esa-sceva/satcom-chunk-collection huggingface dataset:
# A Two-Stage Local Path Planning Algorithm Based on Sampling and Optimization Methods
## 3 Proposed Method
### Optimization Stage
#### 3.3.3 Velocity Constraint  
During the robot's movement, it is also necessary to ensure that the local path meets kinematic constraints. Since this paper considers only a differential-drive robot, the constraint is applied solely to the velocity. The velocity constraint ensures that the speed between any two local path points is less than the robot's maximum velocity \(v_{max}\). The penalty function of the velocity constraint can be expressed as  
\[v=\frac{\lVert(s_{j+1},d_{j+1})-(s_{j},d_{j})\rVert}{\Delta t} \tag{14}\]  
\[f_{vel}=\begin{cases}v-v_{max},&v>v_{max}\\ 0,&else\end{cases} \tag{15}\]
# Undrained Behavior of Microbially Induced Calcite Precipitated Sand with Polyvinyl Alcohol Fiber
###### Abstract  
Microbially induced calcite precipitation can cement sand and is an environment-friendly alternative to ordinary Portland cement. In this study, clean Ottawa sand was microbially treated to induce calcite contents (CCs) of 0%, 2%, and 4%. Polyvinyl alcohol fiber was also mixed with the sand at four different contents (0%, 0.2%, 0.4%, and 0.6%) with a constant CC of 4%. A series of undrained triaxial tests was conducted on the treated sands to evaluate the effects of the calcite treatment and fiber inclusion. Their hydraulic conductivity was also determined using a constant head test. As the CC increased from 0% to 4%, the friction angle and cohesion increased from 35.3\({}^{\circ}\) to 39.6\({}^{\circ}\) and from 0 to 93 kPa, respectively. For specimens with a CC of 4%, as the fiber content increased from 0% to 0.6%, the friction angle and cohesion increased from 39.6\({}^{\circ}\) to 42.8\({}^{\circ}\) and from 93 to 139 kPa, respectively. The hydraulic conductivity of clean Ottawa sand decreased by a factor of more than 100 as the CC increased from 0% to 4%. The fiber inclusion had less effect on the hydraulic conductivity of the specimen with 4% CC.  
microbialy induced calcite precipitation; polyvinyl alcohol fiber; fiction angle; cohesion; hydraulic conductivity +
Footnote †: journal: Applied Sciences
## 1 Introduction
### Objectives  
This study aimed to enhance the precision of smartphone positioning by adopting the approach elucidated in [19]. For ease of reference, we will refer to this approach as the Modified Downdate Algorithm (MDDA). The algorithm was precisely designed to select the most optimal visible satellites that are positioned above the horizon. Additionally, it introduces a distinctive weighted matrix aimed at augmenting the precision of smartphone low-cost GNSS receivers. Furthermore, the effectiveness of the new algorithm was evaluated when combined with a low-cost smartphone Inertial Measurement Unit (IMU) using both LC and TC integration techniques, considering the motion dynamics. To validate the proposed method, real road trajectory data collected from low-cost smartphone sensors were utilized. The main contributions of this study are as follows:  
1. The application of satellite selection-based MDDA to both LC and TC algorithms for all visible satellites.
2. The application of the proposed algorithm in scenarios where the receiver is limited to a specific number of channels.
3. The analysis of the impact of the selected satellite-based MDDA on the overall positioning performance.  
These contributions aim to optimize smartphone positioning accuracy by improving the satellite selection process and investigating the influence of selected satellite-based MDDA on the performance of LC and TC integration techniques. Additionally, the performance of the TC method during various simulated outage periods with different visible satellites at this outage is analyzed.
## 2 Methods  
From the point of view of fine rescue, this paper constructs an estimation model of buried personnel in a single building. As illustrated in Figure 1, the model is mainly divided into two stages: \"Pre-earthquake Key Information Extraction\" and \"Post-earthquake Risk Assessment of Seismic People Trapped\".  
Pre-earthquake stage is for extraction of the key information factor of people trapped. Pre-earthquake high-resolution satellite imagery is used to obtain building edge information; Point of Interest (POI) data are used to obtain building function; and Digital Surface Model (DSM) data are used to obtain building height information. Statistical yearbooks are used to obtain demographic data for the study area. Based on the information obtained above, we used a \"Population Distribution Model\" to simulate the distribution of the local  
\begin{table}
\begin{tabular}{c c c} \hline
**Estimation Modeling** & **Methods** & **References** \\ \hline \multirow{6}{*}{Seismic-based statistical model} & Adding the earthquake-related parameters fitting function & [8; 9; 10] \\  & to the post-earthquake casualty data & \\  & Adding the material structure and collapse rate of buildings & [11; 12; 13; 15] \\  & with the post-earthquake casualty data & \\  & Based on the prediction model of building collapse rate and & [14] \\  & chamber rate & \\  & Based on multi-source data & [16; 17] \\ Statistical model based on kilometer grid & Statistical model was optimized by superimposing & [18; 19; 20; 21; 22; 23] \\  & multi-source data with kilometer grid as unit & \\ \hline \end{tabular}
\end{table}
Table 1: Modeling and methods approaches.  
Figure 1: Flowchart of the proposed work.  
population and to study the indoor population rate assessment under different building attributes and different time periods.  
Post-earthquake stage is an estimation of people trapped after earthquake based on multi-source data. Post-earthquake aerial imagery is used to extract the damaged buildings and the results are fed into our proposed single building burial model, as shown in Equation (1), to finely estimate and analyze seismic people trapped in earthquake.  
\[B_{p}=P_{i}\times P_{t}\times D_{d} \tag{1}\]  
where \(B_{p}\) represents the estimation number of people trapped in a single building; \(P_{i}\) represents the number of people in a single building; \(P_{t}\) represents indoor population rate; and \(D_{d}\) represents degrees of building damage. It is important to emphasis that the description of trapped people in buildings in this paper does not include those who were quickly removed from danger either by themselves or by mutual rescue. These people may die, be seriously injured, or buried too deeply to save themselves or each other during an earthquake. In this paper, referring to previous research findings on the rate of serious injury and mortality of personnel at different degrees of damage [24; 25; 26], the burial rate is taken to be 1/300 in the severely damaged building state and 1/20 in the destroyed building state.
## 1 Introduction
The TRA module employs a dynamically adjusted correlation graph construction strategy, allowing it to flexibly adapt to feature relationships in different scenes. The DLIE module introduces self-learning parameters, optimizing deep learning methods for image features and enabling adaptive modifications to the image enhancement procedure. This enhancement significantly boosts the model's detection capabilities. * We propose an improved YOLOV9 detection network, incorporating a three-layer routing attention mechanism. This mechanism captures the features of the restored clear images from the TRA and DLIE modules through joint learning, enhancing the network's ability to detect objects. * We introduce a comprehensive loss function where the parameters of the routing attention loss function and the IoU loss function are dynamically adjusted based on the weather factor features extracted by the TRA module. This approach allows for the dynamic adjustment of scale factor ratios and blur factors, refining bounding box generation and loss function computation. Consequently, the model better adapts to various weather conditions, achieving more accurate object detection under different weather scenarios. This paper is organized as follows: Section 2 includes an overview of relevant work on image dehazing, deraining, desnowing, and image enhancement. Section 3 elaborates on the specific improvements proposed in this paper. The experimental results and analysis are presented in Section 4. Finally, in Section 5, we engage in a discussion and summary of the research findings.
# Micromechanism of the Dispersion Behavior of Polymer-Modified Rejuvenators in Aged Asphalt Material
###### Abstract  
Polymer-modified rejuvenator has a different composition and dispersion behavior to traditional rejuvenators. The objective of this study was to investigate the micromechanism of polymer-modified rejuvenators on the behavior of aged asphalt binder. Firstly, gel permeation chromatography (GPC) analysis was conducted to determine the dispersion effectiveness. Secondly, the dispersal behavior of polymer-modified rejuvenators was studied by means of atomic force microscopy (AFM) and scanning electron microscopy (SEM). Rheological, toughness-tenacity, and force-ductility analyses of the rejuvenated asphalt binder were additionally performed. The results indicate that the contacted asphaltenic micelles in aged asphalt binder were dispersed by dispersion agent in the polymer-modified rejuvenator, and that the dispersion ability of the polymer-modified rejuvenator was promoted to the commercial rejuvenator level. Additionally, the polymer-modified rejuvenator was found to improve the rejuvenated asphalt binder's resistance to deformation, through the formation of polymeric network structures in the asphalt binder. The results may be used to improve the performance of rejuvenated asphalt binder in recycled-pavement engineering.  
