esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Collision Avoidance Algorithm
### Obstacle Detection and Responsive Distance Modeling  
The object detection workflow begins by computing a minimum representative distance from each angular segment of the sensor module FoV. As described in Section 2.3, each distance sensor provides a matrix of 3D distance data across multiple zones in its field of view. To reduce computational load while preserving directional awareness, the system processes these transformed 3D measurements by selecting the minimum distance value in each column of the sensor's zone matrix. Each column corresponds to a vertical slice (angular segment) of the FoV, and the minimum value in that column represents the closest obstacle point detected in that segment.  
Specifically, for each sensor, the algorithm selects the minimum distance from each column of the matrix (multi-zone data) corresponding to the closest point along each angular segment. Let this minimum distance or distance measured from the nearest obstacle be denoted as \(D_{n}\), where \(D_{n}\) is the minimum measured distance in the \(n\)-th angular segment across all sensors. This scalar distance is then used as input to the Gaussian-based repulsive force model for real-time obstacle avoidance. Figure 13 illustrates the general overview of object detection and representation by the developed system. In this figure, no object is detected within the safety zone represented by threshold \(R_{th}\). Consequently, no repulsive forces are generated, the total repulsive force is zero (\(\mathbf{F}_{t}=0\)), and the drone continues following the user command.
## 2 Preliminaries and Problem Formulation
### Problem Formulation  
The investigated NMR is a typical two-wheel driven mobile robot as shown in Figure 1. In this paper, we consider a group of \(N\) two-wheeled mobile robots. The velocity of each of the two driving wheels (\(v_{il}\) and \(v_{ir}\)) can result in linear velocity \(v_{i}=(v_{ir}+v_{il})/2\) and angular velocity \(\omega_{i}=(v_{ir}-v_{il})/(2b_{i})\), with the half distance between two wheels being \(b_{i}\) (for \(i=1,\cdots,N\)). The positions of the robot \(i\) are denoted by \((s_{xi},s_{yi})\) and the orientation is \(\theta_{i}\). Then, the kinematics model of each robot can be described by  
\[\dot{\mathbf{x}}_{i}=\left[\begin{array}{c}s_{xi}\\ s_{yi}\\ \dot{\theta}_{i}\end{array}\right]=\left[\begin{array}{c}v_{i}\cos\theta_{i }\\ v_{i}\sin\theta_{i}\\ \omega_{i}\end{array}\right] \tag{1}\]  
where \(\mathbf{x}_{i}=[s_{xi},s_{yi},\theta_{i}]^{T}\) is the state variable, \(r_{i}\) is the radius of the wheel.  
Figure 1: A two-wheeled nonholonomic mobile robot.  
By applying the equation \(v_{i}=\omega_{i}r_{i}\), the following equation can be further obtained:  
\[\dot{\textbf{x}}_{i}=\frac{r_{i}}{2}\left[\begin{array}{cc}\cos\theta_{i}&\cos \theta_{i}\\ \sin\theta_{i}&\sin\theta_{i}\\ 1/b_{i}&-1/b_{i}\end{array}\right]\left[\begin{array}{c}w_{ir}\\ w_{il}\end{array}\right] \tag{2}\]  
In order to study the formation control of nonholonomic mobile robots, assumptions about the communication between multi-agents and system parameters are given in the following:  
**Assumption 1**.: _The upper and lower bounds of the value range of parameters \(r_{i}\) and \(b_{i}\) are known, i.e., there exist some known positive constants \(\bar{r}_{i}\), \(\underline{r}_{i}\), \(\bar{b}_{i}\) and \(\underline{b}_{i}\) such that \(\underline{r}_{i}<r_{i}<\bar{r}_{i}\) and \(\underline{b}_{i}<b_{i}<\bar{b}_{i}\)._  
**Assumption 2**.: _Assume that the directed graph \(\mathcal{G}\) is connected and the leader is the root of the spanning tree._  
**Lemma 1**.: _Based on Assumption 2, the matrix \((L+U)\) is nonsingular where \(U=diag\{\mu_{1},\cdots,\mu_{N}\}\). Define_  
\[\bar{q}= [\bar{q}_{1},\cdots,\bar{q}_{N}]^{T}=diag(L+U)^{-1}[1,\cdots,1]^{T}\] \[P= diag\{P_{1},\cdots,P_{N}\}=diag\{1/\bar{q}_{1},\cdots,1/\bar{q} _{N}\}\] \[Q= P(L+U)+(L+U)^{T}P,\]  
