esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Result
### Predictive Modelling of HRV and Stress  
#### 4.3.1 Linear Regression for HRV  
Boxplot visualizations (Figure 8) confirmed the upward trends in LF/HF values across PSS groups, with significant results observed in segment 1 (\(p\) = 0.009), segment 2 (\(p\) = 0.008), and segment 5 (\(p\) = 0.032), although these were uncorrected for multiple comparisons.  
The results of the linear regression model predicting the LF/HF ratio for segment 1 (Table 13) showed that only the PSS had a statistically significant effect (\(p\) = 0.036, \(\beta\) = 0.378), indicating that higher perceived stress levels are linked to increased sympathetic nervous system activity. Age and experience were not significant predictors.  
Similarly, for segment 2 (Table 14), the PSS remained a significant predictor (\(p\) = 0.0009, \(\beta\) = 0.375), with a higher adjusted R\({}^{2}\) (0.357), reinforcing the consistency of the relationship between perceived stress and the LF/HF ratio. In contrast, the regression model predicting  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Variable** & **Coefficient (\(\beta\))** & **Std. Error** & **t-Value** & \(p\)**-Value** \\ \hline Intercept & 1.141 & 6.043 & 0.189 & 0.85211 \\ Age & 0.012 & 0.139 & 0.084 & 0.93418 \\ Experience & \(-\)0.142 & 0.140 & \(-\)1.014 & 0.32285 \\ PSS & 0.378 & 0.126 & 3.007 & 0.0069 ** \\ \hline \end{tabular} R\({}^{2}\) = 0.340, adjusted R\({}^{2}\) = 0.241, F (3, 20) = 3.43, \(p\) = 0.036, Durbin–Watson = 2.551, ** \(p\) < 0.01.  
\end{table}
Table 13: Multiple linear regression results predicting the LF/HF ratio for segment 1.  
Figure 8: Boxplots visualizing the relationship between PSS categories and physiological responses during simulated navigation scenarios. (**Top Left**) Mean RR interval for segment 2, where significant ANOVA differences were observed. (**Top Center**) Overall average of mean RR intervals across all segments. (**Top Right**) Overall average of LF/HF ratios across all segments. (**Bottom**) LF/HF ratios for segments 1, 2, and 5, which exhibited significant group differences.  
the MRR in segment 2 (Table 15) revealed that age was a significant predictor (\(p\) = 0.0255, \(\beta\) = 7.426), while the PSS was not statistically significant.
# Towards an Efficient Chipless RFID System for Modern Applications in IoT Networks
## 3 The RFID Antenna Design
This ensures clutter rejection and low dynamic noise from the hardware modules and environment whenever RFID is interrogated by the antenna's signal. Figure 8: Simulated results for antenna’s co- and crosspolarization characteristics. Figure 7: The total and radiation efficiencies as a function of frequency. The far field radiation pattern of the USRP antenna was investigated for various frequencies, including 2.5, 5, 7.5, and 10 GHz, as shown in Figure 9. From these figures, it is evident that the antenna exhibits an almost omnidirectional radiation pattern in the lower frequency bandwidth while maintaining a good value of gain. The presented results justify the suitability of the USRP antenna for its deployment as a tag's as well as reader's antenna.
# Medium Transmission Map Matters for Learning to Restore Real-World Underwater Images
## 2 Materials and Methods
### Underwater Image Enhancement Subnet  
In the underwater image enhancement subnet, we use the convolution to reduce the resolution of the feature map followed by 11 dilated residual blocks (DRB) [23] to increase the size of the perceptual field out and reduce the resolution. Each DRB has a 3 \(\times\) 3 dilated convolution [24], a ReLU nonlinear function, and another 3 \(\times\) 3 dilated convolution that adds input and output feature maps using skip connections. To avoid gridding issues, we set the dilation ratio of these 11 DRBs as 1, 1, 2, 2, 4, 8, 4, 2, 2, 1 following [25]. Moreover, we adopt the horizontally connected convolution module to add the MT prediction feature to the output feature map, while using the convolution to change the feature map to the size of the MT map and concatenate them together. Finally, we scale the feature map to the size of the input image by convolution.  
