esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Anatomical and Technical Model Generation
Due to the limited CT scan resolution, the cancellous bone region was not modelled based on segmentation. Instead, we modelled it as a generically shaped object (see Figure 1b) directly inside the geometry preprocessor of the simulation software COMSOL Multiphysics(r) version 5.3a (COMSOL; COMSOL Inc., Stockholm, Sweden. [[https://www.comsol.com/](https://www.comsol.com/)]([https://www.comsol.com/](https://www.comsol.com/))). Apart from that, the cancellous bone layer was neglected in bone parts far away from the so-called region of interest (ROI). The ROI is defined as the volume inside the defect domain, highlighted in red in Figure 1b. Due to technical reasons, the geometry file of the anatomical model (consisting of the mandible and the surrounding soft tissue) and the cancellous bone firstly had to be imported into COMSOL, secondly exported as a COMSOL-internal geometry file type._mphbin_, and could only then be reimported and further modified inside the simulation software. The critical size defect in the angular region of the mandible, measuring 35 mm in width and ca. 20 mm in height, was modelled within COMSOL by subtracting a cuboid from the cortical bone geometry. The defect is supposed to be filled with cancellous bone from another part of the body and will be equipped with growth factors in the in vivo experiment. Finally, so-called virtual operations were used to simplify the topological structure of the geometry, namely forming composite domains and composite faces. This allows for an easier and more regular finite-element mesh generation. In the current study, the technical modelling of the implant design and positioning was performed directly in the geometry module of COMSOL. The technical model only comprises the electro-stimulating implant, whereas a reconstruction plate is not modelled geometrically to reduce the problem size. Instead, a boundary condition (see Section 2.4) mimics the stabilizing Ti6 Al4V mesh tray around the defect domain that is supposed to hold the filling material in place. It will be 3D printed for the in vivo experiments. The proposed cylindrical electro-stimulating implant is 55 mm long and designed in a bipolar manner, see the parametrized implant geometry in Figure 1c: Two electrodes (stimulating electrode \"Electrode 1\" and counter-electrode \"Electrode 2\") of length \(h_{\text{el}}\) are separated by an insulator of length 55 mm--2\(h_{\text{el}}\). This geometry was chosen due to comparably easy manufacturing and very regular field distribution around the electrodes. Because the mandible's thickness is only around 5 mm in its posterior region, here the last 10 mm of the electrode are realized as a thin fixation pin of 1 mm in diameter. The diameter of the remaining implant is 4 mm and it is positioned approximately in the center of the defect. The electrode length \(h_{\text{el}}\) is a fundamental parameter influencing the electric field distribution and hence the regeneration success. Therefore, this parameter will be optimized to aim for the most beneficial electric field distribution (see Section 2.5).
## 4 Topology-Based Routing Protocols in FANETs
### Proactive Routing Protocol
The addition of location information enhances the link quality extension by allowing for rapid analysis of the network's link breakage characteristic. Results were acquired using a MAC-layer emulator that incorporates a flight simulator to mimic flight conditions accurately. These numerical findings demonstrate that predictive-OLSR surpasses OLSR and BABEL in communication reliability, even under highly dynamic settings [93]. QTAR is a new Q-learning-based routing protocol for FANETs that enables efficient source-destination combinations in highly dynamic situations. By considering two-hop neighbor nodes in the routing decision, the QTAR protocol enhances the local view of the network topology. QTAR adaptively updates the routing decision based on the network situation using the Q-learning technique. GPS signals can be used to determine the location of UAV nodes in QTAR. The geographic routing strategy based on two hops enhances routing performance. According to simulation results, QTAR outperforms conventional routing protocols across a range of performance measures in various scenarios [94]. OSLR is a routing protocol that is widely used in ad hoc networks. The most critical factors affecting the performance of OLSR are contained within multipoint relay (MPR) nodes. The sender node's function is to select the MPR node, covering two-hop neighbors. Nodes in UAV networks frequently change their location and interconnection link. MPR is a critical feature in OLSR for reducing control messages. MPR nodes are a subset of nodes that are tasked with the responsibility of forwarding link-state updates. This optimization to a pure link-state routing protocol is advantageous in extremely dense network environments, where the MPR technique is optimal [95].
