esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
1. The water environment has a great influence on the shape of the push-shear rupture surface of carbonaceous mudstone, and the rupture surface under the natural water content conditions approximates to an inverse S-shaped curve, while the recommended rupture surface after saturated water immersion is closer to a straight line or a circular arc.
2. The push-shear deformation of carbonaceous mudstone in the natural state shows a typical full stress-strain curve characteristic, and the whole process roughly covers four stages: linear elastic deformation, yield deformation, strengthening, and local deformation. After saturated water immersion, the deformation of carbonaceous mudstone is mainly in the elastic-plastic state, and the push-shear force grows slowly, and when the specimen is damaged, it enters into the plastic deformation stage, and at this time, the push-shear force is only slightly reduced.
3. The moisture content has a significant impact on the shear strength of carbonaceous mudstone. After soaking in saturated water, the average cohesion of carbonaceous mudstone is 33% lower than under natural conditions, and the internal friction angle is 15% lower. Therefore, after soaking in saturated water, the fracture surface, shear deformation curve, and c and \(\varphi\) values of carbonaceous mudstone will be greatly affected. In engineering practice, they must be fully valued and relevant waterproof and drainage measures must be taken.
4. Through numerical simulations of carbonaceous mudstone high slopes before and after rainfall, it is further confirmed that rainfall has a significant impact on the stability of slopes and requires timely maintenance and treatment.  
Conceptualization, R.N.; methodology, R.N.; formal analysis, H.C.; writing--original draft, J.Z.; writing--review and editing, H.C. and J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (No. 52278436); Hunan Provincial Transportation Science and Technology Project (No. 202201); Hunan Provincial Transportation Science and Technology Project (No. 202219); Science and technology development project of Guizhou Road and Bridge Group Co., Ltd. (HL01-KY-01).  
Not applicable.  
Figure 21: Plastic strain contour map of slope after immersion in saturated water and support.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare that this study received funding from Guizhou Road and Bridge Group Co., Ltd. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.
# Analysis of Development Coordination Levels between Skywalk Systems and Urban Spatial Environments
## 3 Materials and Methods
### Cases

Table 2: Indicators for evaluating the consistency between skywalks and urban environmental construction. of the global economy, and the degree of urbanization is also relatively high. This leads to cities in these regions experiencing a greater demand, and potential, for the construction of skywalk systems. Secondly, from the perspective of the urban built environment, North America and Asia differ greatly. North American cities are characterized by well-planned, spacious roads and a high greening rate, while Asian cities are characterized by high density, vertical development, and busy traffic. This contrast reflects the varied application of skywalk systems. Therefore, selecting North America and Asia as the research focus can not only reflect the global development trends of skywalk systems but also illustrate the effects of these system when applied in different environments and cultural contexts. Eighty representative or typical cases of skywalk systems from North America and Asia were selected for this study (Figure 4). This includes 47 cases in North America and 33 in Asia. Specifically, they include America's Minneapolis, St. Paul, Duluth, St. Cloud, Rochester, Cincinnati, Cleveland, Toledo, Akron, Des Moines, Cedar Rapids, Sioux City, Milwaukee, Dallas, Houston, Fort Worth, Rochester, New York, Buffalo, Syracuse, Boston, Atlanta, Charlotte, Chicago, Pittsburgh, Spokane, Seattle, Oklahoma City, Detroit, Grand Rapids, Tampa, San Diego, Fargo, Indianapolis, Louisville, Kansas City, Las Vegas, Lincoln. Canada's Toronto, Hamilton, Kitchener, Oshawa, Calgary, Edmonton, Winnipeg, Vancouver, and Halifax; China's Hong Kong, China, Taipei, China, Beijing, Macao, China, Shanghai, Hangzhou, Shenzhen, and Guangzhou; Japan's Osaka, Chiba Makuhari, Sapporo, Tokyo, Yokohama, and Kobe; Mumbai in India; Singapore; Bandung in Indonesia; and Bangkok in Thailand.