Figure 3: Medium transmission guidance module. The MT map \(\overline{T}\) is a feature selector. \(\overline{T}\) weighs the importance of the different spatial positions for \(F\). \(\otimes\) pixel-wise multiplication. \(\oplus\) pixel-wise addition.
## 5 Test Results and Analysis
### Inversion of Undrained Shear Strength Based on CPTu  
Undrained shear strength \(s_{\rm u}\) is a critical design parameter in geotechnical engineering construction, which the existing CPTu cannot directly measure. Presently, the most commonly used means is to interpret it by adjusting the direct measurement parameter \(q_{\rm c}\) of CPTu. Due to the influence of the pore pressure effect and overlying stress, the cone tip resistance must be corrected by the following formula.  
\[q_{\rm t}=q_{\rm c}+u_{2}(1-\alpha) \tag{9}\]  
\[q_{\rm net}=q_{\rm t}-\sigma_{\rm v0} \tag{10}\]  
where \(q_{\rm t}\) is the corrected cone tip resistance; \(q_{\rm c}\) is the measured cone tip resistance; \(u_{2}\) is the pore pressure at the cone shoulder position; \(\alpha\) is the ratio of the cross-sectional area between the probe top column and the cone bottom, the value in this article is 0.8; \(q_{\rm net}\) is the net cone tip resistance; and \(\sigma_{\rm v0}\) is the overlying stress.  
An empirical parameter, cone coefficient \(N_{\rm kt}\), is also needed to interpret undrained shear strength. Scholars worldwide have developed many theoretical circles, including [PERSON] et al. [28]. The following approximate expressions are derived from the analysis:  
\[N_{\rm kt}\approx 3.4+1.6\ln I_{\rm r}-1.9\Delta+1.3\alpha_{\rm c} \tag{11}\]  
Figure 12: NTH method for internal friction angle inversion.  
Figure 13: Comparison of Inversion Results.  
where \(I_{\rm r}\) is the rigidity index with \(I_{\rm r}=G/s_{\rm u}\); \(\Delta=(\sigma_{\rm v0}-\sigma_{\rm h0})/2s_{\rm u}\); and \(a_{\rm c}\) is the friction ratio of the cone-soil interface, which is usually 0.2-0.6, and this paper takes 0.5. By calculation, \(I_{\rm r}=890.2\), \(\Delta=1.164\), and \(N_{\rm kt}=12.71\).  
\[s_{\rm u}=\frac{q_{\rm net}}{N_{\rm kt}}=\frac{q_{\rm t}-\sigma_{\rm v0}}{N_{ \rm kt}} \tag{12}\]  
\[N_{\rm kt}=\frac{q_{\rm net}}{s_{\rm u}} \tag{13}\]  
The recommended \(N_{\rm kt}\) value for the silty clay layer in the Yellow River Delta was obtained by calibrating the static penetration test data \(q_{\rm net}\) and the undrained shear strength obtained from indoor triaxial tests using Formula (13). The comparison with the theoretical value is well-fitted and can be used as an empirical coefficient for the silty clay layer in this region. The Comparison of \(N_{\rm kt}\) inversion results with standard values is shown in Figure 14.
# Numerical Simulation of Seismic-Wave Propagation in Specific Layered Geological Structures
## 4 Methodological Extension
### Numerical Analysis of the Equivalent Explosion Response  
Earthquake depth and magnitude play critical roles when assessing seismic sources. A significant majority, in fact over 90%, of global seismic activities are categorized as shallow earthquakes, characterized by a focal depth less than 70 km [55; 56].  
Figure 11: Pressure distributions in underwater granite and basalt.  
Figure 12: Pressure–time relationship of the 7.5 earthquake in underwater basalt at different distances.  
To delve into the nuances of seismic-wave propagation within a gradient geological structure, a two-dimensional planar model is introduced, as seen in Figure 13. With its hypocenter hosting a load equivalent to a predetermined pressure load, the model spreads 80 km horizontally and dives 6 km in depth. This vertical geology is segmented into a single layer. Pressure is then applied at the designated loading point, facilitating an exploration into seismic-wave propagation patterns across various media.  
