esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, the crack propagation mode of concrete slabs under low-velocity impacts using different numbers and distributions of impact heads was studied through numerical simulation, and then the numerical simulation results were compared with those of indoor low-velocity impact tests to verify the rationality of the simulation. Finally, according to the field test results, the layout of the drop hammer impact heads was optimized using numerical simulation. The main conclusions are as follows:  
Under the impact crushing of a single impact head, the compression shear failure zone appeared on the top surface of the concrete slab, and a large stress concentration appeared at the four vertices and sides of the rectangular impact head. Cracks first appeared at the bottom of the concrete slab, and then the main cracks developed upwards until they ran through the concrete slab.  
In the multi-impact head fracture tests, a cracked core was formed in the center of the concrete slab. When three impact heads were used, the geometry of the cracked core of the concrete slab was approximately triangular; however, when four impact heads were used, the geometry of the fracture core was approximately quadrilateral. Compared with branch cracks, the main cracks of the cracked cores were more likely to propagate to the edge of the concrete slab. Moreover, the crack development and final fracture state of the concrete slab was affected by the size of the slab.  
The numerical simulation results of low-velocity impact tests showed a good agreement with the experimental data; therefore, it seems that the numerical method can predict low-velocity impact test results with an acceptable accuracy.  
The optimal layout of drop hammer impact heads as determined by numerical simulation not only ensured the homogenized micro-crack crushing effect of the concrete pavement, but also effectively solved the problem of longitudinal crack extension of the cracked core, so as to effectively avoid the generation of longitudinal reflection cracks in the asphalt overlay. However, further field tests should be carried out to evaluate the rationality of the numerical simulation on the layout optimization of hammer impact heads in actual concrete pavement rehabilitation.  
Conceptualization, W.L. and J.Y.; methodology, J.Y.; software, W.L.; validation, W.L., Y.G. and J.Y.; formal analysis, W.L.; investigation, J.Y.; resources, B.L.; data curation, Y.G.; writing--original draft preparation, W.L.; writing--review and editing, J.Y.; visualization, Y.G.; supervision, W.L.; project administration, B.L.; funding acquisition, B.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Project of Science and Technology of Henan Transportation Department (Grant numbers 2018J4 and 2019J1).  
Not applicable.  
The authors declare no conflict of interest.
###### Abstract  
The spatial distribution characteristics and slip rate in the Xianshuie Fault Zone (XSHFZ) are still subject to controversy, and the segments where creeping movement occurs within the fault remain unclear. In this paper, the three-dimensional deformation field of the XSHFZ and its neighboring areas is obtained by integrating InSAR and GNSS data. Subsequently, based on the three-dimensional deformation field, an elastic dislocation model is employed to analyze the slip rate, locking state, and creeping movement within the XSHFZ. The results show that the XSHFZ is a typical sinistral strike-slip fault with compressional characteristics. The slip rate of the XSHFZ ranges from 9.3 to 14.3 mm/yr. The average strike-slip rate of the Qianning and Kangding segments surpasses that of the eastern and western segments, while the Moxi segment exhibits the lowest slip rate. The locking depth of the XSHFZ is estimated to be between 13 and 26 km, with shallow creep movement predominantly concentrated in three segments: Daofu, Qianning, and Kangding, where the shallow creep rate ranges from 1.5 to 4.9 mm/yr. The XSHFZ is known for its short recurrence period of strong earthquakes and frequent seismic activities. A quantitative study of fault slip rates, locking depth, and creeping movement provides essential support for analyzing its seismic hazards. The seismic hazard of each segment of the Xianshuie Fault Zone (XSHFZ) was analyzed based on the principle of seismic moment balance. The areas with high seismic hazards in the Xianshuie Fault Zone correspond to the locations of seismic gaps along the fault. Specifically, the Qianning segment and the Yalahe and Selaha faults within the Kangding segment are associated with seismic gaps and are at risk of experiencing earthquakes with magnitudes of 6.9, 6.9, and 6.6, respectively. The results highlight the importance of continuous monitoring and preparedness measures to mitigate the seismic risks present in the XSHFZ.  
the Xianshuie fault zone; three-dimensional deformation field; fault strike-slip rate; locking depth; seismic hazard +
Footnote †: journal: applied sciences  
0000-0001-523-2276][PERSON]  
1 State Key Laboratory of High-Speed Railway Track System, Beijing 100081, China; [EMAIL_ADDRESS]  
2 Railway Engineering Research Institute, China Academy of Railway Sciences Corporation Limited,  
3 China Academy of Railway Sciences Corporation Limited, Beijing 100081, China  
4 The First Monitoring and Application Center, China Earthquake Administration, Tianjin 300180, China;  
[EMAIL_ADDRESS] (C.C.); [EMAIL_ADDRESS] (B.T.); [EMAIL_ADDRESS] (Q.W.); [EMAIL_ADDRESS] (Y.D.)  
5 School of Geology and Geomatics, Tianjin Chenglian University, Tianjin 300384, China
## 2 Methodology
### Data Collection and Pre-Processing  
The following section details the data collection and evaluation process. Figure 3 is a design diagram that describes how the different datasets were organized in the experiment and organizes the process into 4 blocks with 3 phases (data collection, preparation, and analysis). The first block (top left) in Figure 3 refers to how the process parameter dataset was collected in real-time. All process parameters (MPS, Laser Power, etc.) were collected during an actual print and recorded for each layer of the print. After the part was completed, a dataset was created from ex-situ computerized tomography (CT) scans to determine low-density occurrences (based on a threshold) at each layer. The ex-situ process is highlighted in Figure 3 (second block bottom left) and was used to create binary labels of \"acceptable\" (0) or \"unacceptable\" (1) layers based on an adjustable threshold and were appended to the process parameter dataset. The focal spot size of the CT scanner is 13.92 microns and it can make enough resolution to discern critical porosities in each layer. Once the dataset was pre-processed and prepared inter-layer feature importance and correlation analysis was conducted, as shown in the third block of Figure 3. Finally, inter-layer feature analysis was performed through the use of an advanced DL technique shown in the fourth block of Figure 3. Additional ML classification techniques were used to classify inter-layer quality of the build using features only and compared to the CNN model in Section 3. Figure 3: Design Diagram. Figure 2: Inconel 625 sample hollow cylinders. Once both samples were printed, the data generated from the process that features the process parameters and the sensor signatures were used in a post-process analysis. In parallel, the samples were removed from the plate with a wire EDM step and sent to undergo an XCT scan to detect porosity and the location of identified pores. XCT is defined as a method of establishing 3D representations of objects through obtaining X-ray images centered around an axis of rotation and developing 3D models through algorithms [49; 50; 51]. Between 2010-2014, XCT became a well-established method for measuring porosity by comparing empty voxels [52]. It has since developed into a nondestructive measuring technique [53; 54; 55; 56]. Table 1 lists all the AM parameters collected synchronously during the AM process of these samples. During the builds of the two samples, around 15-23 data points were collected at each layer (for layers in the uniform portion of the cylinders). The sampling rate was around 0.125 s (8 Hz). In the initial stage, we removed the AM parameter data that (1) is statistically insignificant, and (2) physically may not affect the quality of the part. This resulted in two separate datasets, one containing process parameters during the AM build process and another containing the CT scan features for each of the parts. The raw AM dataset was
# An Adaptive Security Framework for Internet of Things Networks Leveraging SDN and Machine Learning
## 4 Secure IoT Communication and Attack Mitigation
### Congestion Control in IoT Networks
#### 4.2.3 Congestion Mitigation  
After congestion occurs, mitigation techniques help restore network performance and stability. Common mitigation methods include the following [32]:  
* Rate Limiting: Nodes temporarily reduce transmission rates to relieve the congested paths when congestion is detected.
* Load Redistribution: Rerouting subsets of traffic to underutilized paths alleviates the overloaded routes. SDN controllers can facilitate global load redistribution.
* Queue Management: Active queue management drops low-priority packets before the buffers become full in order to control congestion.
* Window Limiting: Reducing the sliding window sizes limits how much new data can enter the network during congestion.
* Scheduling and Admission Control: Intelligently scheduling packet transmissions and limiting new flows prevents oversubscription.  
Rate limiting provides immediate congestion relief but can impact throughput. Combining multiple mitigation strategies balances performance, reliability, and congestion responsiveness in IoT systems. Congestion control is critical for IoT networks to maintain stability and meet quality-of-service needs. IoT protocols and architectures must evolve to include native congestion management capabilities. Machine learning also offers new possibilities to make congestion control adaptive and predictive. As the IoT scales massively, optimized congestion control will be essential to delivering robust performance.
# A 2-V 1.4-dB NF GaAs MMIC LNA for K-Band Applications
###### Abstract  
A 1.4-dB Noise Figure (NF) four-stage K-band Monolithic Microwave Integrated Circuit (MMIC) Low-Noise Amplifier (LNA) in UMS 100 nm GaAs pHEMT technology is presented. The proposed circuit is designed to cover the 5G New Release n258 frequency band (24.25-27.58 GHz). Momentum EM post-layout simulations reveal the circuit achieves a minimum NF of 1.3 dB, a maximum gain of 34 dB, \(\mid\)\(S_{11}\)\(\mid\) better than -10 dB from 23 GHz to 29 GHz, a \(\mid\)\(P_{\text{1 dB}}\) of -18 dBm and an OIP3 of 24.5 dBm. The LNA draws a total current of 59.1 mA from a 2 V DC supply and results in a chip size of \(3300\times 1800\) um\({}^{2}\) including pads. We present a design methodology focused on the selection of the active device size and DC bias conditions to obtain the lowest NF when source degeneration is applied. The design procedure ensures a minimum NF design by selecting a device which facilitates a simple input matching network implementation and obtains a reasonable input return loss thanks to the application of source degeneration. With this approach the input matching network is implemented with a shunt stub and a transmission line, therefore minimizing the contribution to the NF achieved by the first stage. Comparisons with similar works demonstrate the developed circuit is very competitive with most of the state-of-the-art solutions.  
low noise amplifier; noise figure; monolithic microwave integrated circuit; gallium arsenide; electromagnetic simulation; input return loss; K-band; 5G +
Footnote †: journal: Sensors
# A Comprehensive Approach to Bias Mitigation for Sentiment Analysis of Social Media Data
## 3 Proposed Methodology
### Data Adaptation  
The main task in the data adaptation step was to convert the Jigsaw Unexpected Bias data set, originally designed for toxicity detection, into a format suitable for sentiment analysis. The dataset includes a comment text column containing individual comments and a target column with continuous toxicity scores ranging from 0 to 1, alongside various attributes of the toxicity subtype.  
To better align toxicity scores with a classification scheme based on toxicity levels, we introduced a new categorization that assigns comments to three distinct toxicity levels: _low_, _mild_ and _high_ as shown in Table 2. This categorization directly reflected the degree of toxicity in the comments and made the data set more suitable for sentiment analysis that involved understanding toxicity levels, rather than conventional sentiment categories.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Subset** & **Number of Instances** \\ \hline Total & 1,971,916 \\ Training set & 1,804,874 \\ Validation set & 70,180 \\ Test set & 96,862 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distribution of the Jigsaw Unintended Bias in Toxicity Classification dataset.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Toxicity Range** & **Toxicity Level** \\ \hline
0.0–0.3 & Low (non-toxic) \\
0.3–0.7 & Mild \\
0.7–1.0 & High (toxic) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mapping toxicity scores to toxicity levels.  
Steps for Adaptation  
Load the Jigsaw Unintended Bias dataset into a Panda DataFrame.  
Define the thresholds for toxicity scores to determine toxicity levels. In this study, we set a lower threshold at 0.3 and an upper threshold at 0.7.  
Add a new column called _toxicity_level_ to the DataFrame. Each comment was assigned a label (_low_, _mild_, or _high_) based on its toxicity score.  
Save the updated data set with the _toxicity_level_ column for further modeling development and testing.  
This approach enhanced sentiment analysis by interpreting toxicity as a spectrum of sentiment. Non-toxic comments were classified as _low_, toxic comments as _high_, and those that were neither benign nor harmful were labeled as _mild_. This also ensured that the dataset was in alignment with the toxicity analysis while maintaining the focus on sentiment analysis.
## 9 Discussion of Results
ADC-SMAC is not suitable for controlling idle listening and overhearing problems. ADC-SMAC also does not support mobility. Our proposed BN-MAC is an energy efficient, semi-synchronous, and low-duty-cycle hybrid protocol that is especially designed to support applications in which events occur in different locations. BN-MAC is simulated on different region-based WSNs. Each region is controlled by a BN. BN-MAC does not compel any node to be elected as a BN based on a probability calculation. BN-MAC selects the BN based on the energy level that improves the network lifetime using the one-hop neighbor node with a semi-synchronous mechanism for scheduling at the MAC level. The multiple hops on the path create nonlinearities in the system. The node must wait for the next hop node to wake up. In this manner, the packet is held on every link of the path for different amounts of time [49]. EAR and LDSNS are used to determine the shortest efficient path at the routing level. Thus, there is a small probability of failure for the one-hop path. If the one-hop path fails, then the second best one-hop path is chosen based on the information stored for each one-hop neighbor node. Furthermore, BN-MAC performs localized time slot allocation without changing the time slots of existing nodes. This procedure reduces the latency and overhead and has a small probability of broken links. AAS is an energy-efficient search that reduces the energy consumption because the nodes automatically sense the environment. IDM is another feature implemented in BN-MAC. IDM forces the sensor node to work either in the passive or active mode, depending on the environment. Furthermore, the BN supports mobile environments because the sensor nodes exchange the schedule at the one-hop path but keep the information for two hops. This feature supports scalability. The characteristics of BN-MAC and other hybrid MAC protocols are illustrated in Table 4 that demonstrates the strengths and weakness based on simulation results.
## 3 Materials and Methods
### Design, Implementation, and Installation of a Low-Cost GNSS Monitoring System
#### 3.3.1 Hardware Technology Installed and Calibration
After calibration, the accuracy detected by the laboratory is 0.02\({}^{\circ}\), about 10 times better than what would occur without calibration. After the examination of six \"4030\" accelerometers calibration constant, it was found that the calibration values differ from each other considerably: it is therefore not possible to perform an average calibration for this family of sensors that must be individually calibrated. 5. Temperature probe. It has been installed near the accelerometer, below the ground. Temperature is transmitted together with the accelerometric data to the control center. During the measurements performed in the laboratory, there was no particular relationship between the accelerometric and temperature measurements. Figure 5: **(A)** shows the location on maps of Global Navigation Satellite System GNSS antennas “SAS a” and “SAS b” with latitude-longitude (upper right) and UTM WGS 84 (bottom left) coordinates; **(B)** a picture shows the location of antennas from the courtyard perspective; **(C)** the photo shows a detail of “SAS a” antenna [451401.968 E, 5070726.618 N, 682.378 h–WGS4 32 N]; **(D)** shows a detail of “SAS b” antenna SAS b [451384.123 E, 5070731.990 N, 683.187 h–WGS4 32 N]. The cost of installed instrumentation is about $500 (Table 2). The installation cost is very dependent on the characteristics of the site, its accessibility, and the existing infrastructures (electricity grid, data transmission network). Under normal conditions, the cost is empirically estimated at about $500.
## 5 Simulation and Discussion
### Day-Ahead DRO Model Simulation Analysis
#### 5.2.6. HCNG-CCS-P2G Structure Evaluation  
This section validates the effectiveness of the HCNG-CCS-P2G structure based on the basic hydrogen coupling system (which only includes P2G devices) and sets four system structure scenarios for comparative analysis:  
Scenario A: Only considers P2G-related devices (EL, MR, and HFC), without CCS devices and HCNG.  
Scenario B: Introduces CCS devices based on Scenario A.  
Scenario C: Considers HCNG based on Scenario A.  
Scenario D: Uses the HCNG-CCS-P2G structure for the DRO day-ahead scheduling strategy.  
The scheduling results are presented in Table 8, which illustrates the impact of various scheduling schemes on the system's economic performance and low-carbon capabilities. By comparing Scenario A with Scenario B and Scenario C with Scenario D, it can be observed that after introducing CCS (carbon capture and storage) equipment, carbon emissions decreased by 2.68% and 4.30%, respectively, achieving significant emission reduction effects. Furthermore, the emission reduction effect in the IES is more pronounced. The CCS equipment directly reduces the system's carbon emission by capturing and storing CO\({}_{2}\). However, with the introduction of the CCS equipment, the total cost in Scenario A and Scenario C increased by 0.32% and 0.20%, respectively. These cost increases primarily stem from the operational cost of the CCS equipment and its increased energy demand. Nevertheless, considering the substantial reduction in carbon emissions, these additional costs are justified.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Total Cost** & **Carbon** & **Carbon Trading** & **IDR Cost** & **Purchasing** & **Operation Cost** \\  & **(CNY)** & **Emission (t)** & **Cost (CNY)** & **(CNY)** & **Cost (CNY)** & **(CNY)** \\ \hline SO & 15,215.17 & 11.789 & 271.09 & 918.79 & 12,887.40 & 1126.10 \\ DRO & 15,271.65 & 11.915 & 304.97 & 938.99 & 12,881.18 & 1134.58 \\ RO & 15,750.62 & 11.934 & 320.02 & 988.19 & 13,319.19 & 1111.28 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Evaluation of the DRO method.  