## 5 Experiment and Evaluation
### Discussion on Detection Performance  
In this paper, we consider attacks on network devices with more specific goals than any other kinds of attacks. That is, an attacker aims to implant a permanent backdoor into the network device by means of remote code execution attacks for the purpose of getting a persistent infiltration channel to the inside network. In fact, our consideration is based on analysis on recently unveiled attacks dubbed SYNful Knock [10], which targets Cisco devices. Such kind of attacks certainly affect control flow of the infected device, thus inherently leave footprints in the target's memory. Besides, the backdoor should remain implanted in the device for a long time according to the attacker's goal. These suggest that after the attack occurs, the internal state (i.e., integrity of the memory) of the infected device as well as its emulated VM remains inconsistent with the original benign state for a sufficient time to be detected by our proposed system. In this regard, we believe that it is enough with 2 s to detect such kind of attacks.  
On the other hand, the attacker may try to disable the detection system after successfully taking control of the target device and before being alarmed by the system. However, the detection system is configured to investigate mirrored traffic without being connected to the network. It is infeasible for the attacker to turn off the alarm since there is no way to access the detection system over the network.
## 4 Societal Benefits and Impact
### Contributions to Smart City and Community Initiatives
#### 4.3.2 Data-Informed Urban Governance and Planning  
At the policymaking level, IoPVT data can inform long-term strategies by giving city officials and planners access to abundant valuable insights. Cities often struggle to identify priority areas for infrastructure investment, particularly when competing needs balance limited resources. Our system provides evidence-based metrics that can guide these decisions from multiple points of view.  
By mapping accident frequencies, near-misses, and environmental hazards across urban spaces, planners can identify high-risk zones that warrant immediate attention. For example, analyzing where pedestrians gather, identifying accident hotspots, and monitoring localized health risks among communities would all inform and improve the design of infrastructure. This geospatial intelligence allows for targeted interventions rather than blanket approaches.  
The continuous monitoring of infrastructure usage patterns and environmental impacts can guide both maintenance schedules and redesign priorities. Planners could prioritize upgrading dangerous staircases, using more durable construction materials in areas vulnerable to climate impacts and adding adaptive lighting or signage in areas with poor visibility. For instance, if certain pedestrian crossings consistently show risky behavior patterns despite signage, the system could suggest alternative designs based on successful implementations elsewhere.  
The 24/7 monitoring capability provides insights into how urban safety risks evolve throughout daily cycles, seasonal changes, and in response to events or festivals. This temporal dimension enables dynamic resource allocation and adaptive management strategies that conventional periodic audits cannot achieve.  
The DT and Metaverse components of our framework allow urban planners to simulate proposed changes before physical implementation. This capability enables virtual A/B testing of infrastructure modifications, such as evaluating multiple staircase designs for slip resistance during simulated winter conditions or assessing how redesigned public spaces might affect pedestrian flow during emergency evacuations.  
As time goes on, with these data-driven interventions layering on top of each other, cities become safer, healthier, and more responsive to the changing needs of their people. The cumulative effect is a more resilient urban environment that proactively adapts to evolving challenges rather than reacting to failures.
# An Analysis of Rock Bolt Dynamic Responses to Evaluate the Anchoring Degree of Fixation
## 2 Materials and Methods
### Geometry  
The geometry of the system was simulated using a 3D sketch (Figure 11) of a rock bolt 0.02 m in diameter and 1.4 m in length and an anchor/bearing plate located 0.2 m from the top external head of the rod. The rod was simulated as a perfect cylinder with a flat external surface; this did not properly simulate the coupling of the true rugged surface of the real bolt, so we accounted for this in the boundary conditions. The grouting was simulated as another cylinder 0.1 m in diameter and 1.2 m in length.