Using granite as a representative medium, its attributes include a density of 2.66 g/cm\({}^{3}\), a \(V_{\text{p}}\) of 5.8 km/s, and a \(V_{\text{s}}\) of 3.456 km/s. Figure 14 portrays the pressure-time curve applied at the load point. This curve's characteristics are evocative of the pressure generated by ambient material during a TNT detonation [57]. The curve's inaugural cycle loading boasts a width of 0.05 s, with a zenith of 4560 MPa at the 0.033 s mark. Ensuing wave crests undergo exponential decline, with peaks recorded at 1220 MPa, 830 MPa, and 620 MPa, with their timestamps being 0.079 s, 0.122 s, and 0.170 s, respectively. After this, the pressure gradually stabilizes and starts a slow decline.  
Figure 15 presents the pressure states and their respective fitting curves at various distances from the load point. These distances encompass points 10, 20, 30, 40, 50, 60, and 70 km to the right in the horizontal trajectory. Meanwhile, Figure 16 shows the pressure variances across the seismic-wave propagation process. For these distances, pressure values at 48.21, 24.58, 15.49, 10.80, 7.88, 5.73, and 4.02 MPa, in sequence. The coinciding arrival times span 2.08, 4.12, 6.15, 8.19, 10.22, 12.24, and 14.26 s, with pulse widths distributed across 0.44, 0.41, 0.42, 0.40, 0.37, 0.33, and 0.27 s. Notably, with an increment in distance, stress amplitude displays a brisk decay between 10 to 40 km, but this rate of decay ta  
Figure 14: Loading pressure–time relationship.  
Figure 13: Schematic diagram of the two-dimensional plane model.
## 3 Results
### Risk of Bias  
The analysis of the risk of bias in the included studies reveals significant heterogeneity in methodological quality, with a variable distribution across different evaluated domains. As illustrated in Figure 2, biases were identified in several key categories, including study design, blinding, loss to follow-up, and co-interventions.  
Regarding study design, a substantial proportion of studies exhibited a high risk of bias, as reflected in the predominance of scores of 2. This suggests methodological shortcomings, particularly due to the absence of randomized designs or adequate controls. However, a subset of studies achieved low-risk scores, indicating an appropriate methodological level in certain cases.  
Blinding was another domain with notable deficiencies, with a considerable proportion of studies presenting high risk scores. This limitation is particularly relevant in clinical studies, where assessment bias may influence the interpretation of results. The lack of adequate blinding can lead to an overestimation of intervention effects, thereby compromising the validity of the findings.  
Loss to follow-up emerged as a critical risk factor in several studies, with a heterogeneous distribution of scores. While some studies demonstrated adequate management of missing data, others exhibited a high risk of bias due to elevated dropout rates without appropriate imputation strategies.  
Finally, in the category of co-interventions, a high proportion of studies received high risk scores. This factor may impact result interpretation by introducing uncontrolled variability in the application of interventions. The presence of undeclared or poorly documented co-interventions represents a significant limitation for the comparability of the included studies.  
Overall, the identified risk of bias in the included studies limits the robustness of the synthesized evidence. The high methodological heterogeneity underscores the need for future studies with more rigorous designs, incorporating adequate blinding strategies, strict control of follow-up losses, and greater transparency in co-intervention documentation. These aspects will be fundamental in improving the internal and external validity of research in this field.
# Toward a Modern Last-Mile Delivery: Consequences and Obstacles of Intelligent Technology
## 1 Introduction
Alternatively, as another example, it is argued that when logistics providers could improve customer service through access to real-time data via modern technology, reducing disputes and making it easier to identify incidents through network optimization [46]. As a result, the goal of this literature review is to frame and maximize the best modern technologies in LMD. When researching this article, the authors formulated a search in the Scopus database as: (TITLE-ABS-KEY (\"last-mile delivery\" OR \"last-mile delivery\" OR \"last-mile logistics\" OR \"last mile logistics\") AND TITLE-ABS-KEY (\"Technology\" OR \"technologies\" OR \"Artificial intelligence\")). The authors conducted a Scopus database search for this purpose: based on the outcome, and despite the importance of the topic, only the last five years have observed motivating trends in scholarly interest. Table 1 shows the most recent journal publications as a showcase, but none of the previous attempts have covered the goal of this article. Thus, this article is expected to fill an existing gap in the literature; however, due to a lack of classified archived literature, the authors chose to write a narrative literature review to achieve their research goal. \begin{table}
\begin{tabular}{c c} \hline \hline
**Authorship** & **Article Title** \\ \hline
[47] & Enhancement of E-commerce service by designing last mile delivery platform. \\ \hline
[48] & Ground vehicle and UAV collaborative routing and scheduling for humanitarian logistics using random walk-based ant colony optimization \\ \hline
[49] & Introducing the benefits of autonomous vehicles to logistics during the COVID-19 era \\ \hline
[50] & Optimization and machine learning applied to last-mile logistics: A review \\ \hline
[51] & From traditional warehouses to physical internet hubs: A digital twin-based inbound synchronization framework for PI-order management \\ \hline
[52] & An active-learning pareto evolutionary algorithm for parcel locker network design considering accessibility of customers \\ \hline
[53] & Drone routing problem model for last-mile delivery using the public transportation capacity as moving charging stations \\ \hline
[54] & How will last-mile delivery be shaped in 2040? A delphi-based scenario study \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Published articles in 2022. The rest of this report is divided into three sections. The first defines the modern LMD. The second presents applications of modern LMD. Following that, the challenges of modern LMD are regarded. Finally, a conclusion is supplied.
# Detecting Malicious Devices in IPSEC Traffic with IPv4 Steganography
## 3 IPsec-Based Network Steganography
### IPsec Overview  
The full name of IPsec is Internet Protocol Security. The \"IP\" part indicates the data's destination, while the \"sec\" part is responsible for their encryption and authentication. In other words, IPsec is a set of protocols that establish a secure and encrypted connection between devices on the public internet.  
The protocols within the IPsec set are typically grouped according to their tasks [56]:  
* Authentication Header (AH)--The AH protocol ensures data integrity, data origin authentication, and optional replay protection. It achieves this through a message digest generated by algorithms like HMAC-MD5 or HMAC-SHA and a shared secret key. Replay protection is implemented via a sequence number field in the AH header. AH authenticates IP headers and payloads, excluding specific transit-permissible fields like time-to-live (TTL).  
Figure 4: Relationships between qualitative measures.  
* Encapsulating Security Payload (ESP)--The main idea of this protocol is to provide data confidentiality (encryption) and authentication (data integrity, data origin authentication, and replay protection). ESP can operate with confidentiality only, authentication only, or both. In authentication mode, ESP employs algorithms akin to AH but with distinct coverage. While AH-style authentication encompasses the entire IP packet, including the outer IP header, ESP's mechanism authenticates solely the IP datagram portion of the packet.
* Security Association (SA) manages establishing, maintaining, and terminating secure connections between network entities. It is crucial to define and agree upon security parameters to ensure a consistent and secure channel for data exchange. IPsec also allows two modes of work:
* Transport mode--encrypts the outgoing data but does not conceal information about where it is intended to go. This means that a hacker may not be able to decipher intercepted messages but would know when and where they were sent.
* Tunnel mode--establishes a secure connection between two devices on the internet, creating a fully private link. In this mode, an IPsec-based virtual private network (VPN) operates as it forms a VPN tunnel.  
Figure 5 illustrates the packet structures for AH and ESP in both transport and tunnel modes. Combining ESP and AH is also permitted under the IPsec standards. However, this setup does not significantly improve security [57].