# Forensic Analysis on Damage of a Cable-Stayed Bridge without Backstays Using BIM Information
## 3 Time-Dependent FE Modeling
### FE Model  
#### 3.2.1 Elements and Materials  
Extracting the key points, geometry shape, and spatial coordinates directly from the BIM model, the parametric FE model is established in the ANSYS software based on APDL [31; 32], as shown in Figure 10.  
To improve the calculation efficiency, the pylon is modeled by a combination of different elements. The main girders of the side span were simulated by Shell 281 elements, the main girders of the middle span were simulated by Beam 188 elements, and the stayed cables were simulated by Link 180 elements. In order to obtain more accurate calculation results, the cracking regions of the pylon were modeled using Solid 95 elements, and steel rebars were modeled using Link 180 elements to accurately simulate the effect of concrete shrinkage. The remaining parts of the pylon were modeled using Shell 281 elements.  
Concrete compressive strength and tensile strength are set as 32.4 MPa and 2.65 MPa according to the standard value of material tests. The elastic modulus and Poisson's ratio of the concrete are set as 34,500 MPa and 0.2. The steel bars' elastic modulus is 2.06 \(\times\) 10\({}^{5}\) MPa, and the yielding strength is 400 MPa. High-strength parallel steel wire ropes are used for stayed cables. The elastic modulus is 1.95 \(\times\) 10\({}^{5}\) MPa, and the tensile strength is 1670 MPa. The density of the concrete is 2600 kg/m\({}^{3}\), and the density of the steel bars and stayed cables is 7850 kg/m\({}^{3}\).
## 2 Operation of the BB84 Protocol
### Key Map  
At this stage, the parties form a secret key by mapping their event records of the sifted data in the secret key according to the following: \(|0\rangle,|+\rangle\to 0\), and \(|1\rangle,|-\rangle\to 1\). The binary string that results from this step constitutes a secret key.
###### Abstract  
The wind field plays an important role in the maintenance of large sport venues. Most wind field simulation research involves no quantitative analysis of the impacts of the wind environment on athletes' safety and performance. Taking the National Sliding Centre (NSC) of the Beijing 2022 Winter Olympics as the research object, this paper conducts a wind field simulation study based on CFD, and innovatively explores the quantitative impact of building-scale wind environment characteristics on micro-athletes' wind resistance and performance for the first time. First, an NSC model and a human body model of athletes are constructed and simplified. Grid independence verification is carried out, and the grid is divided and optimized. Second, wind environment simulation under different climatic conditions is completed, based on CFD technology. The defined wind speed dispersion indexes are calculated. The characteristics of the wind field outside the venue is quantitatively analyzed. Third, we define and calculate the main influencing parameters on athletes' competition performance. The impacts of the wind field on micro-athletes' performance are quantified. With a gradual increase in wind level (3.125, 3.5, 4.5, 5.5, 6.5, 7.5, 8.229), the optimized sliding route can reduce the air resistance by 0.2607 N, 0.3415 N, 0.4600 N, 0.6469 N, 0.9283 N, 1.1741 N, and 1.4535 N, which can improve the athletes' competition results by 0.02 s, 0.03 s, 0.04 s, 0.06 s, 0.09 s, 0.11 s, and 0.14 s, respectively. This paper provides methodological support for exploring the mechanism of athlete performance from the perspective of a building-scale environment.  
CFD; wind environment and athlete performance; wind speed dispersion; wind resistance reduction; optimize the sliding route
# A Software Defined Radio Based Anti-UAV Mobile System with Jamming and Spoofing Capabilities
## 3 System Functionalities  
### Radio Frequency Interference  
Most drones typically operate in one of two modes: the manual piloting mode (controlling the drone with the remote control) and the autonomous mode (previously plotting a route through geographic location coordinates). In both cases, the UAV relies on radio signals. In the case of the UAV being controlled by remote control, radio communications typically at 2.4 GHz or 5.8 GHz are used. When flying autonomously, they resort to GNSS signals. The implemented system can emit efficient signals to block these two types of operation. Since it is difficult to know which flight mode the unauthorized drone is in, the proposed anti-UAV mobile system transmits jamming signals on all frequencies of remote control and GNSS signals.  
In [22], we conducted a study to understand which was the most effective approach for jamming the GPS signal. Five different techniques of jamming the GPS signal were considered: barrage jamming; tone jamming; sweep jamming; successive pulses jamming; and protocol-aware jamming. The different techniques were implemented and evaluated using low-cost SDR platforms and the GNU radio software development toolkit. The best-performing jammer was protocol-aware jamming (Figure 3), which uses an architecture similar to that used by a transmitter of GPS signals. Using this approach, the interfering signal mixes more effectively with the main signal since they exhibit identical spectral behavior, requiring a lower transmitter power to successfully disable the correct reception of the GPS signal.  
In the case of jamming against remote control frequencies, we employ the technique of barrage jamming, because we have no knowledge of the protocol to be used in the communication. The barrage jamming is the simplest form of interference and is generally defined as a jammer that transmits noise-like energy throughout the portion of the spectrum occupied by the target, as shown in Figure 4. It essentially increases the noise level in the receiver, making it difficult to operate the communication system.  
Figure 4: Barrage Jamming Spectrum.  
Figure 3: Protocol-Aware Jamming Spectrum.
## 5 Shape Fitting
### Point Group Normalization  
Under real-world conditions, the size of the input point group varies. The use of projection relationships allows us to obtain rough estimates of the shape dimensions. However, dimensions that are overly large or small can still produce incorrect results. To enhance the stability of the output results, we first normalized the point groups prior to shape fitting to ensure that all of the dimensions of the point groups fell within a set range.  
In the following, we respectively present coordinate \(\mathbf{X}_{i}\) of the point group before and after normalization:  
\[\left[\begin{array}{c}x_{j}\\ y_{j}\\ z_{j}\\ 1\end{array}\right]_{\text{Normalized}}=\mathbf{T}\left[\begin{array}{c}x_{ i}\\ y_{i}\\ z_{i}\\ 1\end{array}\right]_{\text{Original}} \tag{18}\]  
\[\mathbf{T}=\left[\begin{array}{cccc}s_{x}&0&0&-s_{z}\overline{x}\\ 0&s_{y}&0&-s_{y}\overline{y}\\ 0&0&s_{z}&-s_{z}\overline{z}\\ 0&0&0&1\end{array}\right] \tag{19}\]  
where \((s_{x},s_{y},s_{z})\) are the scaling ratios, and \(\overline{\mathbf{X}}=\left[\begin{array}{cc}\overline{x}&\overline{y}& \overline{z}&1\end{array}\right]^{T}\) is the center of the point group. Let \(l\) be the mean distance between the point and the center, and \(s\) be the scaling rate. Assuming that the point group contains \(n\) points, we derive the following:  
\[\begin{array}{l}s_{x}=s_{y}=s_{z}=\frac{s}{l}\\ l=\frac{1}{n}\sum\limits_{i=1}^{n}\left\|\mathbf{X}_{i}-\overline{\mathbf{X}}\right\| \end{array} \tag{20}\]  
Performing normalization before estimating and solving the initial parameters enables the generation of a geometric shape that is highly similar to that of the point group.  
For AR applications, we can replace the identified objects with similar virtual objects using pre-established databases, model database, and scene database. The model database records the shape features of object models, and the scene database stores the index values and probability of the objects relevant to various scenes. Assume that the scene to be generated is known. The step after acquiring the size, shape, and position of the object point group is to search the model information from the database and identify the relevance between the object and the scene for a best-fitted model. The object replacement procedure can then be finished by importing this fitted model into the real scene (or virtual scene) and moving the model to the position of object point group with the correct orientation.
# Bridge Expansion Joint in Road Transition Curve: Effects Assessment on Heavy Vehicles
## 5 Conclusions  
In road engineering, bridge expansion joints often cause troubles concerning evenness and skid resistance. Even if the joint is designed and built in accordance with the best practice, the discontinuity of the pavement may produce a dangerous unevenness. This irregularity could be very important if the joint is in a transition section (clothoid) of the road, as considered in the paper. In fact, in this section, the curvature and super-elevation of the road gradually change between two consequent elements with different planimetric curvature. Consequently, in this area, the road surface is \"slanted\", and if the joint is large (for example in a highway seismic joint), the elevation difference from the points of the road can reach some centimeters. This difference could produce an additional, undesirable unevenness in addition to a possible construction unevenness.  