# LoRA-Tuned Multimodal RAG System for Technical Manual QA: A Case Study on Hyundai Staria
## 6 Discussion
### Considerations for Military Maintenance Manual Application  
For application to military maintenance manuals, the following three considerations exist:  
1. Security systems based on user authentication, access control, and audit logs are essential for protecting the confidentiality of military equipment information.
2. Maintenance manual data structure and metadata consistency conforming to international standards such as NATO STANAG 4677 must be secured, and compatibility design considering interoperability among multinational equipment is necessary.
3. Lightweight models or offline mode support capable of operation in unstable network environments are needed considering field deployment possibilities.
## 3 Experimental Investigations
### Impact Localization Results  
A near-field impact at the point (200 mm, 90\({}^{\circ}\)) is chosen as a typical case to be analyzed first. The impact signals extracted from each sensor of each IMF are shown in Figure 4. From Figure 4, the wave fronts cannot been seen in the high frequency region, including IMF1, IMF2, and IMF3.  
Figure 3: Experiment setup, ULSA arrangement, and impact applied positions (mm).  
Additionally, much more noise signal, boundary reflection, and pattern aliasing appear in the IMF5, IMF6, IMF7, and IMF8. Only the wave fronts of impact signals can be easily found in the IMF4.  
All the IMFs extracted from the impact signals are used to form the input vector of the near-field 2D-MUSIC model. The measured average velocity on this plate is 1154 m/s [11]. According to the structure dimension, the scanned area is set to be the distance from 0 to 450 mm and the direction from 0\({}^{\circ}\) to 180\({}^{\circ}\), and the scanning step length of the distance and direction is 1 mm and 1\({}^{\circ}\), respectively. The near-field impact obtained at the point (200 mm, 90\({}^{\circ}\)) spatial spectrum \(\mathbf{P}_{\text{MUSIC}}(r,\theta)\) is shown in Figure 5. The figure represents the spatial spectrum magnitudes of each scanned point \((r,\theta)\), and the highest pixel point of the figure represents the impact point localized by the presented 2D-MUSIC algorithm. Seen from Figure 5, the spatial spectrum of IMF4 located the impact source, and the direction and distance error are 0\({}^{\circ}\) and 0.4 cm, respectively.  
Ten estimated results of impact points and the errors compared with actual impact points are listed in Table 1, including seven located results at the spatial spectrum of IMF4, and three located results at the spatial spectrum of IMF5. It shows that ten near-field impacts are in good agreement with the actual impacts. The maximum error is at the impact position (255 mm, 150\({}^{\circ}\)), whose direction and distance error are 3\({}^{\circ}\)and 2 cm, respectively.  
Figure 4: The IMFs using the optimized EEMD to decompose the impact array signals.  
Figure 5: The spatial spectrum of each IMF estimated by the 2D-MUSIC model.
# Hierarchical Transformer Network for Utterance-Level Emotion Recognition
## 3 Approach
### HiTransformer: Hierarchical Transformer
#### 3.2.1 Individual Utterance Embedding  
For the input utterance \(u_{j}=\{w_{k}\}_{k=1}^{M_{j}}\), where \(u_{j}\) is the \(j-th\). utterance in \(D_{i}\) and \(M_{j}\) is the number of words in the utterance \(u_{j}\). First, the utterance \(u_{j}\) is in lower-case and is tokenized according to a byte pair encoding (BPE) algorithm. If there are tokens exceeding the preset maximum length of input tokens, those tokens are excluded from the list, which may cause information loss and affects the accuracy of the results. Then, we embed those tokens through WordPiece embeddings [28] and obtain the token embeddings \(e=\{e_{k}\}_{k=1}^{M_{j}}\). Finally, the input embeddings \(E=\{E_{k}\}_{k=1}^{M_{j}}\) are the summation of the token embeddings \(e\) and the positional embeddings \(p=\{p_{k}\}_{k=1}^{M_{j}}\), which are obtained using the approach used by BERT [9]:  
\[E_{k}=e_{k}\,\odot\,p_{k}\,\left(k\epsilon[1,M_{j}]\right) \tag{1}\]  
where \(\odot\) denotes element-wise addition.  
