esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Building Surveillance System as Shared Sensory System for Localization of AGVs
## 2 Test Benches and Methods
### Experiment  
Based on the simulation result we performed the real test, as shown in Figure 5. The objective of the experiment was to determine the absolute accuracy of the detection. Therefore, the gridboard was placed on a static rod, which allowed us to check the detected position with its absolute value that was measured using a precisely placed matrix of points on the floor (white canvas in Figure 5). The rod was moved into 14 different positions, which were 250 mm apart from each other. In every position, 40 measurements were taken for a dataset that was analyzed later. The height of the gridboard above the floor on the simulated robot was the same as the height of the gridboard on the rod in the experiment, 460 mm exactly. The cameras were 2500 mm above the floor. Resolution was set to 1280 \(\times\) 720 px for the cameras in the experiment when it provided the best performance in the simulation, as described in the Results Section. The control PC was running on Ubuntu 16.04 with a CPU of 4 cores at 2.80 GHz, the USB 3 was providing connection to the Intel D435i [34] depth cameras; however, only their RGB image was analyzed. It is important to note that camera calibration process is crucial for the accuracy of the detection, as it is described in [35]. The cameras were calibrated following these instructions.  
Figure 5: Real test: the cameras are highlighted with yellow, blue, and red circles. The gridboard is in a green circle representing the position of an automated guided vehicles (AGV).  
A video with an additional description of the simulation of multiple robots was uploaded on a YouTube channel [36] of the Department of Robotics, VSB-TU of Ostrava. A Python source code of the detection is available as a Github repository [37].
## Materials and Methods
### Study Area
#### Hydrogeological Conditions  
The study area belongs to the first-level tectonic unit of the North China plate and is bounded by the Chifeng-Kaiyuan deep fault. It is divided into two second-level tectonic units, the continental margin of the northern part of North China and the North China block. The platform area is small, and the folds are not obvious. The scale, occurrence, nature, and types of fault structures are complex and changeable. According to their geological types, they are divided into fault basin boundary faults, general faults, and volcanic structure faults. The basin boundary fracture is the main boundary that divides the groundwater system or subsystem. Tensile faults and partial compressive faults in the bedrock area can store water, conduct water, or store or conduct water on one side, and loose layers or shallow overburden areas and rock formations with fractured pore water are the main water-bearing horizons.  
Based on the occurrence conditions and hydraulic characteristics of groundwater, the main types of aquifers (groups) in this area include loose rock pore water, bedrock fissure water, and fracture zone vein water. The spatial distribution of water richness and water quality of each aquifer is quite different.  
Loose rock pore water aquifers are mainly distributed in river valley plains and loess hilly areas. Depending on the lithology, structure, and thickness of the aquifer, their water richness varies in different regions. The first and second terraces on both sides of the Jiaolai River, Yangxu River, Mengke River, Shaolang River, and Laoha River Valley are rich in water. The water in bedrock fissures includes layered rock fissure water and massive rock fissure water. The fissure water of layered rocks is distributed in the low and middle mountains and low hilly areas in the east and middle. The vein-like water aquifers in the fault zone are mainly distributed in the low mountain and hilly area in the southern part of the area. The fault structure is relatively developed, with east-west, north-east, and north-west faults. In the fault structure, several groups of fracture zone rocks are relatively broken. The fissures are developed, the opening is good, and the filling is less.