Further comparison between Scenarios A and C, as well as between Scenario B and Scenario D, reveals that with the introduction of HCNG, the CHP system operates more efficiently, resulting in a decrease in total cost of 1.89% and 1.93%, respectively. However, corresponding carbon emissions also increased. This reflects the trade-off between low-carbon performance and economic efficiency. The HCNG-CCS-P2G system combines the economic benefits of HCNG with the effective suppression of carbon emissions through CCS, achieving an ideal integrated outcome.
## 5 DRL-Based Online Resource Allocation
### Mechanism of DRL
#### 5.2.3 Immediate Reward  
To maximize the terminals' long-term energy efficiency with diverse QoS requirements guaranteed, we attach the QoS constraints to the objective of Equation (10) and reconstruct the objective as  
\[O=\sum_{t=0}^{T-1}\left(a_{1}\sum_{k=1}^{K}EE_{k}^{t}+\sum_{k\in\mathcal{U}_{ 1}}G\left(a_{2}\left(C_{k}^{t}-C_{req,k}\right)\right)+\sum_{k\in\mathcal{U}_{ 2}}G\left(a_{3}\left(SINR_{k}^{t}-\gamma_{\text{eff}}\right)\right)+\sum_{k\in \mathcal{U}_{2}}G\left(a_{4}\left(T_{k}^{t}-T_{req,k}\right)\right)\right)\]  
\(G(x)\) is the piece-wise function, whose expression is,  
\[G(x)=\left\{\begin{array}{l}A,x\geqslant 0\\ x,x<0\end{array}\right. \tag{11}\]  
The objective is composed of four parts. The first one corresponds to the terminals' energy efficiency, while the second to the fourth one indicate the penalty of unsatisfied QoS requirement on transmission rate, outage probability, and latency. The purpose of weight \(a_{1}\) is to balance the promotion of revenue and the penalty of QoS unsatisfaction, whereas \(a_{2}\cdots a_{4}\) aim to normalize the penalty parts. This long-term objective can be divided into that of each time stick in the process of online resource allocation, which can be expressed as  
\[O_{t}=a_{1}\sum_{k=1}^{K}EE_{k}^{t}+\sum_{k\in\mathcal{U}_{1}}G\left(a_{2} \left(C_{k}^{t}-C_{req,k}\right)\right)+\sum_{k\in\mathcal{U}_{2}}G\left(a_{3} \left(SINR_{k}^{t}-\gamma_{\text{eff}}\right)\right)+\sum_{k\in\mathcal{U}_{2 }}G\left(a_{4}\left(T_{k}^{t}-T_{req,k}\right)\right) \tag{12}\]  
To present short-term benefits achieved by making a resource allocation decision for a specific arrival terminal service, we intuitively adopt \(\Delta O_{t}=O_{t+1}-O_{t}\) as the immediate reward to inform the agent how much the total goal increases or decreases because of action \(a_{t}\).  
Notice that if the action can't satisfy the QoS requirements of arrival service and existing services, the action will not be carried out, meaning that the resource will not be allocated. The reward of the actions is thus equivalent to  
\[r_{t}=\left\{\begin{array}{ll}a_{1}\Big{(}\sum\limits_{k=1}^{K}EE_{k}^{t+1}- \sum\limits_{k=1}^{K}EE_{k}^{t}\Big{)}&\text{if }a_{t}\in\Phi_{satisfy}^{t}\\ \sum\limits_{k\in\mathcal{U}_{t}}G\Big{(}a_{2}\Big{(}C_{k}^{t}-C_{reqJ}\Big{)} +a_{3}\big{(}SINR_{k}^{t}-\gamma_{\text{eff}}\big{)}+a_{4}\Big{(}T_{k}^{t}-T_ {reqJ}\Big{)}\Big{)}&\text{if }a_{t}\
otin\Phi_{satisfy}^{t}\\ \end{array}\right. \tag{13}\]  
where \(\Phi_{satisfy}^{t}\) denotes the action set which can satisfy the QoS requirement of existing and arrival services at time stick \(t\).