_then \(\bar{q}_{i}>0\) for \(i=1,\cdots,N\), \(Q\) is positive definite._
## 4 II-37 and IL-38, Broad Inhibitors of Skin and Joint Inflammation  
### Anti-Inflammatory Role of IL-37 in Skin and Joints  
Although IL-37 is not a direct inhibitor of IL-36 cytokines, [PERSON] and colleagues demonstrated that, through its binding to IL-1R8, IL-37 could limit TLR-, IL-1-, IL-18-, IL-33-, and IL-36-mediated inflammation [29]. Indeed, IL-1R8 interacts with and usurps molecules such as IRAK or TRAF6, involved in the downstream signaling of TLR and IL-1 family member cytokines, eventually limiting the activation of the signal. Not surprisingly, IL-1R8 deficient mice display a hyperinflammatory phenotype, are more susceptible to psoriasis, and develop more severe arthritis [84].  
The critical role of IL-37 in inhibiting skin inflammation has been identified and described in human and animal models of psoriasis. IL-37 is less expressed in psoriatic skin lesions compared with non lesional skin [85; 86], and can downregulate the production of key mediators like IL-8, IL-6, or S100 calcium-binding protein A7 (S100A7) involved in the development of psoriasis in mice [87]. These results suggest that the exogenous replenishment of IL-37 represents a promising therapeutic strategy for patients with psoriasis.  
Although data in this field are somewhat limited, IL-37 seems to be also involved in preventing joint inflammation. Intra-articular injections of recombinant (rh) IL-37 or adenovirus encoding human IL-37 in mice with collagen-induced [88] or streptococcal cell wall (SCW)-induced arthritis [89] drive the downregulation of locally produced IL-17 and other Th17-related cytokines and ameliorate arthritic symptoms.  
Furthermore, [PERSON] and colleagues recently discovered that IL-37 could inhibit osteoclastogenesis [90]; this ability is particularly relevant for its potential therapeutic use in RA. Indeed, the typical bone erosions observed in RA patients are driven by the activation of osteoclasts and represent a major cause of pain and disability [91]. Somehow, unexpectedly, IL-37 levels in plasma and PBMCs in patients with RA are significantly higher compared with healthy controls [88] and correlate with the presence of activated T cells and the disease activity [92].  
Conversely, the level of expression of IL-37 within the synovium of RA patients is not dissimilar from healthy controls [89].  
Figure 3 (left panel) summarizes the primary roles and functions of IL-37 in human and mice skin and joints.  
Even if further studies are needed to clarify the divergent expression of IL-37 in the circulation and within the synovium, overall, these data suggest that IL-37 might be exploited not only to treat psoriasis but also RA. To date, nothing is known about the direct role of IL-37 in PsA pathophysiology. The lower circulating levels of IL-1R8 observed in PsA patients might suggest a protective role [93], but additional studies are called to clarify these aspects.
# Minimizing Resource Waste in Heterogeneous Resource Allocation for Data Stream Processing on Clouds
## 4 Experimental Results
### Experimental Environment  
MRG was implemented in Apache Spark, which is an open-source distributed computing framework with the master-slave architecture. Docker [43] was used to execute Apache Spark on three IBM Blade Center HS22 servers. The native orchestration of Docker Swarm [44] was used for the management of container clusters across servers. The master node on one server was launched using Docker Swarm and a container running as the worker node was launched on other two servers. To provide the heterogeneity of runtime environment, one container had 16 CPUs with 8 GB RAM, and the other had 8 CPUs and 16 GB RAM.  