# Business Impact Analysis of AMM Data: A Case Study
## 5 Results
### BIA Evaluation of the AMM System
On the other hand, the customer system (CS) is only used to inform the customer and should never have more than a medium impact because customer registration in the system is voluntary and if the system drops out it will not affect all customers. For MPC1, the impact of an outage is worse over time, because it has more features that are used remotely and the distributor is required to place MPC1 at customer sites using electricity generation. OMC4 would be least impacted because there will be very few of these sites and they will probably be read at intervals of up to one year, like the existing C-type meters. As far as data loss is concerned, for all systems and applications, 1 h gap in backup will not threaten the organization much. The most problematic aspect is the accounting system (AS), which will have a medium impact on the operation of the company at 24 h. The only system that is not affected by a missing backup or data loss is BTS because no data are stored there and BTS only works to transfer information. The biggest impact on data loss occurs in the Data Centre (DC) and the Reading and Validation System (RVS). If data from any of the systems are compromised, this could have a critical impact on all systems and applications in the company, except BTS and FA because the mobile operator in the form of BTS contains little information about the company's sensitive data. The risk of disclosure is also only moderate for FA field workers, as a field worker usually accesses the data of, at most, a few thousand people per year. In the context of error rate assessment, unit errors of system users or some anomalies in the system or application are not as much of a problem for CEZ Distribuce, a.s. However, if there are system errors, the meters at the points of consumption and the field staff will have medium impacts, while the customer system (CS) has a rather low impact. Other systems are highly impacted by system errors. For deliberate errors, the impact on society ranges from high to critical in most systems and applications, as it can be assumed that such behaviors will always aim to cause as much harm to society as possible.
# Advanced Micro/Nanocapsules for Self-Healing Coatings
## 4 Classification of Self-Healing Coatings Based on the Polymeric Matrix
### Elastomers
#### 4.3.1 Silicone-Rubber-Based Self-Healing Coatings  
Coatings that imparted a self-healing effect based on silicone rubber are a very interesting area of material science. Because of the flexibility and durability of silicone rubber, these coatings are able to repair themselves after damage, such as cuts, scratches, or cracks, that enhances their performance and durability in various applications. Briefly, silicone rubber exhibits excellent elasticity and flexibility, even at elevated temperatures; provides good resistance to UV radiation, many chemicals, and weathering; and has thermal stability.  
Elastic silicone rubbers based on polydimethylsiloxane-co-nickel(II)-pyridinedaxbo-xamide copolymers were developed by the team of [PERSON] et al. [60]. The self-healing capability was attributed to the reversible metal-ligand interactions, and it was estimated at room temperature. The Ni\({}^{2+}\) ions acted as cross-links between the polydimethylsiloxane chains through the coordination of Ni-O and Ni-pyridine bonds. [PERSON] et al. [61] prepared self-healing silicone rubber cross-linked by the multiple hydrogen bonds with ethylene carbonate and \(\alpha\),\(\omega\)-aminopropyl poly(dimethylsiloxane) based on the nonisocyanate reaction. The several hydrogen bonds between the imino and carbonyl groups, together with the generated hydroxyl groups, finally produced a hydrogen bond cross-linked network imparting enhanced thermal-induced self-healing efficiency as well as good cyclic self-healing ability.  
[PERSON] et al. [62] prepared a self-healing coating consisting of polydimethylsiloxane-based polyurea together with an organic antifouling agent. The coating demonstrated good adhesion to the substrate, which was assigned to the breaking and reorganization of the urea group hydrogen bonds. The coating exhibited enhanced self-healing capability in both artificial seawater and air at room temperature. The team of [PERSON] et al. [63] synthesized a polysiloxane-based polyurethane elastomer via a triboelectric effect. The elastomer consisted of urea-oxime-carbamate units in order for the molecular chain to present improved fluidity to enhance reconstruction after damage. Reversible dissociation and reconnection occurred due to the imparting of high mobility to the molecular chains, revealing a self-healing effect. [PERSON] et al. [64] developed a self-repairing polydimethylsiloxane elastomer based on the metal-ligand interaction. The chain of the elastomer had a structure based on 2,2\({}^{\prime}\)-bipyridine-5,5\({}^{\prime}\)-dicarboxamide. The metal-ligand interaction created by the addition of Zn\({}^{2+}\) exhibited kinetic instability, enabling the elastomer to have self-healing ability at room temperature.