We feed the input embeddings \(E\) into the lower-level transformer to learn the individual utterance embedding. We adopt the transformer-based pretrained language model BERT (illustrated in Figure 4) as the lower-level transformer, which is designed to pretrain deep bidirectional representations from unlabeled text by jointly conditioning both the left and right contexts in all layers. The detailed structure is shown in Figure 4. The language model converts input embeddings \(E\) into contextual word embedding \(T=\{T_{k}\}_{k=1}^{M_{j}}\).  
\[T=BERT(E) \tag{2}\]  
Figure 4: Structure of BERT.  
The individual utterance embedding is then obtained by max-pooling of the contextual word embeddings within an utterance, which can assist in retaining important information in each dimension:  
\[f(u_{j})=maxpool(T) \tag{3}\]
# Ka-Band Doppler Scatterometry: A Strong Wind Case Study
## 2 Data and Methods  
### Experiment Site  
The measurements presented in this study were taken from the Black Sea research platform (Figure 1d) during 24-25 October 2018. This platform is located seaward of the southern slope of the Crimean mountains, approximately 600 m offshore. In cold seasons, northerly katabatic drainage (bora) winds periodically develop, carrying high-density cold air from higher elevations over the Crimean Mountain plateau down its southern slope. At the platform location, local bora winds are topographically amplified in a coastal mountain canyon that acts as a tunnel (Figure 1b).  
Such harsh conditions were observed during the experiment days. A Sentinel-1A Synthetic Aperture Radar image (SAR) taken at 03:57 UTC on 25 October 2018 (Figure 1a) clearly indicates wind intensification (increased cross-section) over the coastal area, even in the anticipated mountain shadow zone. As typical bora wind is highly temporally variable and blows in gusts, a sample instantaneous imagery may not capture the peak of the wind event at each particular location. In fact, Figure 1a does not capture a gap-like wind jet over the platform, in contrast to numerous jets captured west of the platform. Based on our measurements, wind speed at the platform decreased to \(U\approx 20\) m s\({}^{-1}\) during this Sentinel-1A overpass, but experienced periodical amplifications up to about 30 m s\({}^{-1}\) in approximately 10 min cycles. At the platform location, the fetch of offshore wind-generated waves is about \(X\approx 800\) m. In our experience, for such short fetches and strong winds (young waves with dimensionless fetch, \(x=gX/U^{2}\approx 10\)), spray clouds start developing over the surface when wind speed reaches 20-25 m s\({}^{-1}\) (Figure 1c).
## 2 System and Channel Models  
Figure 1 illustrates a UOWC link between two autonomous underwater vehicles (AUV). In a practical underwater scenario, the transmitted beam is influenced by the underwater particles in terms of absorption and scattering, as well as air bubbles, which are generated by the cavitation effect of AUV propellers, marine flora and fauna respiration, and oxygen systems of scuba divers. A comprehensive model that contemplates the relation of these impairments in realistic scenarios is essential for accurately characterizing the UOWC channel and assessing the performance of UOWC systems. The following section presents the system and channel models adopted in this work, considering the environmental conditions prevalent in underwater settings from experimental measurements presented in [22].
