esa-sceva/satcom-chunk-collection huggingface dataset:
# SCM-IoT: An Approach for Internet of Things Services Integration and Coordination
## 4 Case Study
### Conceptual Model  
The requirements that computational models must meet are listed below:  
* Determining at all times the uication (in one of the rooms) of any inhabitant of the house;
* Turn on the heater whenever an occupied room has a low temperature. Turn it off when either the room is no longer occupied or the temperature rises over a threshold;
* Turn on the AC whenever an occupied room has a high temperature. Turn it off when either the room is no longer occupied, or the temperature falls below a threshold;
* Assess possible emergencies such as fire by analyzing the data reported from temperature, smog, and toxic gas sensors installed in each room of the house;
* Determine if the house is on fire if three conditions are met in any room: very high temperature, presence of smog, and presence of toxic gas;
* Turn off the heater, the AC, or both for all rooms if the house is on fire;
* Make urgent calls to the emergency call center, reporting the room in which the incident started;
* Also, determine if the house is inhabited. Report to the rescuers about those rooms where residents were detected since the last received sensor data.  
The development of the SCM-IoT platform-independent computational model consisted of defining the data model and the three storage and editing services.  
The fields describe the data associated with each sensor or actuator installed in a room. The most relevant fields are the following:  
* ID: room identifier;
* Name: usual name of the room;
* Presence: indicates whether a resident is present in the room (YES or NO);
* Temperature: indicates the temperature range in which the temperature detected by the sensor is in the room (LOW, NORMAL, HIGH, VERY HIGH);
* Smoke: indicates whether the corresponding sensor has detected smoke in the room (YES or NO);
* Toxic gas: indicates whether the corresponding sensor has detected toxic gas such as carbon dioxide in the room (YES or NO);
* Heater: indicates whether the room heater is in operation (YES or NO);
* Cooler: indicates whether the air conditioner in the room is operating (YES or NO);
* Alarm: indicates whether the room's audible (and visual) alarm is activated (YES or NO).  
Having described the requirements for the IoT system, a more detailed presentation of the comfort and energy save conditions are given next.
## 4 Experimental Analysis and the Results
### Comparison with Other Classification Algorithms  
In this section, the performance of our proposed algorithm is compared with other similar and familiar machine learning algorithms in order to show its characteristics and what the sites of strength and weakness are within. Random Forest, SVM, and Decision Trees are compared with the proposed algorithm in terms of the model accuracy. Table 5 shows the experimental results of the three classification algorithms including CCA. By examining the results, it appears that the accuracy of the model in all non-linear classification experiments, as in 2-5, is lower than experiment 1, in which all algorithms achieved perfect accuracy in the case of linear classification. The descending order of the algorithms in terms of accuracy was RF, SVM, DT, and CCA. The results also show that the proposed algorithm is affected by the same data distribution problems that affect each of the other  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experiment. No.** & **Observations** & **Features** & **Domain** \\ \hline
1 & 1000 & 3 & synthetic \\
2 & 11,055 & 30 & Phisher URL \\
3 & 5832 & 8 & Healthcare \\
4 & 20,004 & 11 & Healthcare \\
5 & 36,569 & 15 & Healthcare \\ \hline \hline \end{tabular}
\end{table}
Table 2: The dataset description of the experiments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experiment. No.** & **Iteration** & \(K\) = 1 & \(K\) = 2 & \(K\) = 3 & \(K\) = 6 \\ \hline
1 & 50 & 100\% & 100\% & 100\% & 100\% \\  & 100 & - & 100\% & 100\% & 100\% \\  & 300 & - & 100\% & 100\% & 100\% \\  & 500 & - & 100\% & 100\% & 100\% \\
2 & 50 & 77.7\% & **85.4\%** & 81.0\% & 82.7 \\  & 100 & - & 80.7\% & 84.0\% & 83.6\% \\  & 200 & - & 81.7\% & 84.0\% & 83.3\% \\  & 300 & - & 80.4\% & 84.0\% & 83.7\% \\
3 & 50 & 66.7\% & 59.2\% & 59.8\% & 58.6\% \\  & 100 & - & 59.2\% & 61.3\% & 60.3\% \\  & 200 & - & 59.2\% & 61.5\% & 62.7\% \\  & 300 & - & 59.2\% & **63.2\%** & 62.3\% \\
4 & 50 & 59.9\% & 60.0\% & 60.7\% & 60.8 \\  & 100 & - & 60.0\% & 61.0\% & 61.5 \\  & 200 & - & 60.0\% & 61.0\% & **61.8** \\  & 300 & - & 60.0\% & 61.0\% & 61.2 \\
5 & 50 & 61.9\% & 67.3 & 76.9\% & 69.8\% \\  & 100 & - & 68.7 & 76.9\% & 71.9\% \\  & 200 & - & 75.1 & 76.9\% & 71.1\% \\  & 300 & - & 75.1 & **77.2\%** & 75.9\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of CCA with different number of (clusters, iterations).  