Two applications were used in the study, Spark Pi and Spark PageRank. These applications have distinct benchmarks. Spark Pi is a classic MapReduce framework and a CPU-intensive computing application for big data analysis in Apache Spark. More CPU resources are required to improve the computing efficiency of Spark Pi. Spark PageRank is a typical page ranking algorithm in which a relative score of the importance of website pages is evaluated. To cache a large set of intermediate data in memory, Spark PageRank is memory intensive and the most critical effect on performance is memory utilization. To measure system performance, the overall completion time was evaluated under various combinations of application resource demands and server capacities.
## 3 Computed Tomography and Mitral Valve
### CT Assessment of the Mitral Apparatus for Transcatheter Procedures
#### 3.3.2 Predicting LVOT Obstruction  
The LVOT lies between the mitro-aortic continuity and the interventricular septum. The placement of a transcatheter mitral valve prosthesis could extend and narrow the LVOT (neo-LVOT) due to anterior deflection of the AML [82]. Indeed, LVOT obstruction is a significant cause of mortality and morbidity following TMV replacement procedures (TMVR) [83]. CT can predict the size of anticipated neo-LVOT, thus predicting potential obstruction. A recent study demonstrated that a CT neo-LVOT area measuring 1.7 cm\({}^{2}\) or smaller predicts LVOT obstruction with a sensitivity of 96% and a specificity of 92% [84]. Notably, the neo-LVOT size is also dynamic; for this reason, it is usually measured throughout systole.  
Notably, LVOT changes the cardiac cycle; therefore, this measurement is made in systole. Several device- and patient-specific factors could influence the size of the neo LVOT. The former include stent protrusion, flare, and skirt size [85]. A more ventricular position of the mitral prosthesis, defined as a ventriculo-atrial offset of 80:20 (i.e., 80% of the device is positioned on the ventricular side and the remaining 20% on the atrial side), increases the risk of neo-LVOT obstruction. On the other hand, patient-specific characteristics include aortomitral angle, basal septal thickness, and anterior mitral leaflet length. The aortomitral angle is defined as the internal angle between the central axis of the mitral and aortic annulus; in particular, the risk of obstruction increases when the measurement of this angle approaches or exceeds 90\({}^{\circ}\). Another factor influencing the risk of obstruction is the thickness of the interventricular septum, particularly a value greater than 14 mm [86]. Finally, longer anterior leaflet lengths (>30 mm) are associated with the possibility of neo-LVOT obstruction [87]. All these parameters should be carefully evaluated before proceeding with percutaneous mitral valve replacement to reduce complications and improve procedure success.
# Modeling of Water Losses in Hydraulic Tunnels under Pressure Based on Stepwise Regression Method
## 1 Introduction
In addition, the goal was to validate this methodology by building an MLR model of water losses with greater accuracy and reliability, compared to previously developed models of water losses. The case study was carried out for the hydraulic tunnel at PSHPP \"Bajina Basta\", located on the Drina River, which represents one of the most significant hydropower structures in the Republic of Serbia for the production of electrical energy. The different approaches based on statistical and numerical methods that were discussed have their advantages and their shortcomings. Numerical methods are extremely demanding and complex, and they require the determination of several parameters. On the other hand, they enable filtration and stress-strain analyses of tunnels including the interaction with the rock mass. Statistical methods are widely used for the monitoring and assessment of various hydrotechnical structures (majorly dams). They are based on MLR techniques, and there are certain issues in defining the type and number of regressors and their dependencies. In the case of new methodologies that are based on the application of PCA and stepwise regression methods, it is possible to define the most influential regressors for the variable that is modeled (total losses). Besides that, compared to other models, the effect of the crack width is taken into account as it has great influence on the total losses, as does the time component.
# Exploring Ephemeral Features with Ground-Penetrating Radar: An Approach to Roman Military Camps
## 3 Results
### Alto da Raia  
The GPR survey focused on a small area on the eastern side of the camp. The defences showed a gap visible on the ground and recognisable in the LiDAR and UAV-derived digital elevation models. This phenomenon was believed to relate to the presence of a gate here.  