# For Patients with Stroke, Balance Ability Affects the Leg Extension Angle on the Affected Side
## 4 Discussions
[56] reported that foot placement relative to the pelvis in the sagittal plane is correlated with the propulsion on the affected side and step length asymmetry, suggesting a link between the leg extension angle and forward propulsion of the body COM during gait. Therefore, training to increase the leg extension angle on the affected side during gait is recommended as an essential component of the rehabilitation strategy targeting the improvement of gait speed in stroke patients. Our findings examined the effects of motor function and balancing ability on the leg extension angle of the affected side, and could assist in the identification of target parameters during training to improve the leg extension angle in stroke patients. Path analysis revealed that the balancing ability had a direct effect on the leg extension angle and gait speed, while MI-LL indirectly affected the leg extension angle through the ability to balance. This contribution of the balancing ability on gait performance agreed with previous studies. [PERSON] et al. [28] reported that the BBS at rehabilitation admission was a significant predictor of community ambulation speed at discharge. In addition, [PERSON] et al. [29] reported that BBS was thought to be the only significant predictor of the 6-minute walk test at discharge. Therefore, stroke rehabilitation to improve the leg extension angle on the affected side during gait would require an improvement in balancing ability in addition to direct gait intervention and recovery of the affected lower limb function. This study had several limitations. First, other factors that may affect overall gait function were not examined. For example, impaired somatosensation of the affected lower extremity and muscle strength of the unaffected side may affect leg extension angle during post-stroke gait. Second, utilization of walking aids, including ankle-foot orthoses or T-canes, could affect the leg extension angle and the ability to balance. A systematic review and meta-analysis showed that an ankle-foot orthosis can affect ankle and knee kinematics in post-stroke gait [57; 58]. Third, the effect of balance exercises on leg extension angle was not analyzed in this study, owing to its cross-sectional nature. Finally, a biased ratio of stroke type compared with the common ratio and a large deviation of the time post-stroke were observed owing to the small sample size [59; 60]. These might have caused some bias in our results and reduced the statistical power. Therefore, further studies addressing the above limitations are necessary to clarify the relationships between the leg extension angle during post-stroke gait and the balancing ability.
# Investigation of Railway Network Capacity by Means of Dynamic Flows
## 3 Optimization Model for Determining a Network-Wide Capacity
### Input Sets and Parameters of the Model
#### 3.2.2 Network-Related Parameters  
Since there is no established method for determining node capacities (cf. Section 1), the railway-specific dependencies in nodes are approximated using two different types of constraints in the model. Each vertex of the core network \(i\in V_{c}\) is assigned a capacity \(\kappa_{i}\in\mathbb{N}_{0}\) and a set of route conflicts \(C_{i}\), which can also be empty. The capacity describes the maximum number of available tracks in a station that can be occupied by stopping trains at the same time. When determining this number, it must be ensured that at least two tracks (one in each direction) are available for trains that pass the station without stopping (e.g. for the purpose of an overtaking), if applicable. The set of route conflicts contains individual route conflicts, each of which applies to all trains that belong to the respective route number. One conflict \(c\in C_{i}\) is structured as \(c=((j,\,n,\,\mu),\,(j^{\prime},\,n^{\prime},\,n^{\prime}))\), where \(j\) and \(j^{\prime}\) are vertices adjacent to \(i\), \(n\) and \(n^{\prime}\) are two route numbers and \(\mu\) and \(\mu^{\prime}\) denote station arrivals or departures (\(\mu,\,\,\mu^{\prime}\in\{a^{\prime},\,/d^{\prime}\}\)). For example, a conflict that is given as \(((p,\,P4,\,^{\prime}d^{\prime}),\,(q,\,F2,\,^{\prime}a^{\prime}))\) indicates that all trains belonging to route number \(P4\) (\(k\in K_{P4}\)) departing from station \(i\) towards station \(p\) are in conflict with all trains that belong to route number \(F2\) (\(k^{\prime}\in K_{F2}\)) arriving in station \(i\) from station \(q\). In a feasible solution of the model (feasible schedule), the routes of two trains \(k\) and \(k^{\prime}\) of each possible train pair \((k,\,k^{\prime})\in K_{P4}\times K_{F2}\) must be set at a certain minimum time interval in station \(i\).  
