of channels, leading to a reduction in model storage space and computation. The efficiency of model training is enhanced in FasterNet through the utilization of spatially separable convolution and multi-scale training strategies. In scenarios characterized by constrained resources, effective solutions are presented by these lightweight networks, resulting in comparable performance to more intricate counterparts being achieved.
## 3 Results and Discussion
### Determination of the Warning Thresholds  
#### 3.3.1 Parameters Inversion Using DBA-LSTM  
The period from April to June 2019 was used as the input parameter for the DBA-LSTM model, the shear strength parameters in the numerical model were modified, and the RMSEs in the Y- and Z-directions were calculated to be 3.9 mm and 1.8 mm, with a maximum deviation of 6.1 mm at the point, indicating that the numerical simulation results are basically consistent with the field monitoring results and that the analytical model can be further used for stability analysis. Figure 10 shows the displacement in the Y-direction calculated based on the inversion results, where the data in the figure represent the calculated displacement values of each monitoring station. The displacement nephograms in the X- and Z-directions are not shown, due to space limitations.  
Figure 8: Fitting relationship and goodness of fit \(R^{2}\).
## 2 Cumulative Damage and Failure Intensity
### Equal Damage Model of Failure Intensity  
Due to the distinction of WC impact effects, the unit time damages differ under varying WCs. If there is a connection between them, it can be unified by using equal conversion. Supposing there are two WC, \(e_{0}\) and \(e_{k}\), the probability of \(n\) failures in \(e_{k}\) from 0 to \(t\) can be determined as follows:  
\[P_{k}(N_{0,t}=n)=\frac{\left(W(M(0,t|e_{k}))-W(0)\right)^{n}}{n!}\cdot\exp(-(W (M(0,t|e_{k}))-W(0))), \tag{4}\]The probability of \(n\) failures in \(e_{0}\) from 0 to \(T\) can be expressed as:  
\[P_{0}(N_{0,T}=n)=\frac{\big{(}W(M(0,T|e_{0}))-W_{0}(0)\big{)}^{n}}{n!}\cdot\exp(- (W(M(0,T|e_{0}))-W_{0}(0))), \tag{5}\]  
If the two probabilities are equal, then:  
\[P_{k}(N_{0,t}=n)=P_{0}(N_{0,T}=n)\Rightarrow W(M(0,t|e_{k}))=W(M(0,T|e_{0})), \tag{6}\]  
Under the standard WC \(e_{0}\), the unit time damage of system \(m(t|e_{0})\) is assumed as 1. The cumulative damage \(M(0,t|e_{0})\) from 0 to \(t\) can be obtained as \(t\). So:  
\[\begin{array}{l}W(M(0,t|e_{k}))=W_{k}(t),W(M(0,T|e_{0}))=W_{0}(T)=W(M(0,t|e_{ k}))=W_{0}(M(0,t|e_{k}))\\ \Rightarrow W_{k}(t)=W_{0}(M(0,t|e_{k}))\end{array}, \tag{7}\]  
This Equation (7) shows that when the unit time damage is 1, the CIF about cumulative damage can be regarded as a function of time. The cumulative damage and time can be regarded as a proportional relationship with a proportional value of 1. It can also be explained that the cumulative damage from 0 to \(t\) in WC \(e_{k}\) is equal to the cumulative damage from 0 to \(M(0,t|e_{k})\) in the standard WC \(e_{0}\). The equation is expressed as follows:  
\[\int_{0}^{t}m(u|e_{k})du=\int_{0}^{M(0,t|e_{k})}m(u|e_{0})du, \tag{8}\]  
The cumulative damage can be expressed as:  
\[M(0,t|e_{k})={G_{0}}^{-1}(G_{k}(t)),G_{k}(t)=\int_{0}^{t}m(u|e_{k})du, \tag{9}\]  
The FI function can be obtained by taking the derivative of CIF, which is a composite function of time:  
\[\begin{array}{l}w(t|e_{k})=\lim_{\Delta t\to 0}\frac{W(M(0,t+\Delta t|e_{k}))-W(M(0,t|e_{k}))} {\Delta t}\\ =\lim_{\Delta t\to 0}\frac{W(M(0,t|e_{k})+M(t,t+\Delta t|e_{k}))-W(M(0,t|e_{k}) )}{M(t,t+\Delta t|e_{k})}\cdot\frac{M(t,t+\Delta t|e_{k})}{\Delta t}\\ =m(t|e_{k})\cdot w_{0}(M(0,t|e_{k}))\end{array}, \tag{10}\]  
where \(w_{0}(t)\) is the FI in standard WC \(e_{0}\). If the system operates in \(e_{k}\) from 0 to \(t\), at time \(t\) the WC changed from \(e_{k}\) to \(e_{l}\), the amount of cumulative damage at \(t\) is \(M(0,t|e_{k})\) and the damage next unit of time is \(m(t|e_{l})\). The FI can then be derived as:  
\[\begin{array}{l}w(t|e_{k})=\lim_{\Delta t\to 0}\frac{W(M(0,t+\Delta t|e_{k}))-W(M(0,t|e_{k}) )}{\Delta t}\\ =\lim_{\Delta t\to 0}\frac{W(M(0,t|e_{k})+M(t,t+\Delta t|e_{l}))-W(M(0,t|e_{k}) )}{M(t+\Delta t|e_{l})}\cdot\frac{M(t,t+\Delta t|e_{l})}{\Delta t}\\ =m(t|e_{l})\cdot w_{
## 7 Conclusions  
This study demonstrates a high-speed all-optical encoder and comparator operating at 120 Gb/s using a carrier reservoir semiconductor optical amplifier-Mach-Zehnder interferometer (CR-SOA-MZI), contributing to the ongoing development of advanced optical logic architectures. The distinct contributions of this work include the following: (1) the integration of encoding and comparison functionalities within a unified CR-SOA-MZI structure, enabling multifunctional operation without increased structural complexity; (2) the realization of five key logic functions--\(\overline{\text{AB}}\), \(\overline{\text{AB}}\), AB (AND), \(\overline{\text{A}}\)\(\overline{\text{B}}\) (NOR), and AB + \(\overline{\text{A}}\)\(\overline{\text{B}}\) (XNOR)--within a single platform operating at 120 Gb/s; (3) the use of quality factor (QF) analysis as a robust performance metric, with QF values exceeding 8, indicating strong signal fidelity even at ultra-high speeds; and (4) an in-depth numerical investigation examining how key system parameters--such as input power, data rate, and amplified spontaneous emission (ASE) noise--affect performance. The findings suggest that basic logic operations (e.g., AND, NOR) maintain strong performance stability, while more complex functions (e.g., XNOR) remain within acceptable operational thresholds. Compared with conventional SOA-MZI designs, the CR-SOA-MZI structure shows improved nonlinear dynamics and faster carrier recovery, making it suitable for scalable, high-speed  
Figure 7: Envisioned experimental setup for the all-optical encoder and comparator using CR-SOA-MZIs.  
all-optical processing. This study extends earlier research on CR-SOA-based logic gates and offers a multifunctional architecture that supports broader optical computing applications.  
Conceptualization, A.K.; data curation, A.K.; formal analysis, A.K.; funding acquisition, A.K.; investigation, A.K. and K.E.Z.; methodology, A.K. and K.E.Z.; project administration, A.K.; resources, A.K.; software, A.K.; supervision, K.E.Z.; writing--original draft, A.K.; writing--review and editing, A.K. and K.E.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data are contained within the article.
# Low-Cost Area-Efficient FPGA-Based Multi-Functional ECDSA/EdDSA
## 3 Hardware Architecture
### Elliptic Curve Cryptography Unit  
The ECC unit is the top module of the system. It includes a controller, a random access memory (RAM), a read-only memory (ROM), an inversion module (Inv.), and four PEs. Figure 7 illustrates the architecture of the ECC unit. In our design, the point adding and doubling for both ECDSA and EdDSA are performed under projective coordination. The conversion from affine to projective coordination is performed without any additional fee. In comparison with other types of coordination, projective coordination offers optimization for parallelism processing by reducing data dependency. Four PEs and an inversion module are integrated to achieve the highest performance in projective coordination.  
Each processing element and inversion module has their register to store the temporal data during their operation. The input data for each PE are selected from the RAM, the initial data from ROM, the calculated data from the other PEs, and the generated data by themselves. The controller controls the operations of the four PEs and the inversion module to produce the appropriate results. The FSM of the ECC unit controller is designed to promote the parallelism of the four PEs. The RAM is used for multiple purposes: to store the data/result and exchange data among the PEs. The result is read from RAM in a specific address. When the operation of the processing elements and the inversion module is completed, the controller raises the finish signal to indicate that the result is valid and it can be read.  
Figure 6: Modular inversion architecture.  
Figure 7: Elliptic curve cryptography unit.
## 4 Experiment and Results  
### Dataset Preparation  
We collected 123,672 classical Chinese poems from the Internet as our corpus. Our corpus mainly contains regulated verse and quatrain poems with 5-character and 7-character, having all kinds of poetry styles, such as pastoral, descriptive, romantic, and more. We randomly chose 80% of our corpus as a training set, 10% as a validation set, and 10% as a test set.