Archaeological features start to be visible in the GPR data at 40 cm below the surface (Figure 6). A line marking the outline of the defences is visible, albeit not very clearly, perhaps due to the decomposition of the upper archaeological deposits in connection with heavy, mechanical ploughing. The inner rampart and the berm separating it from the external ditch are visible at a depth of 60-80 cm. Excavations in the area confirmed the latter. At this point, an unmistakable camp feature comes to light: a _clavicula_ (5.7 m radius, 10.34 m long inner arch and an outer arch of approximately 14.5 m) (Figures 7 and 8). The homogeneous filling could be interpreted as the inner part of a V-shaped ditch dug to increase the site's defensibility and extract materials to build the rampart.  
The rampart's core and the ditch are still visible at 80-100 cm below surface level. In addition to these two structural elements, we could identify a line running parallel to the rampart's core underneath the previously defined ditch at a depth of ca. 120 cm. The reflection amplitude suggests that this could be the compacted bottom of the trench. Even lower levels do pinpoint this feature. At lower depths, the feature is surrounded by heterogeneous reflections, most possibly belonging to the decomposition of the bedrock. Therefore, we conclude that archaeological layers are located only 1.5 m below the ground level.  
The positive results obtained at Alto da Raia are due to the overall good preservation of the camp's structures. The absence of modern cultivation with mechanised ploughing in this area permitted these structures to survive. The rocky slate and granitic environment described above affect the quality of the obtained GPR images since the decomposition of the rock introduces considerable noise to the depth slices. This is especially noticeable when compared with other study cases where fine grain sediments prevail, i.e., Villa Maria or Andinales (see Section 3.3). Nevertheless, the results were generally positive and allowed a new interpretation of the camp.
# On the Use of Focused Incident Near-Field Beams in Microwave Imaging
## 2 Motivation  
Incorporating unknown undesired scatterers in the inversion process requires that we (i) include them as extra unknowns in the inversion algorithm, and subsequently (ii) ensure that more scattering data are collected to compensate for the resulting increase in the number of unknowns. However, we may not be able to meet these two conditions in certain cases. To understand this better, it is important to discuss the \"sufficient\" amount of data needed for successful inversion. To this end, we begin by reviewing how the MWI problem, which is an inverse scattering problem, is related to the inverse source problem. (In the inverse source problem, the goal is to find the equivalent currents that generate the measured electromagnetic fields.) Once this relation is established, the amount of information needed for successful inversion can be better understood from NF antenna measurements' point of view. We then address how the choice of the NF distribution can be utilized advantageously to suppress the scattering from undesired scatterers in an attempt to alleviate the necessity of including them as extra unknowns in the inversion process.
## 3 Methodology  
Water samples were collected from 48 wells (Table 1) in November 2018; Figure 1 shows the well location map of the study area. Samples were collected in polyethylene bottles for geochemical and isotopic analyses. pH and electrical conductivity (EC) were measured in the field. Electrical conductivity was measured using AD-410 ADWA testers; pH was measured using the AD-11 ADWA model; testers were calibrated twice daily during the field campaign. Dissolved major-ion analyses, including anions (Cl, SO4, and HCO3) and cations (Ca, Mg, Na, and K), were conducted at the Desert Research Center, Water Central Laboratory, Cairo, Egypt. Total dissolved solids (TDS) were estimated using the calculation method. Major cations and anions were analyzed in groundwater samples according to [39; 40]. Carbonate (CO3\({}^{2-}\)) and bicarbonate (HCO3\({}^{-}\)) levels were determined via titration against H2 SO4 using the neutralization method, using phenolphthale as an indicator for CO3\({}^{2-}\) and methyl orange as an indicator for HCO3\({}^{-}\). Chloride (Cl\({}^{-}\)) levels were determined vol- untertically via titration against AgNO3 using K2 CrO4 as an indicator. Calcium (Ca\({}^{2+}\)) and magnesium (Mg\({}^{2+}\)) levels were determined via titration against Na2 EDTA using a complex metric method. Calcium levels were determined using a murexide indicator; magnesium levels were estimated by subtracting the calcium values from the (Ca\({}^{2+}\) + Mg\({}^{2+}\)) values after determining them using Eriochrome Black T in the presence of a suitable buffer solution. A Flame Photometer (PFP 7, Jenway, London, UK) was used to determine sodium (Na\({}^{+}\)) and potassium (K\({}^{+}\)) levels. Sulfate (SO4\({}^{2-}\)) levels were determined using the turbidity method using a UV/Visible Spectrophotometer, Unicam UV 300 (Thermo Spectronic, Waltham, MA, USA). The calculated e Error\(\%=[\sum\)Cations \(-\sum\)Anions\(]/[\sum\)Cations \(+\sum\)Anions\(]\) was less than 5%.  
