esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 VLC System Model
### Distance between the Receiver and the LEDs  
The aim is to obtain the distance between the LED \(i\) and the medical device. To achieve this goal, it is essential to note that the position of each LED is known. Let \((x_{t},y_{t},z_{t})\) be the location of the medical device that is currently unknown. Thus, the distance from LED \(i\) to the receiver can be expressed as  
\[d_{i}=\sqrt{(x_{i}-x_{t})^{2}+(y_{i}-y_{t})^{2}+(z_{i}-z_{t})^{2}}. \tag{10}\]  
Moreover, considering the right triangle formed by the distance from the height of the receiver to the position of the LEDs on the ceiling of the chamber, the relationship can be expressed as  
\[\cos{(\phi_{i})}=\frac{z_{i}-z_{t}}{d_{i}}. \tag{11}\]  
The target position must necessarily include all three dimensions. However, achieving accurate three-dimensional (3D) localization poses challenges in terms of algorithm complexity or reduced precision [44]. To simplify the calculations, it is assumed that the receiver in the device is positioned at a known height \(h\) relative to the floor of the room. By combining Equations (9) and (11), the following expression is obtained:  
\[P_{LOSi}=\frac{A_{ri}(m_{i}+1)}{2\pi}T_{si}(\psi_{i})g_{i}(\psi_{i})P_{ni}* \frac{(z_{i}-z_{t})^{m_{i}+1}}{d_{i}^{m_{i}+3}}. \tag{12}\]  
Rearranging Equation (12), we obtain the following expression:  
\[d_{i}=(\frac{A_{ri}(m_{i}+1)}{2\pi}T_{si}(\psi_{i})g_{i}(\psi_{i})P_{ni}\frac{ (z_{i}-z_{t})^{m_{i}+1}}{P_{LOSi}})^{\frac{1}{m_{i}+3}}. \tag{13}\]  
It is observed that the equation depends solely on the received power at PD \(i\).
## 3 Results and Discussion  
### Effect of Washing Times  
The results of ESEM-EDS are shown in Table 3. A small amount of fluorine was detected in the residual sands after twice washing. This means that there still remained ILs in the residual sands. However, the fluorine was not detected in residual sands after washing for 3 or 4 times, suggesting no ILs anions were left in the residual solids. Figure 3 presents the results of infrared spectrum of the residual sands with different washing times after [Bmim][BF\({}_{4}\)]-enhanced oil sands extraction. The residual sands without water washing displayed a peak at 1550-1600 cm\({}^{-1}\), which is the characteristic peak of the in-plane C-C of the imidazole ring. This proved the remaining of  
Figure 2: Schematic of the removal of ionic liquids from residual sands by washing–ion exchange combined process.  
ILs cations in the residual sands. However, it was found that this peak was absent for the residual sands which were washed for 3 and 4 times, indicating that there were no cations of ILs in the residual sands. The strong absorption band near 1100 cm\({}^{-1}\) is the C-N stretching vibration of the cations of ILs, whereas it is also the characteristic peak of silicates in the residual sands. From the above results of ESEM-EDS and FTIR, it can be concluded that washing the residual sands for at least 3 times is required to remove ILs to aqueous solution completely. The ILs concentration in the filtrate after 3 times water washing was detected to be in the range of 8.02-10.69 g/L. Consequently, the concentration of the model ILs solution was determined to be 10 g/L.
## 2 Materials and Methods  
For the rest of the paper, we assume that the kernels are separable, i.e.,  
\[K_{1j}(x,t)=s_{j}(x)h_{1j}(t),\quad K_{j}(x,t)=g_{j}(x)h_{j}(t),\quad j=1, \ldots,m,\]  
where \(s_{j}(x)\), \(g_{j}(x)\), \(h_{1j}(t)\), \(h_{j}(t)\in X\), and write the integro-differential Equation (1) in the symbolic form  
\[Bu=Au-S\Phi(A_{1}u)-G\Psi(Au),\quad D(B)=D(A), \tag{3}\]  
where the vectors of functions  
\[S=\left(\begin{array}{cccc}s_{1}&\cdots&s_{m}\end{array}\right),\quad G= \left(\begin{array}{cccc}g_{1}&\cdots&g_{m}\end{array}\right),\]  
and the vectors of functionals, for \(w(x)\in X\),  
\[\Phi(w) = \left(\begin{array}{c}\Phi_{1}(w)\\ \vdots\\ \Phi_{m}(w)\end{array}\right)=\left(\begin{array}{c}\int_{\Omega}h_{11}(t) w(t)dt\\ \vdots\\ \int_{\Omega}h_{1m}(t)w(t)dt\end{array}\right),\] \[\Psi(w) = \left(\begin{array}{c}\Psi_{1}(w)\\ \vdots\\ \Psi_{m}(w)\end{array}\right)=\left(\begin{array}{c}\int_{\Omega}h_{1}(t) w(t)dt\\ \vdots\\ \int_{\Omega}h_{m}(t)w(t)dt\end{array}\right).\]  
It is noted that \(\Psi(G)\) denotes the \(m\times m\) matrix  
\[\Psi(G)=\left(\begin{array}{ccc}\Psi_{1}(g_{1})&\cdots&\Psi_{1}(g_{m})\\ \vdots&\ddots&\vdots\\ \Psi_{m}(g_{1})&\cdots&\Psi_{m}(g_{m})\end{array}\right),\]  
where the element \(\Psi_{i}(g_{j})\) is the value of the functional \(\Psi_{i}\) on the element \(g_{j}\). It is easy to show that for an \(m\times k\) constant matrix \(N\),  
\[\Psi(GN)=\Psi(G)N.\]  
We recall that a linear operator \(T:X\to X\) is _injective_ if for every \(u_{1},u_{2}\in D(T)\), \(u_{1}\
eq u_{2}\) implies \(Tu_{1}\
eq Tu_{2}\). The operator \(T\) is _surjective_ if \(R(T)=X\). If \(T\) is both injective and surjective, then the operator is called _bijective_ and there be the inverse operator \(T^{-1}:X\to X\) defined by \(T^{-1}f=u\) if and only if \(Tu=f\) for each \(f\in X\). The operator \(T\) is said to be _correct_ if it is bijective and the inverse operator \(T^{-1}\) is bounded on \(X\). The problem \(Tu=f\) is correct if the operator \(T\) is correct.  
Finally, we will use several times the following Theorem, which has been shown in [21] and it is recalled here but with a different notation tailored to the needs of the present article.  
**Theorem 1**.: _Let \(X\) be a complex Banach space, \(A:X\to X\) a bijective linear operator, \(G=(g_{1},\ldots,g_{m})\in X^{m}\), \(\Psi=col(\Psi_{1},\ldots,\Psi_{m})\in[X^{*}]^{m}\), and \(T:X\to X\) the linear operator_  
\[Tu=Au-G\Psi(Au),\quad D(T)=D(A).\]  
_Then the following statements are true:_  
1. _The operator_ \(T\) _is bijective on_ \(X\) _if and only if_ \[\det W=\det[I_{m}-\Psi(G)]\
eq 0,\] _and the unique solution to boundary value problem_ \(Tu=f\)_, for any_ \(f\in X\)_, is given by the formula_ \[u=T^{-1}f=A^{-1}f+A^{-1}GW^{-1}\Psi(f).\]
2. _If in addition the operator_ \(A^{-1}\) _is bounded on_ \(X\)_, then_ \(T\) _is correct._
# A Mobility Management Using Follow-Me Cloud-Cloudlet in Fog-Computing-Based RANs for Smart Cities
## 2 Related Works
### Related Works
[25] proposed a mobility-based services migration prediction (MSMP) scheme by addressing the trade-off between the overhead and Quality of Experience (QoE). [PERSON] et al. [26] further considered the trade-off by modeling the service migration issue by using a Markov Decision Process (MDP). Mobility management results using the concept of follow-me edge (FME) is studied in [9]. [PERSON] et al. [9] realized the FME concept by enforcing an autonomic creation of Mobile Edge Computing (MEC) service to allow the data access with the optimum QoE and the reduced latency. To realize the FME, the Edge Orchestrator (EO) of the mobile network operators (MNOs) needs to update the resource information and the user location information. Some novel results of cloud and fog services to vehicular networks had developed [28; 29], [PERSON] et al. [28] proposed an energy-efficient adaptive resource management for real-time vehicular cloud services. [PERSON] et al. [29] proposed an energy-efficient adaptive scheduler for Vehicular Fog Computing (VFC) that operates at the edge of a vehicular network. Some results of 5G mobile networks with caching are provided [30; 31; 32; 33]. [PERSON] et al. [30] investigated the key challenges and current trends of 5G mobile networks. [PERSON] et al. [31] developed a joint optimization of cloud and edge processing for fog radio access networks. [PERSON] et al. [32] developed a backhaul-aware caching placement for wireless networks. [PERSON] et al. [33] developed a cloud radio access networks with coded caching.
# Cooperative Caching and Resource Allocation in Integrated Satellite-Terrestrial Networks
## 4 Problem Formulation  
In this section, the joint optimization problem is formulated to minimize the weighted sum of storage resource usage and network bandwidth consumption, and then, the problem analysis is provided.
## 3 Results and Discussion
### Characterization of MGONCs
#### 3.1.4 VSM  
The saturation magnetization (MS) was applied to evaluate the magnetic properties for Fe\({}_{3}\)O\({}_{4}\) nanoparticles. The result of VSM (shown in Figure 7) confirmed the ferromagnetic property for both Fe\({}_{3}\)O\({}_{4}\) and MGONCs. Typically, the MS values for Fe\({}_{3}\)O\({}_{4}\) nanoparticles prepared with the conventional chemical methods ranged from 30 to 80 emu/g, with most values being less than 60 emu/g. The saturation magnetization of Fe\({}_{3}\)O\({}_{4}\) nanoparticles was measured to be 43.2 emu/g. Notably, MGONCs possesses the magnetic property (19.8 emu/g), which indicated the easy separation advantage by an external magnet instead of the conventional techniques that consume a long time [38].  
Figure 6: FTIR characterization chart of Fe\({}_{3}\)O\({}_{4}\), GO, and MGONCs.  
Figure 7: Magnetic hysteresis loops of Fe\({}_{3}\)O\({}_{4}\) and MGONCs.  
Indeed, compared to Fe\({}_{3}\)O\({}_{4}\) particles, the saturation magnetization (MS) value of MGONCs was noticeably lower. This reduction in magnetization could be attributed to the decreased content of magnetic Fe\({}_{3}\)O\({}_{4}\) in the composite material. However, it was worth noting that the magnetic properties of MGONCs were still sufficient for effective separation using a magnetic separator. The strong magnetism of MGONCs allowed for the easy and rapid separation within 2 s from an aqueous solution.
## 5 Experiment Results and Discussion
### Contrasting Physical Experiment Results against Finite Element Analysis Outcomes
#### 5.3.1 Model Development  
Previous studies showed that sandstone materials involved in the compression experiment share common mechanical properties and constitutive relations with autoclaved aerated concrete (AAC) [28]. Thus, an eight nodded linear brick (C3D8R) element was adopted in this study for the numerical simulation on the sandstone specimen [29]. As seen from the above, sandstone failure occurs at the last moment of the compression process, and before failure, the related deformation shows elastic characteristics. Therefore, only the elastic model was used in the finite element simulation based on Abaqus.
## 3 Results and Discussion
### Production Time and Cost Analyses
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Main Part** & **Support** & **Total** & **Main Part** & **Support** & **Total** & **Total Fixed** & **Total** \\  & **Volume** & **Volume** & **Volume** & **Total Cost** & **Total Cost** & **Cost** & **Cost** & **Indirect Cost** & **Direct Cost** \\ \hline AT53G80 & 256.0 & 120.46 & 376.46 & 116.75 & 54.94 & 171.69 & 144.52 & 26.69 & 0.48 \\ CT53G80 & 256.0 & 115.49 & 371.49 & 118.31 & 53.37 & 171.68 & 144.52 & 26.69 & 0.47 \\ PT53G80 & 256.0 & 113.11 & 369.11 & 119.07 & 52.61 & 171.68 & 144.52 & 26.69 & 0.47 \\ AT33G80 & 256.0 & 924.3 & 348.43 & 126.12 & 45.53 & 171.65 & 144.52 & 26.69 & 0.44 \\ AT80G80 & 256.0 & 142.63 & 398.63 & 110.27 & 61.44 & 171.71 & 144.52 & 26.69 & 0.51 \\ AT33G100 & 256.0 & 81.10 & 337.10 & 130.24 & 41.29 & 171.63 & 144.52 & 26.69 & 0.43 \\ AT16G100 & 256.0 & 115.41 & 371.41 & 118.33 & 53.35 & 171.68 & 144.52 & 26.69 & 0.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The cost calculation results (cost: USD; volume: mm\({}^{3}\)). