esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Proposed Fleet Management System
### Software Development  
For the development of the fleet management software, several programming languages were used, including the HTML, CSS, JS, and PHP, as shown in Figure 16. The combination of these tools allowed for the creation of an interactive and functional web application capable of managing and monitoring different models of mobile robots.  
Figure 15: A flowchart of the fleet management software operation.  
The HyperText Markup Language (HTML) is the programming language responsible for structuring the content of the web page, serving as the foundation for creating the interface that the user interacts with. Cascading Style Sheets (CCSs) are used to apply visual styles to the content of the web page, making the interface more intuitive and organized for the operator. JavaScript (JS), a client-side interpreted programming language, allows interactivity to be added to the web page, consisting of various small functions that are executed when the user interacts with the elements defined on the web page through the HTML.  
For the communication between JavaScript and PHP, the FetchAPI was used, a native JavaScript interface that allows asynchronous HTTP requests. This API acts as an intermediary between the client-side code JS and the server-side code (PHP). Through the API, it is possible to send data to PHP scripts, which can then query, add, or remove information from the AGV databases or the fleet manager's access control database. All of this is achieved dynamically without the need to reload the web page.  
Additionally, the Axios library in JS was used to make HTTP requests. In the project, Axios was particularly useful in the communication with the AMRs, allowing real-time data to be retrieved through HTTP or TCP/IP interfaces.  
The correct integration of all these technologies--the HTML, CSS, JavaScript, Fetch API, PHP, and Axios--is essential to ensure the proper functioning of the entire system, enabling the centralized and efficient management of the fleet of mobile robots.  
Figure 17 presents the internal data integration architecture within the runtime environment of the fleet management system. The data from mobile robots is acquired, analyzed, normalized, synchronized, and stored in a structured local repository. The internal access interface provides these processed data to other system modules, enabling a modular, scalable, and reliable operation in the context of industrial automation.  
Figure 16: The interconnection of the different technologies used.
# Internet of Things-Based Healthcare Systems: An Overview of Privacy-Preserving Mechanisms
## 8 Privacy-Preserving Patient Monitoring
### Encrypted Health Data Streams  
Regarding the pursuit of privacy preservation, this section underscores the pivotal significance of encrypting health data streams originating from wearable IoT devices. Robust encryption protocols, exemplified by homomorphic encryption, ensure the confidentiality of health information. This approach aligns seamlessly with Secure Data Encryption and Transmission, extending the discourse to accommodate the challenges of implementing encryption in resource-constrained wearable devices [16].
# SMCSPO-Based Robust Control of AUV in Underwater Environments including Disturbances
###### Abstract  
In the underwater environment, robust control algorithms are required to control autonomous underwater vehicles (AUVs) at high speed while preventing large nonlinearities and disturbances. Sliding mode control (SMC) is a well-known robust control theory and has been widely used not only in AUV control but also in systems such as industrial robots which have high nonlinearity in their system dynamics. However, SMC has the disadvantage of causing chattering on the control input, and it is difficult to apply this method to the control fins of an AUV system that cannot move its fins at high speed underwater. In this work, a design for a sliding mode control with sliding perturbation observer (SMCSPO) algorithm is applied to AUVs, and the simulation results under underwater disturbance conditions are discussed. From simulation using MATLAB, it is confirmed that AUV control using SMCSPO shows better trajectory tracking control performance without chattering than PID control.  
automous underwater vehicle; sliding mode control with sliding perturbation observer; PID control; MATLAB simulation +
Footnote †: journal: Applied Sciences  
18 November 2021  
Footnote †: journal: Applied Sciences
# An Inference Framework of Markov Logic Network for Link Prediction in Heterogeneous Networks
###### Abstract  
The presence of multiplex edges and sparse links often hampers the efficacy of link prediction (LP) tasks. By harnessing the expressive power of Markov logic network (MLN) formulations, multiplex edges can be unified to enhance LP effectiveness. However, scaling up inferences for effective LP remains challenging due to the inefficiency of traditional MLN inference methods. To tackle this issue, we redefine LP tasks within heterogeneous networks using MLN inferences and introduce a tailored inference framework to handle unobserved nodes and complex MLN structures. We propose a method to partition the MLN structure into discrete substructures and compute node label distributions using the variational expectation maximization (VEM) algorithm. Additionally, we establish a termination condition to streamline inference search space and present the MLN-based LP algorithm. Experimental findings demonstrate the efficacy of our VEM-driven MLN inference framework for LP tasks in heterogeneous networks, showcasing superior accuracy compared to existing approaches.  
heterogeneous network; link prediction; Markov logic network; variational expectation maximization Article
# A Review of Recent Research into the Causes and Control of Noise during High-Speed Train Movement
## 4 High-Speed Train Noise Control Methods
### Acoustic Metamaterials Applied to High-Speed Train Noise Control  
After the train speed is increased to 600 km/h, the aerodynamic noise and wheel-rail noise will be significantly increased, and the noise-reduction ability of the traditional extruded aluminum profiles used in the body of high-speed trains, the sound barriers along the railroad line, and the polyurethane foam materials laid inside the car body are all stretched to their operational limit. When traditional materials and structures fail to meet the needs of high-speed train development, acoustic metamaterials with exotic characteristics will become an important means by which to solve the noise problems associated with high-speed trains.
## 4 Discussion
### Comparison with Clinical Study  
In clinical studies, CMA is used to improve molar occlusion, followed by treatment with a wire-bracket appliance. The time required to correct Class II malocclusion was measured to be 5.2 [6] or 6.3 months [7]; that is, the CMA was effective in reducing treatment time. One reason may be caused by the tipping movement of the teeth. However, in the study by [PERSON] et al. [7], the total treatment time including wire-bracket appliance was prolonged probably due to various side effects of CMA, such as extrusion of canines and extra space distal to the lateral incisors. The mechanism of these side effects was clarified by the present FEM simulation.  
Tooth movements produced by CMA have been measured precisely using CBCT [8; 9]. The present FEM model corresponds to the standard CMA in the study by [PERSON] et al. [9]. Their measured values are cited in Table 1 and compared with those in the present FEM simulation, in which distal movement, distal tipping angle, extrusion, and rotational angle in the occlusal plane of the maxillary canines (U3), first premolar (U4), and molar (U6) were measured before and after wearing the CMA for Class II malocclusion. The distal movement and extrusion were measured at the canine cusp, the first premolar buccal cusp, and the molar mesiobuccal cusp. In the clinical study, all values of axial rotation angles were statistically insignificant [9].  
The clinical study showed that the maxillary canines moved with distal tipping, rotation, and extrusion (Table 1). The molars also moved with distal tipping. Such movement types could be simulated by the FEM (Figure 2). The amounts of tooth movements in the FEM simulation were within the range of those in the clinical study (Table 1). However, in the canine, the amount of distal movement and rotation was smaller than those measured in the clinical study. The reason for this difference could not be clarified, but it is probably due to the FEM model of the teeth, which affected the quantitative results in the simulation.
## 2 Equations Derivation of the Direct Calculation Method (DCM)
### Solution for the Support-Ground Interaction under the Elastic Condition  
When the stresses of the surrounding rock at the intrados of the tunnel are in the elastic region (Case I), the analytical solution of the displacement/stress at the equilibrium point (point _E_) can be obtained according to the proposed process of DCM in the elastic medium [40]. Then, the radial displacement (\(u_{R}^{s}\)) and the mobilized support pressure (\(p_{s}\)) can be obtained as:  
\[p_{s}=\frac{k_{s}}{2G+k_{s}}(1-\lambda_{d})\sigma_{v} \tag{3}\]  
\[\frac{2G}{\sigma_{v}}\frac{u_{R}^{s}}{R}=\frac{2G+\lambda_{d}k_{s}}{2G+k_{s}} =\lambda_{s} \tag{4}\]  
where \(G\), \(k_{s}\), \(\sigma_{v}\) and are the shear modulus of the ground, the stiffness of the support, and the vertical stress of the ground, respectively.
## 5 Analysis
### Using Three Counterweights
#### 5.1.3 Numerical Results  
Among the solutions found in the Pareto front, it is possible to select the one that is the most appropriate according to the specific problem that is being solved.  
As an example, three solutions of the Pareto front are taken; the first one is the best result when optimizing the index corresponding to the ShM (\(\beta_{ShM}=0.1600587\), \(\beta_{ShF}=0.9152829\)), the second one is the best result when optimizing the index corresponding to the ShF (\(\beta_{ShM}=0.71311372\), \(\beta_{ShF}=0.00295769\)) and the last one is selected when both ShM and ShF indexes are optimized by almost 60% (\(\beta_{ShM}=0.42969434\), \(\beta_{ShF}=0.45176319\)).  
The first chosen solution is the one in the Pareto front with the minimum value in \(\beta_{ShM}\) (\(\beta_{ShM}=0.1600587\), \(\beta_{ShF}=0.9152829\)). It corresponds to the following variables values:  
\[\begin{array}{ll}x_{c1}=-0.306033474&y_{c1}=-0.05120233&t_{c1}=0.007840031\\ x_{c2}=-0.031811247&y_{c2}=-0.09348429&t_{c2}=0.04\\ x_{c3}=-0.115671647&y_{c3}=0.112686771&t_{c3}=0.04\end{array}\]  
Figure (a)a shows the comparison between the ShF (on the x and y axes) of the original mechanism and the ShF after the optimization. In this case, the total ShF was improved only by 8.47%. But with this solution, it is possible to appreciate that the ShM of the four-bar optimized mechanism is 83.99% better than the original one (Figure (b)b). It can be observed that upon the sole use of counterweights, it is not possible to eliminate the ShM, but it can be significantly reduced, reducing also the ShF a bit.  
Figure 4: Pareto analysis of optimization objectives: \(\beta_{ShM}\) and \(\beta_{ShF}\).  
The second chosen solution is the one in the Pareto front with the minimum value in \(\beta_{SHF}\) (\(\beta_{ShM}=0.71311372\), \(\beta_{ShF}=0.00295769\)). It corresponds to the following variables values:  
\[\begin{array}{llll}x_{c1}=-0.15548064&y_{c1}=-0.007810097&t_{c1}=0.035743454\\ x_{c2}=-0.046683398&y_{c2}=-0.068805445&t_{c2}=0.027404923\\ x_{c3}=-0.132085338&y_{c3}=0.015127099&t_{c3}=0.037640418\end{array}\]  
Figure 5: Shaking Force and Shaking Moment comparison.  
In Figure 5c, it is possible to appreciate that the ShF is significantly reduced (99.70%) compared to the original mechanism. It can be considered that the ShF is almost completely eliminated by using counterweights and also that the ShM has a reduction of 28.69% (Figure 5d).  
The third chosen solution is the one in the Pareto front where both indexes corresponding to ShF and ShM are optimized almost by 60% (\(\beta_{ShM}=0.42969434\), \(\beta_{ShF}=0.45176319\)). It corresponds to the following variables values:  
\[\begin{array}{llll}x_{\rm c1}=-0.233449901&y_{\rm c1}=0.047539111&t_{\rm c1}= 0.005\\ x_{\rm c2}=0.0000527&y_{\rm c2}=0.000296052&t_{\rm c2}=0.005000159\\ x_{\rm c3}=-0.125584323&y_{\rm c3}=0.058770112&t_{\rm c3}=0.04\end{array}\]  
By using these counterweights, the ShM is reduced by 57.03% and the ShF by 54.82% (Figures 5e,f).
###### Abstract  
This study proposes a new damage identification method for hangers of arch bridges using the static deflection difference at the anchorage point of the hanger and the tie-beam. The relationship between the ratio of cable tension loss and the deflection difference at the anchorage point is studied. For the first time, the deflection difference influence matrix for hanger damage identification is defined. The static deflection change parameter is formulated as a function of the deflection difference matrix and the ratio of cable tension loss. The study shows that the percentage of cable tension loss can be obtained from the changes in static deflection at the anchorage point and the deflection difference influence matrix. Therefore, by observing a plot of the ratio of cable tension loss, the damage locations can be identified conveniently. Numerical and laboratory studies show that this method can accurately locate the damaged hanger of through-arch bridges under various scenarios. The proposed damage detection method has a clear theoretical base and is simple to operate, and it is suitable for practical engineering.  
deflection difference; anchorage point; damage identification; hanger; through-arch bridges
# Mathematical Models for Named Data Networking Producer Mobility Techniques: A Review
## 2 NDN Architecture and Mobility Component
NDN mobility encompasses several solutions: anchor-based, anchorless-based, and rendezvous approaches.
# Anonymous Access System with Limited Number of Uses in a Trustless Environment
## 4 The Proposal
### Access Counter Usage Protocol  
Once the access counter creation protocol has been performed, \(\mathcal{U}\) can use the credentials \(n\) times executing the access counter usage protocol, depicted in Figure 3.  
The details of the protocol are as follows:  
1. \(\mathcal{U}\) currently has the secret key, \(sk^{\prime}_{\mathcal{U}}\), and the \(id_{\mathcal{U}}\).
2. Using \(sk^{\prime}_{\mathcal{U}}\), \(r^{\prime}\), and \(id_{\mathcal{U}}\), \(\mathcal{U}\) computes a NI-Schnorr ZKP by doing: \[t_{i}=(i+1)\cdot H(id_{\mathcal{U}})\cdot sk^{\prime}_{\mathcal{U}}+r^{\prime}\] (17)
3. \(\mathcal{U}\) sends \(id_{\mathcal{U}},R^{\prime}\) and \(t_{i}\) to \(\mathcal{SP}\), by calling the Consume service, and \(\mathcal{SP}\) calls a Smart Contract with these values.
4. The Smart Contract retrieves the object indexed by H(\(id_{\mathcal{U}}\)) from storage. If it does not exist, it reverts the execution. Otherwise, it checks: \[t_{i}\cdot P\stackrel{{?}}{{=}}R^{\prime}+(i+1)\cdot H(id_{ \mathcal{U}})\cdot pk^{\prime}_{\mathcal{U}}\] (18)  
Figure 3: Message exchange between \(\mathcal{U}\) and \(\mathcal{SP}\) during the \(i\)-th iteration of the access counter usage protocol.  
Figure 2: Data and call flow between \(\mathcal{U},\mathcal{SP}\) and the blockchain during the access counter creation protocol.  
If the Smart Contract execution is successful, \(\mathcal{SP}\) could provide the service; otherwise, if the execution is reverted, \(\mathcal{SP}\) should deny the service.
# Recent Advances in Laser-Induced Surface Damage of KH\({}_{2}\)Po\({}_{4}\) [PERSON]
## 4 Simulation and Experimental Exploration of Laser-Induced Damage Behavior in the Later Stage
### Exploration of Transient Behavior at the Initiation of Damage
**c1** The distribution of thermal stress due to temperature gradient and expansion; **c2** The deformation of crystal materials under tensile stress [87]. In later stages, the compressive stress generated in the early stages also makes the thermal expansion of the irradiation area on the upper side of the absorption layer larger than the expansion on the lower side. With the increase of the temperature in the irradiation area, the stress state in the material will increase, and the material is completely in an elastic deformation state. For crystal materials, plastic deformation will occur when the temperature of the irradiated area exceeds the threshold that the material can withstand. [PERSON] et al. (2016); [PERSON] et al. (2016) proposed that the existence of plastic deformation will relieve the stress state. At last, the internal temperature of the crystal material will gradually cool down with the end of laser irradiation, while the generated compressive stress can be relaxed and convert to tensile stress ([PERSON] and [PERSON], 2016; [PERSON] and [PERSON], 2016). KDP crystal materials will produce birefringence during laser irradiation due to its anisotropic properties. Owing to the existence of the birefringence effect, the incident laser will be decomposed into two beams with different characteristics. The transmission and focusing characteristics of the incident laser will also be changed accordingly, which will cause an asymmetric distribution of thermal stress in crystals ([PERSON] and [PERSON], 2016; [PERSON] and [PERSON], 2016). Therefore, the residual stress is easily generated during the relaxation of stress in elastic and plastic areas. Figure 19(c2) ([PERSON] and [PERSON], 2016) shows the deformation process of crystal materials under tensile stress. In general, based on the investigation of the transient behavior of damage initiation and the evolution of shock wave, it can be found that the rapid plasma injection under the irradiation of ultra-short pulse laser will cause mechanical damage. However, there is a thermal conduction process under the irradiation of short pulse laser, which will lead to the coexistence of thermal stress and mechanical stress, which has an effect on the material.
# Leveraging Multi-Modal Information for Cross-Lingual Entity Matching across Knowledge Graphs
## 1 Introduction
Secondly, it is difficult for the existing entity matching methods to integrate the representation learning methods from different modalities. Due to the heterogeneity of cross-lingual knowledge graphs, the representations of entities, relationships, attributes and images with the same meaning in different knowledge graphs may be completely different. That means the entity representation learning from each single modality is insufficient and complementary. Therefore, an effective entity matching method requires dealing with this insufficiency and integrating the information from different modalities. To address the above problems, we propose a new cross-lingual entity matching method (called CLEM) using knowledge graph representation learning which integrates the rich multi-modal information. The motivation is that the representation of one entity from each single modality (called view) can only capture the partial entity information, while different views describe different aspects of the entity and may share some common redundant information. Therefore, the entity representations can be learned from each particular view and jointly optimized to improve the representation learning (i.e., entity embedding learning) performance. We first attempt to perform representation learning on relation triples, attribute triples, entity text descriptions and images. Then we apply multi-view intact space learning (MISL) [14] to combine multi-modal information of entities and conduct entity matching on cross-lingual datasets. The main contributions are summarized as follows:  
* We leverage four views including relations, attributes, text descriptions and images to learn entity representations, and each view corresponds to an independent learning model;
* We apply multi-view intact space learning to solve the insufficiency in each individual view and integrate the multi-modal view information to obtain the entity representation in the intact space;
* We perform experiments on cross-lingual datasets and evaluate our CLEM with different evaluation metrics. The experimental results show that the proposed method outperforms the state-of-the-art methods in most evaluation criteria.
## 3 A Recommender of Tourist Activities Based on Clustering and Rule Mining
### Data Collection and Pre-Processing
#### 3.2.2 Identification of Tweets from Tourists  
As one of the aims of this work was to build profiles of tourists and study how they move in a particular city, it was of paramount importance to distinguish the tweets sent by visitors from the tweets sent by local citizens. Twitter does not provide any specific information in this regard.  
To identify the tweets of the tourists, we extended the solution proposed in [15] to consider locations frequently visited by users. We define a tweet as touristic if it was sent from a location other than the user's frequently visited locations or his/her profile location (which the user may indicate by free text in his/her public profile). The frequently visited locations are defined as those cities from which the user posted tweets for at least 20 days, at least one tweet per day.  
Algorithm 1 details the steps performed to determine if a tweet was sent by a tourist. The frequent locations of all users are first determined by counting the number of days a user has tweeted from each place. After that, the coordinates of each geolocated tweet are checked against the set of home locations. If they do not fall within any home location, then the tweets are considered _touristic_. We classify the tweets rather than the users, because a user might be posting as a tourist in one tweet and as a local citizen in another.  
```
1: Let \(T\) be the set of all geolocated tweets;
2: Let \(U:L\) be the set of all users and their profile locations \(U:L=\{u_{1}:l_{1},\ldots,u_{i}:l_{i}\}\);
3: Let \(\alpha=20\) be the threshold of days tweeting from a location;
4:for users in \(U\)do
5: Initialize an empty set of home locations for user \(u_{i}(H_{u_{i}})\)
6: Add user's profile location \(l_{i}\) to \(H_{u_{i}}\), if available
7: Get tweets \(t_{u}\) of user \(u_{i}\) from \(T\)
8: Group tweets \(t_{u}\) by their tweet location
9:if the count of days with tweets in a tweet location exceeds \(\alpha\)then
10: Add tweet location to \(H_{u_{i}}\)
11:endif
12:for tweet in \(t_{u}\)do
13:if tweet location in \(H_{u_{i}}\)then
14: Set tweet as local
15:else
16: Set tweet as touristic
17:endif
18:endfor
19:endfor
```  
**Algorithm 1** Identification of tourists' tweets (pseudocode)