esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Materials Characterization and Experiment Set-Up  
The CeO\({}_{2}\)NPs (particle size \(<\) 25 nm) in the form of nanopowder were purchased from Sigma-Aldrich Co. LLC. The soil was collected from a residential area located in the proximity of the University of Udine, Italy (46\({}^{\circ}\)04\({}^{\prime}\)52\({}^{\prime}\) N, 13\({}^{\circ}\)12\({}^{\prime}\)33\({}^{\prime}\) E; top 20 cm), air-dried at room temperature and sieved through a 2 mm mesh before the characterization. The soil was classified as clay soil (sand 26%, silt 6.4% and clay 67.6%) with a pH 7.40 \(\pm\) 0.01, CEC 13.9 \(\pm\) 0.3 (cmol kg\({}^{-1}\) DM) and OM 4.4 \(\pm\) 0.6 (%); it was sieved through a 2 cm mesh before the addition of the nanoparticles and the application of the biochars. Both these two phases were achieved using a cement mixer (20 min for each mixing and 15 days equilibration between the nanoparticles and the biochar addition). The two biochars used to amend the soil were produced using the same feedstock (fir wood pellets) but with a different quenching procedure: wet (BCwetQ) and dry (BCdryQ), as described by [27]. Briefly, the first quenching procedure was performed by watering the pyrolyzed feedstock directly in the kil of the carbonization system, while the second quenching procedure was done by sealing the kiln until the biochar cooling was complete. The mixtures were used to fill up 12 packed lysimeters (height: 55 cm; diameter: 15 cm) (Figure 1) according to the following experimental designs: CTRL (soil + CeO\({}_{2}\)NPs at 1000 ppm), DRY (soil + CeO\({}_{2}\)NPs at 1000 ppm + 5%DW biochar dry-quenched) and WET (soil + CeO\({}_{2}\)NPs at 1000 ppm + 5%DW biochar quenched with water). There were 4 replications. The biochars (BCwetQ and BCdryQ) were produced using the Elsa Research carbonization system (BLUECOMB(tm)) [27].
# SS3 DNet-AF: A Single-Stage, Single-View 3D Reconstruction Network with Attention-Based Fusion
## 3 Proposed Methods
### Image Encoder  
The image encoder module extracts high-level features from the input image using a pre-trained ResNet-18 model with a frozen classification layer. This module includes a convolutional layer, followed by four residual blocks, each having two convolutional layers and a global average pooling layer to flatten the extracted features. These residual blocks enhance the network's ability to capture and preserve key features. These features are then fed into the deep attention network, as detailed in Module-03. The network layers involved in this module are listed below.
## 3 Results
### Observed Network Performance
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **EMG** & **SENS** & \multirow{2}{*}{_p-Value_} & **SP** & \multirow{2}{*}{_p-Value_} & **ACC** & \multirow{2}{*}{_p-Value_} & **F1** & \multirow{2}{*}{_p-Value_} \\  & **Type** & \(\pm\)** SD (\%)** & & & \(\pm\)**SD (\%)** & & & \(\pm\)**SD (\%)** & \\ \hline \multirow{2}{*}{2G-B} & Paretic & \(57.09\pm 10.35\) & \multirow{2}{*}{0.0012**} & \multirow{2}{*}{\(57.09\pm 10.35\)} & \multirow{2}{*}{0.0012**} & \multirow{2}{*}{\(88.36\pm 12.07\)} & \multirow{2}{*}{0.0048**} & \multirow{2}{*}{\(57.72\pm 14.39\)} & \multirow{2}{*}{\(0.156\)} \\ \cline{2-2} \cline{5-8}  & Fusion & \(62.00\pm 10.79\) & & & \(62.00\pm 10.79\) & & & \(84.75\pm 13.17\) & & \(60.51\pm 13.33\) & \\ \hline \multirow{2}{*}{3G-B} & Paretic & \(52.58\pm 11.38\) & \multirow{2}{*}{\(1.3226\times\)} & \(79.10\pm 7.79\) & \multirow{2}{*}{\(1.3900\times 10^{-12}\)***} & \multirow{2}{*}{\(1.3900\times 10^{-12}\)***} & \multirow{2}{*}{\(77.95\pm 8.30\)} & \multirow{2}{*}{\(7.5595\times\)} & \multirow{2}{*}{\(52.45\pm 13.79\)} & \multirow{2}{*}{\(3.1825\times\)} \\ \cline{2-2} \cline{5-8}  & Fusion & \(63.54\pm 11.48\) & \(10^{-12}\)*** & & \(86.58\pm 6.07\) & & & \(85.69\pm 6.63\) & \(10^{-12}\)*** & \\ \hline \multirow{2}{*}{4G-B} & Paretic & \(43.81\
## 5 Simulation Test and Data Analysis
### Case Analysis  
This subsection is based on a real warehouse case to verify the conclusions described in this section. The case is a four-way shuttle system for auto parts run by company A, with ten layers of shelves, 64 columns per layer, a single layer deep. The total capacity of the storage system is 1280 totes, and the current system's average daily outgoing volume is approximately 1800 totes, working 12 h a day. The other parameters of the system are shown in Table 10.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Take Value** & **Parameters** & **Take Value** \\ \hline \(Vs\) (m/s) & 2 & \(\gamma_{l}\) (s) & 3 \\ \(\varepsilon_{s}\) (s) & 2 & \(L\) (m) & 0.65 \\ \(\gamma_{s}\) (s) & 5 & \(W\) (m) & 0.45 \\ \(Vl\) (m/s) & 2.5 & \(WA\) (m) & 0.45 \\ \(\varepsilon_{l}\) (s) & 1 & \(H\) (m) & 0.67 \\ \hline \hline \end{tabular}
\end{table}
Table 10: System parameters.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Number of Layers** & & **4** & & & **5** & & & **6** & \\ \hline Number of columns & 16 & 32 & 40 & 24 & 32 & 48 & 40 & 48 & 56 \\ \hline Specification & 128 & 256 & 320 & 240 & 320 & 480 & 480 & 576 & 672 \\ \hline Number of layers & & 7 & & & 8 & & & 9 & \\ \hline Number of columns & 48 & 56 & 64 & 56 & 64 & 72 & 64 & 72 & 80 \\ \hline Specification & 672 & 784 & 896 & 896 & 1024 & 1152 & 1152 & 1296 & 1440 \\ \hline \hline \end{tabular}
\end{table}
Table 9: System size parameters.  
The comparison between the serial operation strategy in this system and the parallel operation strategy incorporated in this paper is shown in Figure 15. Once the arrival rate reaches \(\lambda\,_{r}=160\) cases per hour, the system will increase in response to the arrival rate, which makes the number of customers served in the system tend towards the maximum load value, while the shuttles will all be in a busy state; thus, the increase in the waiting time causes the system response time to speed up. However, compared with a single serial strategy, the four-way shuttle system outbound flow model provided in this paper with the addition of a parallel strategy alleviates, to a certain extent, the excessive increase in the system response time. It can be seen from the data that when the average arrival rate of this system is 150 cases per hour, the addition of a parallel operation strategy can reduce the response time of the pickup operation by 4.17%. Between the arrival rate of 50 cases per hour and the arrival rate of 200 cases per hour, adding the parallel operation strategy reduces the response time of the picking operation by 4.36% on average.
# LEACH-MTC: A Network Energy Optimization Algorithm Constraint as Moving Target Prediction
## 1 Introduction  
Wireless sensor networks (WSNs) are intelligent communication networks composed of a large number of sensors in a multi-hop and self-organizing manner. WSNs have many advantages including flexible deployment, low price, low power consumption, and high accuracy of information acquisition. Thus, they are widely used in military fields [1, 2, 3], environmental monitoring [4, 5, 6], industrial control [7, 8, 9], and so on. However, as network nodes are usually deployed in complex, dangerous places, it is difficult to maintain them frequently, and the energy carried by the node is limited. Once the energy of nodes is exhausted, they are die, and as a result, they cannot be guaranteed to detect and track targets, which leads to the subsequent decline of the monitoring performance of the network [10, 11, 12]. Therefore, how to effectively reduce nodes' energy consumption and prolong the lifetime of WSNs is of great significance.
## 2 Materials and Methods
### DNA Extraction, Polymerase Chain Reaction (PCR) Amplification, and Sequencing 2.5.1. DNA Extraction
#### 2.5.2 165 rRNA and ITS Gene Amplicon Sequencing  
PCR amplification of the bacterial 165 rRNA gene V3-V4 region was performed using the forwards primer 338F (ACTCCTACGGGAGGCAGCA) and the reverse primer 806R (GGACTACHVGGGTWTCTAAT). PCR amplification of fungal internal transcribed spacer (ITS) regions was performed with the primers ITS3 (GCATCGATGAAAGCAGC) andITS4 (TCCTCCGCTTATGATGC). Sample-specific 7-bp barcodes were incorporated into the primers for multiplex sequencing. The PCR components contained 5 \(\upmu\)L of Q5 reaction buffer (5\(\times\)), 5 \(\upmu\)L of Q5 HighFidelity GC buffer (5\(\times\)), 0.25 \(\upmu\)L of Q5 High-Fidelity DNA Polymerase (5 U/\(\upmu\)L), 2 \(\upmu\)L (2.5 mM) of dNTPs, 1 \(\upmu\)L (10 \(\upmu\)M) of each forward and reverse primer, 2 \(\upmu\)L of DNA template, and 8.75 \(\upmu\)L of ddH2O. Thermal cycling consisted of initial denaturation at 98 \({}^{\circ}\)C for 2 min, followed by 25 cycles consisting of denaturation at 98 \({}^{\circ}\)C for 15 s, annealing at 55 \({}^{\circ}\)C for 30 s, and extension at 72 \({}^{\circ}\)C for 30 s, with a final extension of 5 min at 72 \({}^{\circ}\)C. PCR amplicons were purified with Agencourt AMPure Beads (Beckman Coulter, Indianapolis, IN, USA) and quantified using the PicoGreen dsDNA Assay Kit (Invitrogen, Carlsbad, CA, USA). After the individual quantification step, amplicons were pooled in equal amounts, and paired-end 2 \(\times\) 300 bp sequencing  
Figure 2: The brewing process of Hunan light-flavor Baijiu.  
was performed using the Illumina MiSeq platform with MiSeq Reagent Kit v3 at Shanghai Personal Biotechnology Co., Ltd. (Shanghai, China).
# Time-Sensitive Networking in IEEE 802.11 be: On the Way to Low-Latency WiFi 7
## 4 Supporting TSN in WiFi 7
### Traffic Classification  
IEEE 802.1Q standard specifies up to eight different traffic classes by means of the priority code point (PCP) in the VLAN tag, which helps to identify and differentiate time-sensitive traffic. In concordance, bridges supporting IEEE 802.1Q allocate packets to outbound queues according to their traffic class. Then, if no other TSN traffic scheduling (e.g., IEEE 802.1 Qbv) or shaping method (e.g., IEEE 802.1 Qav) is used, a strict priority policy first forwards packets from queues with higher traffic class values.  
IEEE 802.11-2016 specification supports VLAN tag traffic stream differentiation as defined in IEEE 802.1Q, by means of the traffic specification (TSPEC) and traffic classification (TCLAS) elements (the IEEE 802.11 header includes the traffic ID (TID) field to classify the type of traffic. TID is used to select a user priority (UP) for prioritized QoS or a TSPEC for parameterized QoS. Whereas TID values between 0 and 7 are considered UPs, those between 8 and 16 refer to TSPECs). In consequence, appropriate traffic classification mapping enables seamless interworking among wired and wireless networks [40].  
As for the EDCA access categories (ACs), they map directly from the aforementioned Ethernet-level traffic classes. However, having only four ACs is insufficient for fine control of real-time applications, as they cannot provide hard bounds on latency/jitter, especially under congestion. For that reason, TGbe is also considering possible enhancements to EDCA, such as the incorporation of a new AC with the highest priority for time-sensitive traffic [41], as well as some modifications to how obtained transmission opportunities(TXOPs) are used (for instance, to allow using any TXOP, regardless the AC that has obtained it, to send time-sensitive traffic when available [20]).