Stable isotopic analyses for \(\delta\)18O and \(\delta\)2H were analyzed at the Stable Isotope and Radiocarbon Units, Institute of Nanoscience and Nanotechnology (INN), National Centre for Scientific Research \"Demokritos\" on a continuous flow Finnigan DELTA V plus equipped with a Gas bench device (Thermo Electron Corporation, Bremen, Germany) stable isotope mass spectrometer. Results are expressed in the standard notation, delta per mil (5%), for both Oxygen (\(\delta\)18O) and deuterium (\(\delta\)2H).  
Figure 3: N-S hydrogeological cross-section along traverse A-A’ of Figure 1, based on subsurface lithological data modified from [24; 37].  
Data obtained from the chemical analyses were used as input data for NETPATHXL [41] inverse geochemical modeling. NETPATHXL is a computer program that uses inverse geochemical modeling techniques to calculate net geochemical reactions that can account for changes in water chemistry between initial and final evolutionary waters along the flow path [15; 42].
## Materials and Methods
### Prediction Models  
A total of 787 soil sampling points was extracted from the NDVI data using the Spatial Analyst tool box of ArcGIS 10.4. After the abnormal values were rejected by the rule of 38 criteria (eliminate \"outliers\") [57], 678 sampling sites (not under cloud cover) were chosen from 787 soil samples, which completely belonged to cultivated land, to develop the SOC prediction models. The 678 soil samples were divided into the training dataset that comprises 407 (60%) samples and the validation dataset that consists of 271 (40%) samples.
## 3 Problem Description and Proposed Solution
### Method of the Proposed Approach
#### 3.2.1 O&M Knowledge Graph Construction  
This section describes the general construction method of an O&M knowledge graph of a microservice-based system in a cloud environment and explains how to analyze, extract, store, and mine the relevant O&M knowledge.  
O&M knowledge includes O&M subjects and their relational attributes, data monitoring, and historical O&M knowledge data. Historical O&M knowledge data are used for restoring the system O&M information at a specific timestamp. By comparatively analysing data at different time points, it is convenient for O&M personnel to see the distribution and change of resources, and, to a certain extent, it plays an important role in anomaly diagnosis and investigation.  
A knowledge graph can be represented by a resource description framework (RDF) [26], which is a subject, predicate, and object as shown in Equation (1), and sometimes called a statement:  
\[\xrightarrow{\textit{{Subject}}}\qquad\xrightarrow{\textit{{ predicate}}}\qquad\textit{{Object}} \tag{1}\]  
An RDF consists of nodes and edges. Nodes represent entities and attributes, and edges represent the relations between entities and entities and the relations between entities and attributes. RDF data can be stored after serialization. RDF serialization includes RDF/XML, N-Triples, Turtle, RDFa, and JSON-LD. We used RDF/XML in our study.  
O&M subjects include software and hardware and their running statuses. Software includes services, microservices, middleware, storage services, databases, and containers. Hardware includes a computer room, a cluster, a server physical rack, a virtual machine, a container, a hard disk, and a router. The characteristics of O&M subjects can be mined from the monitoring data, which commonly include metrics, log events, and tracing data. Figure 2 is a simple sample of a knowledge graph of an MSA system. There is a \"calling-called\" predicate relation between microservices, a \"hosting-hosted\" predicate relation between microservices and containers, and a \"locating-located\" predicate relation between containers and physical servers.  
Figure 1: Framework of the proposed method.  
Anomalies are usually observed from some monitoring indicators. For microservices, response and latency times are important key performance indicators (KPIs). We can extract more static attributes or KPIs from other subjects such as memory usage and cache-related KPIs for the physical server and container, and this information could be also represented on the knowledge graph, as shown in Figure 3.  
Section 4 describes the construction of the O&M knowledge graph for an MSA application in the Kubernetes environment.