For the arcs of the core network \((i,j)\in A_{c}\) the parameter \(s_{ij}\in\{0,1\}\) indicates whether the associated line section is a single-track (\(s_{ij}=1\)) or double-track line section (\(s_{ij}=0\)). For double-track line sections, an average minimum headway time \(\overline{h}_{ij}\) plus a minimum buffer time \(b_{ij}\), and an hourly capacity \(u_{ij}\) are defined. For single-track line sections, an hourly capacity \(u_{ij}\), which describes the maximum number of trains that can enter the line section from both directions within an hour is specified. Furthermore, each arc \((i,j)\in A_{c}\) is assigned a set of trains \(K_{ij}\) whose routes contain the arc \((i,j)\), i.e., all trains that will use this arc within the time horizon in case they are scheduled by the model (\(K_{ij}:=\ \left\{k\in K\ \middle|\ (i,j)\in A^{k}\right\}\)).
# Influence of Concrete Strength Class on the Long-Term Static and Dynamic Elastic Moduli of Concrete
## 3 Results and Discussions  
### Static Modulus of Elasticity  
As previously mentioned, the static modulus of elasticity in compression was determined in accordance with currently available standards [13]. The results are presented in Table 2 as the average values of 14 determinations each of these individual values were, in turn, obtained as the average of three measurements [17].  
Similar results were reported on the static modulus of the elasticity of concrete at various ages [18]. There is a rising tendency in the values of the modulus of elasticity with increasing the curing/storage time but it also depends on the curing/storage conditions. Storing the specimens in air, with a low relative humidity, often leads to lower values of the modulus of elasticity compared to those obtained for specimens stored in water for the same time interval. However, it is rather difficult to conduct a direct comparison between the data presented in the scientific literature because of the large variability in the considered concrete mixes, compressive strengths, curing/storing conditions, etc.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Mix Designation**} & **Static Modulus of Elasticity** & \multirow{2}{*}{**Standard Deviation**} & **Coefficient of** \\  & **Elasticity** & & **Variation** \\ \cline{2-3}  & **[MPa]** & **[MPa]** & **[\%]** \\ \hline Mix 1 & 25,214 & 2461.7 & 9.76 \\ \hline Mix 2 & 31,369 & 1563.1 & 4.98 \\ \hline Mix 3 & 33,147 & 1838.3 & 5.55 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Static modulus of elasticity.  
Figure 1: Experimental setup for the assessment of dynamic longitudinal and torsional frequencies of vibration.  
It can be observed that the values of the static modulus of elasticity increased with the increasing of the concrete strength class. The values of the standard deviations and coefficients of variation (COV) show a relatively narrow spreading of the results around the median, with higher accuracies for Mix 2 and Mix 3. Similar values for the two statistical terms were reported in the scientific literature for the hardened properties of concrete and their variation with age [19].
# The Impact of Automated Vehicles on Road and Intersection Capacity
## 2 Methods
### Traffic Flow Basic Map Model  
The basic diagram model of traffic flow describes the relationship among traffic volume, lane density, and vehicle speed, and is a basic model in traffic flow theory and traffic engineering. The basic traffic flow map model can be used to calculate the basic road capacity, and has a priori theoretical guiding significance for analyzing the impact of the permeability of autonomous vehicles on the capacity of roads and signalized intersections in the future man-machine mixed environment.  
The density calculation formula is shown in (4). Road density reflects the number of vehicles in the lane and the degree of traffic congestion. The higher the density, the more crowded the section.  
\[K=\frac{N}{L}, \tag{4}\]  
where \(K\) representation density, \(N\) represents the number of vehicles on the road, and \(L\) represents the road length.  
The relationship between the three parameters can be expressed by Formula (5).  
\[Q=vK, \tag{5}\]  
where \(Q\) is the traffic volume, \(v\) is the speed.  
In 1934, [PERSON] proposed the \"speed-density\" model in his paper [30], in which the \"flow-density\" model and the \"flow-velocity\" model could be obtained. These three models are the core of the basic diagram model of traffic flow. Among the relationships among flow, density, and speed, the relationship between flow and density is the most widely used, and it is also the main research object of this paper.  