three compared algorithms. The model's accuracy is the highest, as shown in the first case experiment, while it came to its least, as in experiment 4, for all experiments.
# Area Division Using Affinity Propagation for Multi-Robot Coverage Path Planning
## 1 Introduction
This paper presents a significant contribution to the field of multi-robot CPP by introducing a novel model for area division predicated on the Affinity Propagation (AP) algorithm. The fundamental premise of the proposed approach is to deconstruct the multi-robot CPP problem into a collection of single-robot CPP tasks, which may or may not be interdependent. While numerous models in the literature adopt a similar divide-and-conquer strategy, our model distinguishes itself in three vital respects:  
1. The proposed approach assigns territory to robots without arbitrarily defining the number of robots (and hence the number of areas). The AP clustering algorithm leverages a weighted similarity index (SI) function to discern similar cells and ascertain the optimal number of robots for the multi-robot CPP task. This effectively liberates the user from the responsibility of determining the appropriate number of robots for the task. To our knowledge, we are the first to incorporate an AP algorithm to address the multi-robot CPP problem. 2. The algorithm proposed in this paper presents a novel perspective on multi-robot area division, where the number of robots and their respective areas are not predesignated arbitrarily by the user. A common drawback of many existing algorithms is their inability to handle scenarios where the robots' initial positions are in close proximity, leading to inefficient area division and path planning. Unlike these approaches, our proposed algorithm demonstrates exceptional resilience to such situations. It intelligently circumvents the constraints of predefined robot counts and positions, thereby offering a more flexible and efficient solution for area division in multi-robot coverage path planning. 3. The similarity function of the AP algorithm takes into consideration several factors, such as layer type, the spatial connectivity of cells, and their \"normalized distance\",before incorporating them into a cluster. This results in reduced area blending rates, meaning that the cells within the final clusters created by this method are less likely to be enveloped by cells from other clusters. The structure of the remaining manuscript is as follows:  
Section 2 of the document introduces similar works from the literature and highlights their advantages and disadvantages. Following that, in Section 3, the formal problem definition is provided. The subsequent section, Section 4, elaborates on the algorithm we propose. Section 5 presents the experimental data and techniques employed to assess the effectiveness of our algorithm. The algorithm's limitations, possible performance improvements, and the usage of parallel computing are discussed in Section 6. Finally, Section 7 contains concluding remarks.
# The Impact of Gravity on Different Height Systems: A Case Study on Mt. Medvednica
## 2 Height Systems
### Physically Defined Height  
#### 2.2.1 Orthometric Heights  
Orthometric height is the curved-line distance measured along the plumbline (Earth's gravity field line) from the geoid to the point of interest. It can be determined by dividing the geopotential number by the integral mean value of gravity for all the points along the plumbline \(\overline{g}\)[2,4]:  
\[H^{ORT}=\ \frac{C}{\overline{g}}. \tag{9}\]  
This means that to define the orthometric height, we would have to measure gravity at every point along the plumbline (Figure 4), which is, of course, impossible, so the true orthometric height system cannot be achieved.  
Since the integral mean value of gravity cannot be measured or calculated, because it requires a detailed model of mass distribution in the area of interest, it is determined based on a hypothesis about the distribution of mass within the Earth. Throughout history, proposals for determining this value have been made by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]. Most countries that are using orthometric height  
Figure 4: Orthometric heights (according to [PERSON] and [PERSON] [4]).  
Figure 3: Dynamic heights (according to [PERSON] and [PERSON] [4]).  
systems today rely on [PERSON]'s approximation, which, for the normal density of Earth's masses \(\rho=2670\) kg m\({}^{-3}\), can be calculated by the following [6]:  
\[\overline{g}=g+0.0424\cdot 10^{-5}H. \tag{10}\]  
where \(\overline{g}\) is expressed in ms\({}^{-2}\), provided that \(g\) is given in the same unit and \(H\) in meters.  