# Internet of Things Geosensor Network for Cost-Effective Landslide Early Warning Systems
## 4 Data Processing
### Data Analysis  
The data analysis methodology being developed for the Inform@Risk LEWS is based on a combination of dynamic thresholds for trigger- and deformation observations, sensor fusion methods and pattern recognition methods. As the geosensor network is designed for densely populated areas, accidental or intentional tampering with the sensors has to be taken into account. Otherwise, a large number of false alarms would be the consequence--which, even if reviewed by an expert before issuance of a notification, would most likely make an effective operation of the system impossible. For this reason we plan to apply sensor fusion and pattern recognition techniques to filter the incoming data and separate the significant from the insignificant data. For example, when a threshold is surpassed on a single inclination sensor, the system checks if any of the surrounding sensor nodes have also registered a deformation increase. It also checks if any of the sensors monitoring the triggering factors (rainfall, groundwater, seismic events etc.) show an increased level. Depending on the severity and degree of confidence of the observation, either the event is temporarily ignored or appropriate action is taken, which ranges from marking the event for review through the system manager to issuing an alarm. In any case, the system will instruct the relevant sensors to increase the measurement interval and as further data of the event is collected throughout time, the system will reevaluate the event utilizing pattern recognition methods on the time series of the relevant deformation and triggering datasets. This cycle is continued until the event is identified as an outside influence, or--if the situation remains uncertain--is reviewed and classified by the system manager. With time, the system will collect a database of classified events, which can be reviewed periodically and serve as training data for the pattern recognition algorithms. The thresholds used for the different sensor types at the different locations are first determined based on sensitivity analyses performed using the geological/geomechanical models created during the hazard analysis and include absolute values as well as rates where applicable (e.g., deformation rate). Later with increasing length of observation the thresholds are adapted using the results from timeseries-analyses performed on the collected data. This also includes trigger informations, especially for rainfall, one of the most important trigger factors. The data analysis methodology for the Inform@Risk LEWS outlined above is currently still in development. Its functionality will be evaluated and improved during a planned \"learning-phase\" of the LEWS of at least one year.
# Information Fusion for Multi-Source Material Data: Progress and Challenges
## 4 Future Research Challenges
### Optimizing Material Data Fusion Techniques  
There are many sources of material data such as experiment, calculation, production and literature data. In addition, many data formats of material data exist, including structured, semi-structured and unstructured data, along with various combinations of columns. The current data fusion technologies not only lose a lot of useful information, but also cannot combine the domain knowledge to eliminate erroneous data when solving data conflicts. Therefore, a comprehensive consideration of data freshness, data sources and related expertise in data fusion will be a major research direction in the future.  
Basically, the optimization of material data fusion technologies is supposed to focus on the following aspects:  
1. Optimization based upon deep learning models. How to choose the appropriate machine-learning model and choose the appropriate parameters are essential for the fusion result. Deep learning primarily uses neural networks, which include supervised neural networks and no/semi-supervised learning. Supervised neural networks include deep neural networks, recurrent neural networks and convolutional neural networks. Unsupervised pre-training networks include Boltzmann machines, restricted Boltzmann machines, deep belief networks, generative adversarial networks and self-encoders. The choosing of a model not only affects the validity of the results, but also the cost of training.  
2. Optimizing the algorithms used in data fusion. This includes algorithms such as weighted average, Kalman filter [PERSON] (1968), Bayesian estimation, statistical decision, evidence theory, fuzzy theory, neural network and production rules. These techniques do not have a best algorithm that is obviously better than the others on solving conflicts between multiple data sources, so the combing results of various algorithms can be considered in the future [69]. In addition, the accuracy of the data source corresponds to the weight of the data source in data fusion, which has a huge impact on the result of the fusion algorithm. In the future, it is necessary to optimize the estimation of the accuracy of the data source, and if necessary, the semi-manual method can be used.  
3. Optimization from the perspective of knowledge discovery and knowledge use. In the whole process of data fusion, it is necessary to process and fuse the data using the knowledge of the material field and the generated rules. After the fusion results are obtained, knowledge extraction can be performed by means of machine learning and knowledge graph. In the future, we should consider adding the new knowledge we have learned to the original knowledge set, so as to get a positive feedback, and gradually improve the performance and effect of data fusion.