Using the three-parameter basic relationship of traffic flow (5), along with [PERSON] speed-density relationship (6), the flow-density relationship can be derived, as shown in Equation (7).  
\[v=v_{f}(1-\frac{K}{K_{j}}), \tag{6}\]  
\[Q=Kv_{f}(1-\frac{K}{K_{j}})=v_{f}(K-\frac{K^{2}}{K_{j}}), \tag{7}\]  
where \(v_{f}\) represents free travel speed, and \(K_{j}\) represents blocking density.  
It can be seen from Equation (7) that the \"flow-density\" model used in the traditional manual driving environment, is a quadratic function.
###### Abstract  
The transboundary river basin is a great challenge for water management and disaster reduction due to its specific characteristics. In this study, upstream impacts from natural and artificial sources on the downstream discharge on the Imjin river basin, the well-known transboundary region in the Korean peninsula, were evaluated using a hydrological model integrating a dam operation module at an hourly timescale. The module uses a concept of the AutoROM method as the operational rule to update the dam storage and decide water release. Dam storages were translated into water levels using a water level-storage curve. To quantify the impact of hydraulic structures on the Northern Imjin river basin, change in discharge was analyzed in four flood events (2009, 2010, 2011, and 2012). Dam failure scenarios were developed under conditions of the 2010 flood event, in which the releases of 100%, 80%, 50%, and 20% of water storage of Hwanggang dam were simulated. The results indicate that the amount of water released from upstream dams is the main cause of floods in the downstream region. To reduce the risk of floods in the downstream river basin, an optimal dam operation module and information on upstream dams play an important role and contribute to the effective use of water resources.  
**Keywords:** transboundary basin; flood risk; dam failure; Imjin basin; river management +
Footnote †: journal: Applied Sciences
# Deformation of Air Bubbles Near a Plunging Jet Using a Machine Learning Approach
## 2 Material and Methods
### Algorithms
#### 2.3.3 Bagging and Random Forest  
A regression tree essentially consists of a decision tree used as a predictive model [54]. Each internal node represents an input variable, while leaves correspond to specified real values of the target variables. The development of a regression tree consists of a recursive procedure in which the input data domain is divided into subdomains, while a multivariable linear regression model is employed to obtain predictions in each subdomain.  
The recursive tree growth process is carried out by splitting each subdivision into smaller branches, considering all the possible splits in every field and detecting, at each step, the subdivision in two separate subsets minimizing the least squared deviation, defined as:\[R(t)=\frac{1}{N(t)}\sum_{i\in t}\left(y_{i}-y_{m}(t)\right)^{2} \tag{10}\]  
where \(N(t)\) is the number of sample elements in the \(t\) node, \(y_{i}\) is the value of the target variable in the \(i\)-th element and \(y_{m}\) is the average value of the target variable in the \(t\) node. This sum estimates the \"impurity\" at each node. The algorithm stops when the lowest impurity level is reached or if a different stopping rule is encountered.  
The grouping of multiple learning models leads to an ensemble method, which may increase the forecast accuracy. The outcomes of different regression trees can be combined to get a single numeric result, for example, through a weighted average. Bootstrap aggregating, also known as Bagging, a ML algorithm introduced by [PERSON], is based on such an approach: numerous training datasets of the same size are randomly picked, without replacement, from the original dataset in order to build a regression tree for each dataset. Different predictions will be provided by different regression trees, since small variations in the training dataset may lead to significant changes in ramifications and in outcomes for test instances. Bagging attempts to counteract the instability of the regression tree development process by varying the original training dataset instead of sampling a new independent training set each time: some instances are replaced by others. Finally, the individual predictions of the different regression trees are averaged.  
The Random Forests algorithm is similar to the Bagging algorithm, but the procedure by which regression trees are built is different. Each node is allocated without considering the best subdivision among all the input variables, but by randomly choosing only a part of the variables to split on. The number of these variables does not change during the training process. Each tree is developed as far as possible, without pruning, until the minimum number of instances in a leaf node is reached.  
In this study, the minimum number of leaf elements has been chosen to be equal to five, while each forest consists of 1000 trees.