Thus, when discussing geometric leveling, if we want to obtain orthometric heights for all the benchmarks in the leveling line, one method is to first convert the heights of the benchmarks and height differences into the geopotential number system. Then, in the geopotential number system, we perform an adjustment of the leveling line, which yields the geopotential numbers for all the points in the line. Finally, the transition to the orthometric height system needs to be made using Equations (7) and (8).  
Another method for obtaining orthometric heights based on geometric leveling measurements is by applying the following orthometric correction [4]:  
\[OC_{AB}=\ \sum_{A}^{B}\frac{g-\gamma_{0}^{45}}{\gamma_{0}^{45}}\delta n+\frac{ \overline{g}_{A}-\gamma_{0}^{45}}{\gamma_{0}^{45}}H_{A}-\ \frac{\overline{g}_{B}-\gamma_{0}^{45}}{\gamma_{0}^{45}}H_{B} \tag{11}\]  
on the measured height differences:  
\[\Delta H_{AB}^{ORT}=\ \Delta n_{AB}+OC_{AB}, \tag{12}\]  
resulting in orthometric height differences. By performing a least square adjustment using the obtained orthometric height differences, the orthometric heights of all points in the leveling line can be determined.
# Error Recovery in the Time-Triggered Paradigm with FTT-CAN
## 3 System Model
### Schedulability Tests in FTT-CAN and Real-Time Performance  
In FTT-CAN, when scheduling the synchronous messages for the next EC, some idle time may appear at the end of the SW (Figure 2). This Inserted Idle Time (IIT) is essential to allow for the transmission of the TM without any blocking. The actual amount of IIT added in each EC depends on the traffic scheduled for that SW and it can be upper bounded by the length of the longest ready message whose transmission must be postponed to a future EC to avoid a TM overrun, as would be the case of message \(\mathsf{m}_{9}\) in Figure 2. The upper bound of the IIT is denoted by \(X\) in Equation (2). The scheduling model with IIT that is used in FTT-CAN is the Blocking-Free Non-Preemptive scheduling model [26]. Traffic schedulability in this model can be assessed as if the scheduling was fully preemptive, e.g., using common response time analysis (RTA) [27], as long as the message  
Figure 1: Elementary Cycle (EC) and Trigger Message (TM) encoding in Flexible Time-Triggered -Controller Area Network (FTT-CAN).  
transmission times are inflated as in Equation (2), where LEC represents the EC duration and LSW the maximum SW duration.  
\[C_{i}^{E}=\frac{LEC}{LSW-X}C_{i} \tag{2}\]  
Therefore, when considering an FTT-CAN system with a message set \(M\) as in Equation (3) with \(n\) messages characterized by a maximum transmission time \(C_{i}\) (including maximum bit-stuffing), a period \(T_{i}\), and deadline \(D_{i}\leq T_{i}\), schedulability can be guaranteed if an upper bound to message \(i\) response time (\(\mathrm{R_{i}}\)) when considering the inflated transmission time (\(C_{i}^{E}\)), as in Equation (4), is lower than or equal to the respective deadline (\(D_{i}\)) for all \(n\) messages. Equation (4) can be solved with a common fixed-point iteration method and \(\mathit{lpe}(i)\) stands for the set of messages having higher or equal priority than message \(m_{i}\).  
\[M=\{m_{i}(C_{i},T_{i},D_{i}),i=1\ldots n\} \tag{3}\]  
\[\mathrm{R_{i}}=C_{i}^{E}+\sum_{k\in\mathit{lpe}(i)}\left[\frac{\mathrm{R_{i}} }{T_{k}}\right]C_{k}^{E} \tag{4}\]
# CFD-DEM Simulation of the Transport of Manganese Nodules in a Vertical Pipe
## 5 Results and Discussion
### Effect of Particle Diameter
#### 5.2.2 Average Parameters  
As seen in Figure 10, the average liquid velocity attains its maximum at the pipe axis, as is common for the three cases. Particle diameter has a non-negligible effect on the seawater velocity. At \(d_{s}\) = 5.0 mm, the particle number is large, and the energy loss is high owing to frequent interactions between particles and wall. Therefore, the overall flow velocity is lower than the other two cases. However, this tendency is overturned near the wall. Essentially, the frictional loss between the two-phase flow and inner wall of the pipe is alleviated as small particles are transported. Meanwhile, as shown in Figure 10, the particle velocity at \(d_{s}\) = 5.0 mm is low in the central zone but is high near the wall compared to those corresponding to \(d_{s}\) = 15.0 mm and 30.0 mm.  