# A Fault Diagnosis Method of Mine Hoist Disc Brake System Based on Machine Learning
###### Abstract  
The performance of the brake system is directly related to the safety and reliability of the mine hoist operation. Mining the useful fault information in the operation of a mine hoist brake system, analyzing the abnormal parts and causes of the equipment, and making accurate early prediction and diagnosis of hidden faults are of great significance to ensure the safe and stable operation of a mine hoist. This study presents a fault diagnosis method for hoist disc brake system based on machine learning. First, the monitoring system collects the information of the hoist brake system, extracts the fault features, and pretreats it by SPSS (Statistical Product and Service Solutions). This work provides data support for fault classification. Then, due to the complex structure of the hoist brake system, the relationship between the fault factors often has a significant impact on the fault. Considering the correlation between the fault samples and the attributes of each sample data, the C4.5 decision tree algorithm is improved by adding Kendall concordance coefficient, and the improved algorithm is used to train the sample data to get the decision tree classification model. Finally, the fault sample of the hoist brake system is trained to get the algorithm model, and then the fault diagnosis rules are generated. The state of the brake system is judged by classifying the data. Experiments show that the improved C4.5 decision tree algorithm takes the relativity of conditional attributes into account, has a higher diagnostic accuracy when processing more data, and has concise and clear fault classification rules, which can meet the needs of hoist fault diagnosis.  
m m e hoist; decision tree; kendall concordance coefficient; fault diagnosis Article
# SDNPS: A Load-Balanced Topic-Based Publish/Subscribe System in Software-Defined Networking
## 5 Performance Evaluation
### Performance Evaluation of SDNPS
#### 5.1.1 Delay and Loss Rate for 1:1 Transmission  
This set of experiments is devised to study the end-to-end delay characteristics of the SDNPS on the aforementioned testbed. we first analyze end-to-end delay between a pair of publisher and subscriber connected via a one-hop path (from Node 1 to Node 2), two-hop path (from Node 1 to Node 4) and three-hop path (from Node 1 to Node 6) in the topology, as depicted in Figure 5. For every test, 100,000 UDP packets are sent continuously from a publisher to a subscriber. We recorded the time period from the time that the first packet is sent from the publisher to the time that the last packet was received by the subscriber as end-to-end delay. As every test is repeated 1000 times, we compute the average value for end-to-end delay and packet loss. Table 1 depicts the results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Hops** & **Source** & **Destination** & **Average Delay (ms)** & **Average Loss Rate** \\ \hline
1 & 1 & 2 & 2439.16 & 4.23\% \\
2 & 1 & 4 & 2754.45 & 3.21\% \\
3 & 1 & 6 & 15,098.67 & 0.00\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average delay and loss rate for 1:1 transmission.  
Figure 5: Experiment topology.  
From Table 1, the average delay rises very quickly with the increase of the hop count. However, there is a dramatic phenomenon: the greater the delay, the lower the packet loss rate. The main reason is due to the performance of the virtual machine we adopt. The reception capacity of the virtual terminal node limits the performance of SDNPS.
## 4 Discussion
### Individual Factors in the Context of the PAPE Effect  
The collected findings clearly suggest the usefulness of PAPE protocols for short-term improvement in barbell velocity. However, several individual factors must be considered in practical training settings. The studies included in this review primarily involved male participants with at least two years of resistance training experience, most of whom were classified as advanced lifters (with bench press 1 RM values reaching up to 168.5 kg) [32]. According to the conclusions of [PERSON] and [PERSON] and [PERSON], athletes with higher strength levels are more resistant to fatigue and may more easily benefit from the PAPE effect compared to less-trained individuals. Similar observations were made by [PERSON] et al., who reported that a higher level of relative strength played a key role in achieving performance gains in power output among trained men [18; 35; 36]. In less experienced individuals--or conversely, elite athletes from strength sports--different responses to the same protocol may occur. It is likely that more highly trained individuals require a stronger stimulus or more precisely controlled rest intervals to achieve a measurable PAPE effect. Another important factor is the type of warm-up used. Several of the analyzed studies showed that a well-structured submaximal warm-up (e.g., short sets at 40% and 80% of the target load) can elicit an immediate increase in mean velocity (MV) during the first training set. This response often aligns with the principles of PAPE, where performance is enhanced immediately following a conditioning activity [29]. However, to maintain such effects over longer sequences of exercises, closer control over training volume and rest duration is required.  
Some studies found no clear advantage between shorter rest intervals (3-4 min) and longer ones (6-8 min), but much evidence suggests that a 4-5 min interval may be sufficient to reduce fatigue while maintaining elevated neuromuscular readiness. Reducing this rest to 1-2 min generally diminishes the beneficial effect--especially when high loads are involved [27; 30].  
The specificity of the sport discipline also plays a critical role in the context of PAPE. In sports focused on maximal strength (e.g., powerlifting), the stimulus intensity must be high enough while avoiding premature fatigue. In contrast, disciplines that combine strength and power (e.g., combat sports, basketball) may benefit more from velocity-focused modifications at moderate loads. Some studies suggest that optimizing movement velocity can facilitate repeated actions, such as strikes or throws, which are essential in these disciplines [2; 3].