## 3 Results
### Nutraceutical Parameters  
The breads baked with different percentages of flaxseed cake (5.0%, 7.5%, 10.0%) and the control were analyzed with the same assays used for the characterization of the flaxseed cake and flour, and the obtained results are reported in Table 5. The nutraceutical value, attributed to baked bread in terms of both total phenols and flavonoids, significantly increased (\(p<0.001\)) with the growing percentage of flaxseed cake added to the flour mix. The same trend was observed for the antioxidant power (Table 5). In relation to fatty acids composition, saturated fatty acids (SFA) were already significantly decreased at 5.0% of fortification with flaxseed cake addition, remaining consistent thereafter. At the same time, an increase in the percentage of both monounsaturated fatty acids (MUFAs) and polyunsaturated fatty acids (PUFAs) n-3 were observed. This is in accordance with the profile of individual fatty acids since the breads obtained with the addition of flaxseed cake also contained higher levels of n-3 alpha-linolenic acid in comparison with the control. Accordingly, the n-6/n-3 ratio significantly decreased from the control to the different amounts of cake addition, reaching the lowest value at the highest cake fortification amount (Table 6). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Samples (Cooked Breads)**} \\ \cline{3-6}  & \(p\)**-Value \({}^{1}\)** & **Bread 1 \({}^{2}\)** & **Bread 2** & **Bread 3** & **Bread 4** \\ \hline Water activity (aw) & ns & 0.956 \({}^{\text{a}}\) & 0.957 \({}^{\text{a}}\) & 0.955 \({}^{\text{a}}\) & 0.956 \({}^{\text{a}}\) \\ \% of Dry matter (\% dm) & ns & 56.9 \({}^{\text{a}}\) & 55.7 \({}^{\text{a}}\) & 55.4 \({}^{\text{a}}\) & 57.9 \({}^{\text{a}}\) \\ Free acidity (acidity & *** & 9.12 \({}^{\text{a}}\) & 11.20 \({}^{\text{b}}\) & 12.31 \({}^{\text{c}}\) & 14.31 \({}^{\text{d}}\) \\ degrees) & & & & & \\ C16:0 & ns & 15.22 \({}^{\text{a}}\) & 11.05 \({}^{\text{a}}\) & 15.26 \({}^{\text{a}}\) & 11.84 \({}^{\text{a}}\) \\ C18:1t9 & ** & 0.48 \({}^{\text{a}}\) & 0.57 \({}^{\text{a}}\) & 0.82 \({}^{\text{a}}\) & 3.28 \({}^{\text{b}}\) \\ C18:0 & ns & 1.01 \({}^{\text{a}}\) & 0.54 \({}^{\text{a}}\) & 1.33 \({}^{\text{a}}\) & 1.12 \({}^{\text{a}}\) \\ C18:1c9 & ns & 8.80 \({}^{\text{a}}\) & 8.88 \({}^{\text{a}}\) & 13.37 \({}^{\text{a}}\) & 12.44 \({}^{\text{a}}\) \\ C18:2n-6 & ** & 62.50 \({}^{\text{d}}\) & 56.44 \({}^{\text{c}}\) & 47.12 \({}^{\text{b}}\) & 44.32 \({}^{\text{a}}\) \\ C18:3n-3 & *** & 3.67 \({}^{\text{a}}\) & 19.07 \({}^{\text{b}}\) & 17.35 \({}^{\text{b}}\) & 23.98 \({}^{\text{b}}\) \\ \hline \hline \end{tabular} Notes: \({}^{1}\) Significance level \({}^{***}\)\(p<0.001\), ** \(p<0.01\); ns: not significant (\(p>0.05\)). In the same row, different letters indicate significant differences among samples. \({}^{2}\) For bread formulation, see Table 2.  
\end{table} 
Table 4: Physical and chemical characterization of cooked breads: water activity (aw), dry matter (dm %), free acidity, most representative fatty acids (relative %). Data presented are the mean of three replicates.
# Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges
## 3 Datasets, Labelling, and Augmentation
### Radar Datasets
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Name** & **Year** & **Task** & **Radar Type** & **Data** & **Doppler** & **Range** & **Other Sensors** & **Scenarios** & **Wother** & **Aantions** & **Size** \\ \hline \multicolumn{10}{c}{**Radar Datasets for Detection**} \\ \hline mc/somes [44] & 2020 & DT & LR & PC & & SV & CLO & USH & ✓ & 3 DT & L \\ Pistol [53] & 2021 & DT & LR & PC & ✓ & MR & CLO & USP & ✓ & 3 DT & M \\ RadarScenes [56] & 2021 & DTS & HR & PC & ✓ & SV & CO & USHT & ✓ & P\({}_{c}\) & L \\ Pontillum [7] & 2020 & D & 2 LR & PC & ✓ & MR & CL & U & ✓ & 3D & M \\ Zander [51] & 2020 & D & SAR & ADC,RDC & ✓ & MR & CLO & U & ✗ & P\({}_{c}\) & S \\ Dense [30] & 2020 & D & LR & PC & ✓ & LR & CLO & USHT & ✓ & 3D & L \\ RADATE [44] & 2020 & LDT & SP & RA & ✗ & SV & CLO & USHT & ✓ & 2 DT,P, & M \\ \hline \multicolumn{10}{c}{**Radar Pre-CRAR Datasets for Detection**} \\ \hline CARRADA [45] & 2020 & DTS & LR & RAD & ✓ & SR & C & R & ✗ & 2 DT,P\({}_{c}\),M & M \\ RADATE [46] & 2021 & D & LR & RAD & ✓ & SR & C & USH & ✗ & 2 DT & M \\ CRUN [47] & 2021 & D & LR & RAD & ✓ & USR & C & USHT & ✗ & P\({}_{c}\) & L \\ RADICAL [48] & 2021 & L & LR & ADC & ✓ & USR,SR & C\({}_{c}\) & USHT & ✗ & 2 DT & L \\ Ghent Velt [49] & 2020 & D5 & LR & RAD & SR & CL & U & ✗ & M & M \\ \hline \multicolumn{10}{c}{**4D Radar Datasets for Detection**} \\ \hline \hline
**Adv-1** & 2019 & D & HR & PC & ✓ & MR & CL & SH & ✗ & 3D & S \\ View-of-Diff [50] & 2022 & DT & HR & PC & ✓ & SR & CLO & U & ✗ & 3 DT & S \\ RADATE [51] & 2021 & DS & HR & ADC,RADC & ✓ & MR & CLO & USH & ✗ & P\({}_{c}\),M & M \\ T4 DReNet [52] & 2022 & DT & HR & PC & ✓ & LR & CLO & U & ✗ & 3 DT & M \\ \hline \multicolumn{10}{c}{**Radar Datasets for Localization**} \\ \hline \hline
**Oxford** [53] & 2020 & L & SP & RA & ✗ & SV & CLO & U & ✓ & P\({}_{c}\) & L \\ Molman [54] & 2020 & L & SP & RA & ✗ & SV & LO & US & ✗ & P\({}_{c}\) & M \\ Browna [53] & 2022 & LD & SP & RA & ✗ & SV & CLO & S & ✓ & P\({}_{c}\),3D & L \\ EU Long-term [56] & 2020 & L & LR & PC & ✓ & LR & CLO & U & ✓ & P\({}_{c}\) & M \\ [PERSON] [57] & 2021 & L & LR & PC & ✓ & SLR & LO & S & ✗ & P\({}_{c}\) & M \\ CookMark [58] & 2021 & L & LR & ADC,FC & ✓ & USR & LO & ST & ✓ & P\({}_{c}\) & M \\ \hline \multicolumn{10}{c}{**Radar Datasets for Other Tasks**} \\ \hline PREVENTION [59] & 2019 & DT & LR & PC & ✓ & IL,RSE & CLO & UH & ✓ & 2 DT & L \\ SCORP [60] & 2020 & S & LR & ADC,RAD & ✓ & USR & C & P & M & S \\ Ghost [61] & 2021 & DS & LR & PC & ✗ & LR & CLO & S & ✗ & P\({}_{c}\) & M \\ \hline \multicolumn{10}{c}{**Radar Datasets for Localization**} \\ \hline \hline