Due to interactions between particles and between particles and the pipe wall, the distributions of the slip velocity in radial directions are not uniform, which is implied in Figure 11. However, a general trend indicates that the slip velocity decreases with the increase of the radial distance from the pipe axis. The slip velocity for \(d_{s}\) = 5.0 mm decreases sharply near the wall. Overall, the slip velocity decreases as particle diameter increases from 15.0 mm to 30.0 mm, which is in accordance with the conclusion reported in [5].  
Figure 11: Effects of particle diameter on average slip velocity (\(V_{mix,0}\) = 4.0 m/s, \(C_{p,0}\) = 5%).  
Figure 10: Effects of particle diameter on average phase velocities (\(V_{mix,0}\) = 4.0 m/s, \(C_{p,0}\) = 5%).  
The variation of average SVF differs for the three cases with increasing radial position \(r/R\), as evidenced in Figure 12. For particles with a diameter of 5.0 mm, the average SVF distributes uniformly and only declines near the wall. For particles with a diameter of 15.0 mm, the average SVF increases with the radial position and rises near the wall. The distribution of average SVF for particles of 30.0 mm is similar to that for particles of 5.0 mm at the region far away from the wall. It implies that both high local SVF and low local SVF contribute to the uniform distribution of average SVF except in the region near the wall. At the same initial SVF, the average SVF of small particles is far larger than that of large particles, which implies more energy will be lost due to collisions between particles and the wall.
## 2 Geological Background  
The Qinshui Basin is a super-large coal-bearing basin and an important coal base in North China (Figure 1A). The Qinshui Basin was separated into large-scale independent multiple synclines after the Triassic Indosinian orogen and the Jurassic-Cretaceous Yanshan orogen in the North China Craton (NCC), with an axial direction from NNE to SSW.  
Many small faults and folds have developed in the interior of the basin. The directions are NS in the southern and northern parts of the basin and NNE in the central part of the basin. The faults are mainly normal faults in NE, NNE, and NEE directions, and the major faults include the Sitou and Jinhuo Faults (Figure 1B). The Silurian to Lower Carboniferous strata are absent, whereas the Upper Carboniferous Benxi and Taiyuan Formations, Permian Shanxi Formation, Lower and Upper Shihezi Formations, Shiqianfeng Formation, and Triassic strata are continuously deposited. The Taiyuan Formation mainly consists of fine sandstone, siltstone, gritstone, mudstone, limestone, and coal seams (Figure 1C). The Taiyuan and Shanxi Formations are the primary coal-bearing strata, of which the Taiyuan Formation has total and average thicknesses of 62.70-103.52 m and 79.11 m, respectively. This formation contains approximately 8-11 layers of coal seams with an average total thickness of 6.34 m, and the coal-bearing coefficient is 8.01%.  
As a part of the NCC, the study area mainly presents the paleogeographic background of marine continental transition facies in the Late Paleozoic. The northern part of Qinshui Basin is the lower delta plain depositional system, the central and southern part is the barrier island-lagoon depositional system, and the southeastern part is the offshore shelf depositional system [23; 24; 25; 26]. The Gaoping mine is located in the southeast of the Qinshui Basin, belonging to the offshore carbonate shelf depositional facies. Its geographical coordinates are 35\({}^{\circ}\)40\({}^{\prime}\)-36\({}^{\circ}\)0\({}^{\prime}\) N, 112\({}^{\circ}\)40\({}^{\prime}\)-113\({}^{\circ}\)10\({}^{\prime}\) E (Figure 1B). The No. 15 coal from the Taiyuan Formation are the main minable coal seams with the thickness of 0.55-3.89 m, and an average of 2.54 m (Figure 1C).  
Coalification in the southern Qinshui Basin was mainly influenced by high heat flow from magmatic activity due to a tectonic thermal event during the Yanshanian Orogeny, which occurred during the Late Jurassic-Early Cretaceous [27; 28]. The geothermal gradient was up to 6 \({}^{\circ}\)C/100 m [29], which led to the formation of these abnormally high-rank coals.