disperson; aged asphalt binder; modified; rejuvenator; micromechanism; performance
# A Set-Theoretic Approach to Modeling Network Structure
## 2 The Interior  
Let \(S\) be a set. An **operator**\(\tau:2^{S}\to 2^{S}\) is an injective function which maps subsets of \(S\) into subsets of \(S\). We denote operators by Greek letters and use postfix notation, as in \(Y.\tau\), where \(Y\subseteq S\). An operator \(\varphi\) is said to be a **closure** operator if, for all \(X,Y\subseteq S\), (C1) \(Y\subseteq Y.\varphi\) (expansive), (C2) \(X\subseteq Y\) implies \(X.\varphi\subseteq Y.\varphi\) (monotone) and (C3) \(Y.\varphi.\varphi=Y.\varphi\) (idempotent). Closure operators are a staple of topological mathematics.  
If we replace axiom C1 with an contractive axiom II, so that for all \(X,Y\in\mathcal{S}\): (I1) \(Y.\iota\subseteq Y\) (contractive); (I2) \(X\subseteq Y\) implies \(X.\iota\subseteq Y.\iota\) (monotone); and (I3) \(Y.\iota.\iota=Y.\iota\) (idempotent), then \(\iota\) is said to be an **interior** operator. We use \(\iota\) to denote interior operators and \(\varphi\) to denote closure operators; they are similar, except that one is contractive while the other is expansive.  
If one visualizes \(S\) as a polytope, then its closure might be the smallest sphere containing \(\mathcal{S}\) (often called its convex hull), while its interior could be the largest inscribed sphere, or ball. Alternatively, if one thinks of \(S\) as being a bit of irregular surface terrain with ridges and valleys, then a closure operator fills in the valleys until the terrain is uniformly smooth. An interior operator, in contrast, levels the peaks and ridges until a smooth terrain is obtained.  
Let \(\mathcal{N}\) be a network. For any \(Y\subseteq N\), we say the **neighborhood** of \(Y\) is \(Y.\eta=\{z|\exists y\in Y,\{y,z\}\in L\}\cup Y\). (In graph theory, \(Y.\eta\) is sometimes called the \"closed neighborhood\" of \(Y\), and denoted \(N[Y]\), while \(N(Y)=Y.\eta-Y\) is called the \"open neighborhood\" [1,2]). Finally, since all operators map sets into sets, even when we are talking about the neighborhood of a single node, for example \(z\) in (1) below, we express it as \(\{z\}.\eta\). A **neighborhood closure** operator, \(\varphi_{\eta}\), on \(\mathcal{N}\) can be defined by  
\[Y.\varphi_{\eta}=\{z\in Y.\eta\mid\{z\}.\eta\subseteq Y.\eta\}. \tag{1}\]  
\(Y\subseteq Y.\varphi_{\eta}\subseteq Y.\eta\), so \(\varphi_{\eta}\) is expansive. It is not hard to see that \(\varphi_{\eta}\) is monotone. Finally, since \(Y.\varphi_{\eta}\subseteq Y.\eta\), \(Y.\varphi_{\eta}\) must be idempotent, implying \(\varphi_{\eta}\) is a closure operator. (C3, or idempotency, is normally the most difficult property to prove when establishing a closure, or interior, operator.) The neighborhood closure operator, \(\varphi_{\eta}\), is fundamental to the development of following sections.
###### Abstract
The ship's berthing is affected by the port's characteristics, natural environmental elements such as the wind and tide, and other characteristics according to the type and size of ship, as well as human factors [20]. At the present time, the human factor corresponds to pilots who are experts at navigating the port environment, and major ports around the world safely dock ships by bringing pilots on board when ships arrive and depart. However, there are instances that sometimes lead to shipping accidents due to pilot negligence, i.e., human error [16]. In addition, in no port in the Korea is there an official ship maneuvering port guideline, and ships are fully dependent on the experience and knowledge of the pilots. For instance, the 2014 oil spill accident in Yeosu of the WU YI SAN, a tanker with a gross tonnage 160,000 tons, resulted from a collision with a terminal because the pilot did not sufficiently control the ship's approach speed [21]. In addition, an accident in which a container ship with a gross tonnage of about 150,000 tons collided with a gantry crane in 2020 at the Busan New Port was also caused by a pilot's failure to control the ship's berthing velocity [22]. In order to solve these problems, it is necessary to analyze the predominant patterns of ship trajectory. The analysis was based on AIS data for ship arrival and departure in a port. Through the results of this study, a basic study on the MASS technology of ship maneuvering in ports can be proposed, and human error can be prevented. In addition, in the short term, it is possible to propose ship maneuvering guidelines for ports. Accordingly, a flowchart of this investigation is shown in Figure 1. In this study, AIS data on vessels arriving at and departing from the Busan New Port in the Korea were collected and analyzed by dividing them into port arrival and departure data. The algorithm that was then used to analyze the patterns of ship trajectories was the same DBSCAN algorithm as that used in previous navigational route studies.
# Secure Data Distribution Architecture in IoT Using MQTT
## 3 Distributed MQTT Architecture
### Broker Service  
The broker service wraps an MQTT broker to expose its internal resources and allow it to publish and subscribe to topics outside its jurisdiction. The service is mainly concerned  
Figure 4: Discovery process illustration.  
with interactions with the discovery service and other broker services, leaving interactions between MQTT clients and the broker intact. This ensures backward compatibility of the service with the basic MQTT protocol, while having all that functionality associated with the discovery and consumption of external data, in addition to exposing internal data. We detail the three main responsibilities of a broker service as follows:  
1. Discover global topics for local clients: The broker service advertises its interests in new topics to the discovery service as they come. The entire process is event-driven, where if a subscription for a new topic has not been seen before by the service, it advertises that topic to the discovery server. In response to that, the discovery service sends subscription notification messages to the publisher of that topic, as illustrated in Section 3.2.
2. Deliver local publications to global clients: The broker service that is managing a publisher provides the service that allows brokers managing subscribers to connect and join groups pertaining to the topics they are interested in. Subsequently, the broker managing a publisher is responsible to push new publications to all new publications to the respective groups.
3. Deliver global publications to local clients: This is the last mile delivery of data coming from global publishers, which has to be published to local clients. The broker service in this case publishes incoming data to its intended topic locally.  
An illustration of the end-to-end delivery of publications is shown in Figure 5. In the figure, it is assumed the subscriber already knows about the publisher following the process described in Figure 4. Immediately following the discovery, Broker B connects to Broker A's Websockets server and asks to join a group call \(T\) about the topic. Hence, whenever a new publication of topic \(T\) is generated at Broker A's end, it is pushed through all the sockets in the group called \(T\). This greatly simplifies the design of architecture and improves the reliability of the implementation as all the socket management functionality can be delegated to a robust socket management library, such as Socket. IO. Furthermore, the use of WebSockets as transport between brokers means that the delivery of messages across brokers is reliable by default.
## 3 Results  
### Response of Plant Root Growth to Varying Flooding Durations  
#### 3.1.1 Root Morphology  
The response of various plant root morphology indicators to different durations of inundation was modeled via a quadratic function (y = ax\({}^{2}\) + bx + c) (Figure 2, Table 1). The total root system length of _Carex_ initially increased but then decreased as the inundation duration increased, whereas that of _Cymodon_ exhibited a similar trend, and that of _Eleusine_ consistently decreased. The total root lengths of _Carex_, _Cymodon_, and _Eleusine_ reached their respective maximum values of 1113.52 cm, 903.44 cm, and 384.86 cm at 103, 94, and 41 days of inundation, respectively. The total surface area of the root systems of the three species tended to increase but then decreased with prolonged inundation. The total root surface areas of _Carex_, _Cymodon_, and _Eleusine_ peaked at 103, 94, and 41 days of inundation, respectively. The maximum total root surface areas for _Carex_, _Cymodon_, and _Eleusine_ were 229.29 cm\({}^{2}\), 171.16 cm\({}^{2}\), and 88.2 cm\({}^{2}\), respectively, which were achieved at 103, 95, and 97 days of flooding, respectively. The total volume of the root systems also tended to increase and then decrease with increasing flooding duration for all three plants, with maximum values reaching 119, 131, and 88 days of flooding for _Carex_, _Cymodon_, and _Eleusine_, measuring 3.94 cm\({}^{3}\), 2.71 cm\({}^{3}\), and 1.37 cm\({}^{3}\), respectively. The mean diameter of the root systems of the three plants gradually increased with increasing flooding duration, reaching maximum values of 0.68 cm, 0.67 cm, and 0.92 cm at 180 days for _Carex_, _Cymodon_, and _Eleusine_, respectively. Notably, the range of all the root morphometric indices for _Eleusine_ was significantly different from that of the other two plants, with a linear range of 0.6-0.9 cm for _Eleusine_, while _Carex_ and _Cymodon_ exhibited a linear range of 0.6-0.7 cm(Figure 2A). The extremely high values of the three morphological indicators, excluding root diameter, for _Eleusine_ were lower than the extremely low values for _Carex_ and _Cynodon_.
# Preface of Special Issue on Laser Scanning
###### Abstract  
A laser is a spatially coherent light that can travel through space with very little diffraction. This distinctive feature makes lasers an ideal signal carrier for measuring distances. The special issue of \"Laser Scanning\" (also known as LiDAR--Light Detection and Ranging) provides several interesting reports on the wide range of applications that this technology supports. Laser scanning is so defined because it uses laser technology to detect objects and calculate distances between the sensor and the reflective surfaces. Reflected laser pulses provide a means for measuring time-of-flight, phase differences, and other properties of the reflected pulse and of the surface that causes a full or partial reflection. A strong added value of laser scanning is that it can provide multiple returns from a single emitted pulse, because of the partial obstruction of the laser beam. For several decades, accurate distance measures have been done with laser scanners as described above, and the technology is ever actively being improved. Recent developments include multi-wavelength scanners, solid-state LiDAR, single-photon LiDAR, and scanners with increased pulse frequency, thanks to solutions related to concurrent pulses in the air (multiple time around--MTA). Also the decreased weight of components allows sensors to be assembled into unmanned vehicles (e.g., Unmanned Aerial Vehicles--UAVs) [1; 2].  
Laser scanning applications covered in this special issue can be divided into the following categories: multi-wavelength LiDAR [3; 4], mobile mapping support for indoor [5; 6; 7], outdoor, and other applications [8], object tracking and navigation [9; 10; 11], deformation monitoring [12; 13], modelling and detection [14; 15; 16], geometric accuracy assessment [17; 18; 19], and hybrid technologies like LiDARgrammetry [20], which are summarized in more detail in the following section.
## 3 Results and Analysis  
### Stress-Strain Curve Analysis  
The stress-strain curves of each specimen under different proportions are shown in Figure 5.  
Figure 4: Position diagram of acoustic emission sensors.  
Figure 3: The acoustic emission equipment. (**a**) DS5-16B acoustic emission signal analyzer. (**b**) Acoustic emission sensor.  
The analysis of Figure 5 reveals that each rock-like specimen underwent stages of pore and crack compaction, elastic deformation, damage, and post-fracture. This was the same as the change rules of the typical original rock stress-strain curve.  
1. [label=(0)]
2. Pore and crack compaction stage: with the increase of axial compression, the original pores and cracks in the specimen were gradually closed, the material was compacted, and the stress-strain curve was concave downwards.
3. Elastic deformation stage: the stress-strain curve gradually approached a sloping straight line as the loading process continued. At that time, the slope of the straight line was the elastic modulus of the rock-like materials.
4. Damage stage: at this stage, new cracks and fissures were generated inside the rock-like specimens and the specimen produced irreversible deformation. The stress-strain curve was no longer a sloping straight line, and the slope of the curve gradually decreased.
5. Post-fracture stage: as the loading continued, the load that the rock-like specimen could withstand reached the peak value. At that time, the stress-strain curve suddenly dropped, and the specimen failure entered the post-fracture stage.  
The analysis of Figure 5 revealed that in the absence of gypsum, the stress of the rock-like specimen rapidly decreased after reaching its peak, the specimen immediately failed, and it lost its bearing capacity. This brittle failure characteristic was similar to the mechanical properties exhibited by hard brittle rocks. When the rock-like specimen contained gypsum and reached its compressive limit, the stress of the specimen gradually decreased, and the failure of the specimen was relatively moderate. Beyond the peak strength, the specimen still maintained a certain bearing capacity, which was similar to the mechanical properties of soft rock.  
Figure 5: Stress-strain curves of samples with different proportions. (**a**) A1–A4 stress–strain curve. (**b**) B1–B4 stress–strain curve. (**c**) C1–C4 stress–strain curve.