A heavy vehicle was instrumented with piezometric accelerometers to evaluate the effects of the irregular BEJ on a running vehicle. The results of the performed surveying and tests highlighted the unevenness on the surface profile caused by the presence of the BEJ along a transition curve. The analyses carried out in the present work underline how this kind of discontinuity may affect the response of truck suspensions and the dynamic response of the bridge. The measurements permitted also identifying the most representative frequencies of loads applied to the structure. The results also confirmed the possibility of a complete lifting of the vehicle's rear axles with serious risks of grip loss on the road and, consequently, serious detrimental effects on road traffic safety. The road agency adopted two main actions to improve the level of service in the road section and to ensure a safe and efficient traffic flow: reduction of geometric irregularities resulting from the joint and reduction of the allowed speed.  
BEJs are localized irregularities that can have different severity levels according to many factors, such as if placed in the road transition curve. This study provides a guidance on the evaluation of the detrimental effects induced by BEJs, which may affect all of the road infrastructure components: vehicles, pavement, bridge and users, with attention to safety. The results can be used by road agencies to specify strategies and procedures for policies to guarantee minimum safety levels at roads.  
[PERSON] had the original idea for the study. [PERSON] and [PERSON] carried out all in situ measurements. [PERSON], [PERSON] and [PERSON] analyzed the data. [PERSON] implemented the finite element model in LS-DYNA and performed the data post-processing analysis. [PERSON] and [PERSON] wrote sections for the first draft of the manuscript. All authors contributed to further drafts and had full access to all of the data.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction
### Specification of Computer Systems  
Temporal logics are a well-known paradigm for the specification and verification of computer systems, especially where sensor data must be processed under strict timing constraints. Several temporal logics, including linear temporal logic (LTL) [8] and metric temporal logic (MTL) [9], are used to capture system requirements. LTL allows expressing properties over system executions, such as specifying that every occurrence of a problem will eventually trigger an alarm. MTL extends LTL by introducing time constraints on temporal operators, making it appropriate for real-time system specifications.  
MTL supports two main semantics: \"continuous\" and \"pointwise\". The pointwise semantics, which interprets time as discrete points (time words), is the standard approach for systems modeled as timed automata [10]. Both semantics have been extensively examined and investigated [11; 12; 13; 14]. Using MTL, it is possible to express time-bounded requirements, such as ensuring that any occurrence of a problem will trigger an alarm within a specified maximum of five time units.  
Extensions of MTL, such as EMTLK [6], introduce an epistemic dimension, allowing for the representation of what agents know or believe over time. This enhancement is particularly useful for multi-agent systems with sensing capabilities, where reasoning about agents' knowledge, alongside timing constraints, is critical for system verification.  
In this paper, we further extend EMTLK by incorporating deontic operators, allowing reasoning about norm compliance and violations in real-time multi-agent systems. Our new logic, deontic-epistemic metric temporal logic (EMTLKD), provides the following key advantages:  
* Explicit Modeling of Norm Compliance--unlike classical temporal logics, which assume agents follow protocols correctly, EMTLKD allows distinguishing between ideal (norm-compliant) and actual (possibly violating) behaviors. This is essential for verifying safety-critical systems, where norm violations must be detected and analyzed.
* Integration of Epistemic and Temporal Reasoning--many verification approaches consider either epistemic reasoning (agent knowledge) or real-time constraints (temporal logic), but not both. EMTLKD enables the verification of knowledge evolution over time -useful for systems where agents make decisions based on time-sensitive information acquisition.
* Scalability Through SMT-Based Bounded Model Checking (BMC)--classical model checking methods often suffer from state-space explosion, particularly in real-time multi-agent systems. Our SMT-based BMC approach mitigates this issue by encoding the problem into SMT constraints, allowing efficient verification of finite execution prefixes rather than the full infinite state space.  
By combining deontic, epistemic, and temporal reasoning, EMTLKD provides a more expressive and practically useful framework for verifying real-time multi-agent systems, particularly in safety-critical environments where compliance, knowledge, and timing constraints must be verified together.
## 3 Results and Discussion
### Parametric Study  
#### 3.3.1 Effect of Cohesions of Solidified Soil  
Figure 7 shows the effects of the cohesions on solidified soil embankment. The cohesions of solidified soil performed a crucial role in the total settlements of embankment in Figure 7a. When the cohesions were larger than 25 kPa, the total settlements were fairly small, and exhibited minimal changes with increasing cohesions. In contrast, when the cohesions were smaller than 25 kPa, the total settlements significantly increased. Significantly, the total settlements at point C with a cohesion of 10 kPa were approximately ten times larger than those observed at a cohesion of 75 kPa. Additionally, Figure 7b demonstrates the impact of cohesions on the vertical stress values in the upper part of the embankment. Larger cohesions contributed to higher vertical stress, resulting from the additional stresses of the pavement and traffic loads. The vertical stress remained relatively consistent at a depth of approximately 4.5 m, after which it increased with increasing embankment depth. Afterward, there was an obvious difference in the distribution of vertical stress among different cohesions.  
When the cohesions were smaller than 25 kPa, the distribution of the vertical stress displayed a \"Z\" shape, which is similar to the distribution in gravel embankments. Conversely, the vertical stress gradually decreased to near 0 kPa at point C, in solidified soil with cohesions exceeding 25 kPa. It was observed that higher cohesions in the solidified soil led to a more apparent decrease in the soil arch heights.  
Figure 7c,d show the total settlements distribution along with AB and AC (as shown in Figure 1). The total settlement within the pile cap range were particularly small under different cohesions. When the cohesions were less than or equal to 25 kPa, the total settlement at the edge of the pile cap increases sharply, while when the cohesion was greater than 25 kPa, it increased slightly. In Figure 7e, the axial force of the pile also exhibited a similar variation pattern with the total settlement along AC and AB. This is because the piles bear more embankment and vehicle loads with increasing the cohesions of the solidified soil embankment. There was an economical cohesion (25 kPa) for solidified soil embankments. The cohesions of solidified soil could be changed by adjusting the amount of curing agent.
## 3 Preliminaries: Network Model and Problem Definition
### Network Communication and Observation Model  
Suppose the radio channel bandwidth resource of a single base station is \(B\), and the service radius is \(r\). In addition, we assume that the base station has dynamic channel allocation capability, the user \(ud_{i}\) allocated bandwidth weight for the node \(en_{j}\) is \(bf_{i,j}\), and then the allocated bandwidth \(B_{i,j}\) can be expressed as follows:  
\[B_{i,j}=\frac{bf_{i,j}}{\sum_{x=1}^{m}bf_{x,j}}\cdot B \tag{3}\]Here, if the user \(ud_{i}\) leaves the service area, the \(bf_{i,j}=0\). The above Formula (3) represents the proportion of the bandwidth allocated by the base station to a user in the total bandwidth of the base station. So, the maximum data transmission rate between \(ud_{i}\) and \(en_{j}\) can be expressed as follows:  
\[C_{i,j}=B_{i,j}log_{2}\bigg{(}1+\frac{PG_{i,j}}{\sigma^{2}}\bigg{)} \tag{4}\]  
where \(P\) is the transmission power of the sender's equipment. \(G_{i,j}\) is the channel gain between \(ud_{i}\) and \(en_{j}\). \(\sigma^{2}\) is the Gaussian noise power of the channel.  
Electromagnetic waves travel at the speed of light \(V_{\text{space}}\) in the service area. For wired communication, the link transmission rate is \(C_{\text{fiber}}\) and the link propagation rate is \(V_{\text{fiber}}\). Each line has a fault probability \(p_{\text{fiber}}\) in every minute, and communication capability can be recovered after maintenance time \(T_{\text{repair}}\).  
The MEC network model considered in this paper also has the problem of local observation. A single device only has the ability of local observation, and the global state information of the network can only be obtained by synchronous communication between devices. In addition, for user equipment, the computing density and computing result size of the task cannot be estimated.