###### Abstract
The content discovery mechanisms in ICN can be categorized into two major approaches: routing-by-name and lookup-by-name [19]. In routing-by-name, the request is forwarded to an information provider based on the name alone, and then the provider sends the content to the requesting user along the reverse path of the request [20]. However, routing-by-name requires routers to store a large amount of routing states of information objects [21]. Lookup-by-name uses an infrastructure named the name resolution system (NRS) to map the names to locators (e.g., IP address) and then routes the request to one of the locators. In this paper, to solve the problems faced in IP multicast, we proposed an Identifier and Locator Decoupled Multicast (ILDM) routing approach based on the ICN architecture using the lookup-by-name mechanism. The proposed ILDM used globally unique names to identify multicast services and used the NRS to store the mapping between the multicast service name and the locators of the corresponding multicast tree nodes. To avoid core overload and reduce traffic concentration, we designed a dynamic core management and selection mechanism. Routers could register with NRS as candidate cores. NRS maintains the state of each candidate core and dynamically assigns a low-load core to each multicast group. Further, we designed a path state-aware multicast tree node selection mechanism, which dynamically selected a multicast tree node with the best path state for each receiver to join the multicast service. Traffic concentration can be alleviated by using low-load links more efficiently. The simulation results show that our multicast approach had the following advantages compared with other multicast approaches: (1) it reduced the load of each core and achieve load balancing; (2) it reduced the number of join requests that each router needs to handle; (3) it reduced link load and avoided traffic concentration; (4) it reduced the number of multicast flow states per router. The rest of the paper elaborates on the details and performance evaluation of our proposed multicast approach. The remainder of this paper is structured as follows. Section 2 introduces the related researches on IP multicast and ICN multicast. In Section 3, we describe the proposed multicast approach from five aspects: system overview, dynamic core management and selection, member event handling, path state-aware multicast tree node selection mechanism, and comparison of ILDM and PIM-SM. Then, we conduct the experiments for performance evaluation in Section 4. Section 5 concludes this paper and discuss future works.
## 2 Data and Methods
### Ground-Based LST Measurements
However, the measurements of the surface-observing KT-15.85 IIPs contain radiance emitted by the surface (_i.e._, the target signal), as well as reflected downward IR radiance from the atmosphere, which needs to be corrected for [34]. Therefore, at each station, an additional KT-15.85 IIP measures downward longwave IR radiance from the atmosphere at 53\({}^{\circ}\) VZA: measurements under that specific zenith angle are directly related to downward hemispherical radiance [39], so that no ancillary data for deriving ground truth LST are needed. Accurate estimations of land surface emissivity (LSE) are essential for obtaining satellite LST products, but also for limiting the uncertainty of ground-based LST estimates. Especially sites with larger fractions of bare ground are prone to be misrepresented in satellite-retrieved LSEs: Comparisons with _in situ_ LSE revealed that over arid regions, satellite-retrieved LSEs differ by more than 3% [36]. Since for \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Dahra** & **RMZ** & **Gobabeb** & **Evora** \\ \hline \multirow{3}{*}{**Location**} & Senegal & Namibia & Namibia & Portugal \\  & Lat.: 15.402336 & Lat.: \(-\)23.010532 & Lat.: \(-\)23.550956 & Lat.: 38.540244 \\  & Lon.: \(-\)15.432744 & Lon.: 18.352897 & Lon.: 15.05138 & Lon.: \(-\)8.003368 \\ \multirow{2}{*}{**Elevation**} & 90 m & 1450 m & 406 m & 230 m \\  & Tropical Wet-Dry & Steppe & Desert & Mediterranean \\
**TCWV 2010** & 17 to 56 mm & 2 to 28 mm & 1 to 38 mm & 2 to 43 mm \\  & Grassland; & Savanna; & Baren; & Woody savanna with \\
**Vegetation** & 96\% grass, & 85\% grass/soil, 15\% & 32\% tree, 68\% & isolated groups of \\  & 4\% tree & tree & grass & evergreen oak trees \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of KIT’s validation stations. TCWV, total column water vapor. vegetated sites, LSE is a dynamic quantity, we use LSA-SAF's daily LSE to derive _in situ_ LST from the _in situ_ radiance measurements at Dahra (Senegal, tiger bush, 45 m a.s.l. ), Rust Mijn Ziel (RMZ) (Namibia, Kalahari bush, 1450 m a.s.l.) and Evora (Portugal, cork-oak tree forest, 230 m a.s.l.). _In situ_ LST at the desert site Gobabeb (Namibia, gravel plains, 450 m a.s.l.) is derived using a static emissivity obtained from _in situ_ measurements [36].