# Research on New Drilling Downlink Communication Based on Anisotropic Magnetoresistive Technology
## 1 Introduction
### Mathematical Model of Downhole Drill String Current Distribution
The transmitting current can be obtained by Equation (6):  
\[I(a)=\frac{V_{t}\sin\omega t}{Z^{2}}\frac{1}{4\pi\sigma H\sqrt{L^{2}+a^{2}}}. \tag{6}\]  
The current at any point a on the drill string is equal to the sum of all current elements from \(a\) to \(H\), that is \(I_{a}=\sum\limits_{a}^{H}I(x)\). Its differential form is shown in Equation (7):  
\[I(a)=\int\limits_{a}^{H}I(x)dx. \tag{7}\]  
Equation (8) can be obtained by substituting Equation (6) into Equation (7):  
Figure 3: Model connected to drill string. \[\varphi(r)=q/(4\pi\varepsilon\left|\overset{\rightarrow}{r}-r^{\overset{ \rightarrow}{1}}\right|). \tag{1}\]0  
By solving Equation (8), Equation (9) is obtained:  
\[\varphi(r)=q/(4\pi\varepsilon\left|\overset{\rightarrow}{r}-r^{\overset{ \rightarrow}{1}}\right|). \tag{1}\]1  
\(V_{t}\sin\omega t\) is the power supply voltage of the transmitter, \(\sigma\) is the conductivity of the rock and soil layer, \(Z\) is the input impedance value, \(H\) is the length of the downhole drill string, \(L\) is the distance between two electrodes, and \(a\) is the distance from any point of the drill string to the ground. The current value at any point of the drill string is obtained, as shown in \[\varphi(r)=q/(4\pi\varepsilon\left|\overset{\rightarrow}{r}-r^{\overset{ \rightarrow}{1}}\right|). \tag{1}\]2 The distance between the two electrodes at the transmitting end is 10 m, the electrical conductivity of the rock and soil layer is \(10^{-3}\) S/m, the drill string depth is 500 m and 1000 m, and the voltage of the transmitting end is 330 V, and the impedance \(Z\) is 3420 [29]. It can be seen from Figure 4 that under the condition of only changing the depth of the downhole drill string, the current value at any point on the drill string changes logarithmically with its position, and the closer to the bottom of the drill string, the greater the current loss in the rock and soil layer, and the smaller the current on the drill string. This has important reference significance for the position arrangement of the anisotropic magnetoresistance sensor in the actual measurement process.
## 3 System Model
### WiFi Fingerprint Database  
The database containing locations and RSS is established in the offline phase. The walkthrough method has been recognized as a time and cost-effective approach to construct  
Figure 1: AO-PSO-PF positioning algorithm flow chart.  
a fingerprint database, enhancing efficiency while reducing redundancy [34]. As depicted in Figure 2, reference points are initially set within the test area, spaced approximately 2 to 4 m apart. A laser range finder is then used to measure the 2D coordinates of these points. During the walkthrough, both the duration of the walk and the RSS fingerprint data are recorded at each RP [35]. Finally, fingerprints of other locations are obtained using interpolation.  
WiFi _RSS_ update rates for Android smartphones are typically low, such as the Samsung S9+ with an update rate of 0.2 Hz. During the survey, when the surveyor reaches the reference point, they press a button to record the time. Subsequently, the WiFi fingerprint closest to the recorded time is selected as the reference point fingerprint. Below is the WiFi fingerprint for the \(i\)-th reference point [35].  
\[W_{i}=\left\{pos_{i_{r}},(mac_{i,1},RSS_{i,1}),(mac_{i,2},RSS_{i,2}),(mac_{i,j}, RSS_{i,j}),\ldots,(mac_{i,m},RSS_{i,m})\right\} \tag{1}\]  
where \(pos_{i}\) is the \(i\)-th reference point's 2D coordinate, and the media access control address and \(RSS\) of the \(j\)-th AP at the \(i\)-th reference point are denoted as \(mac_{i,j}\) and \(RSS_{i,j}\), respectively. Additionally, \(m\) denotes the number of available APs at the \(i\)-th reference point [35].
# An Effective Ensemble Approach for Preventing and Detecting Phishing Attacks in Textual Form
## 2 Related Work
### Limitations of the Literature  
Despite recent progress in predicting the performance of phishing email detection, numerous unaddressed constraints still need to be addressed in the literature. These include the limitation of comprehensive evaluation metrics and samples, a lack of experimentation that takes into account their evolving nature, and the use of non-representative textual features. One notable limitation of the research is the need for complete consideration of various data sources. Specifically, previous studies used only a few datasets to conduct their evaluations. Although ensemble learning has achieved impressive outcomes in several fields [25], it has yet to be used in phishing email detection. Specifically, a limited number of previous studies [23; 24] use ensemble learning techniques, which have many drawbacks. Some of the issues with the assessment datasets include a limited number of outdated phishing emails, inadequate representation of the emails due to insufficient implemented features, and poor evaluation measures, namely the AUC and accuracy. Therefore, there is a significant deficiency in the suitability of ensemble learning for identifying contemporary phishing email assaults. We have devised a method that applies text-based features to deliver a comprehensive email representation tailored to machine learning classification applications. The inadequacies in the literature that have been discovered are effectively addressed by this strategy. Our proposed solution uses two ensemble learning methods, weighted ensemble learning (Method 1) and soft voting ensemble learning (Method 2), which are both methods that fall under the general category of ensemble learning.  
This work offers new features for evaluating our proposed method, guaranteeing a neutral and precise assessment. The primary objective is to establish a strong and efficient approach to combat the growing threat of email phishing attacks.
## 5 Discussion
### Limitations and Future Work  
While our method has demonstrated significant promise in interpreting FDS for 3D reconstruction, it is important to acknowledge its current limitations, particularly in handling incomplete, distorted, or ambiguous sketches. Our primary objective is to enhance the interpretation of sketches created by designers during the early stages of conceptualization. However, a key challenge remains: existing approaches, including ours, face difficulties when dealing with incomplete sketches that exhibit extreme distortions, am  
Figure 10: Qualitative results on test set. (**Column 1**) shows ground truth models. (**Column 2**) shows reconstructed models obtained by Meshsdf [38], (**Column 3**) shows reconstructed models obtained by Sketch2 Mesh [37], (**Column 4**) shows reconstructed models obtained by ours.  
biguous contours, overlapping strokes, or missing elements. This is especially problematic when sketches contain support lines or partial details, as current methods often merge these lines with adjacent curves, thereby losing critical contextual information required for accurate 3D reconstruction.  
To address these challenges, we propose several strategies aimed at enhancing the robustness of our method in handling incomplete, distorted, or ambiguous sketches. Previous works have already tackled similar issues. For instance, [PERSON] and [PERSON] [53] introduced an image-processing technique to extract clean vector curves from incomplete sketches. By analyzing edge features and line continuity, their method can infer missing elements, which is essential for reconstructing 3D shapes from incomplete sketches. Similarly, [PERSON] et al. [54] employed deep learning algorithms to iteratively process sketch images, refining blurred or incomplete parts to generate more accurate 3D models. Additionally, [PERSON] et al. [13] proposed a machine learning-based approach for reasoning over sketches, which predicts missing or ambiguous parts by leveraging design patterns learned from a large dataset. This enables their system to infer plausible continuations of partial sketches. We suppose that integrating such machine learning methods into our pipeline could significantly enhance our model's ability to handle missing components, ambiguous details, and extreme distortions, leading to more reliable 3D reconstructions even when the input sketch is incomplete, unclear, or contains overlapping strokes.  
In addition to these technical improvements, we propose incorporating a user feedback loop into the system. This would allow the system to interact with the designer when encountering ambiguous or incomplete sketches, prompting for clarifications or additional input. This iterative feedback process would help ensure that the final 3D model aligns more closely with the designer's intent. By adopting this collaborative approach, our system would not only become more robust in handling incomplete, distorted, or ambiguous sketches but also improve the overall user experience, making the process of converting freehand sketches into 3D models more intuitive and efficient.
# Error Correction Method of TIADC System Based on Parameter Estimation of Identification Model
## 5 Conclusions  
This paper presents a parameter estimation and calibration method for the TIADC system identification model. According to system identification theory, the TIADC system identification model is established. The least mean square estimation algorithm is used to estimate the parameters related to the system frequency characteristics, and the digital compensation filter is established through the frequency domain correction method to correct the error of the TIADC system. A 16-bit, four-channel TIADC data acquisition system is used to verify the proposed method. Through simulation and experiments, the actual input and output characteristics of the data acquisition system are consistent with the nature of the identification model, and the digital compensation filter established by the frequency domain correction method has high correction accuracy. The data acquisition system can achieve SNR above 59.1 dB and SFDR above 68.9 dB in the input signal from 150 MHz. SNR increases more than 12 dB, SFDR increases more than 13 dB, and the dynamic performance of the system is close to its sub-ADC dynamic performance. The experimental results show the feasibility of the model identification method and the effectiveness of error correction.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **SNR/dB** & **SFDR/dB** \\ \hline Method used in this article & 59.1 & 68.9 \\ Method used in [28] & 56.4 & 70.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the correction effects of the methods in [28] and in this article.  
Figure 11: The output spectrum of the 150 MHz sinusoidal signal before and after compensation: (**a**) before calibration; (**b**) after calibration.  
**Author Contributions:** Conceptualization, N.S. and J.L.; methodology, D.Z.; software, J.J.; validation, N.S., D.Z. and J.J.; formal analysis, X.P.; investigation, S.W.; resources, Z.Z.; data curation, W.C.; writing--original draft preparation, N.S.; writing--review and editing, N.S.; visualization, C.H.; supervision, D.Z.; project administration, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 61973280), the National Natural Science Foundation of China (No. 62003316) and the Shanxi Province Key R&D Program (202003D111007).  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.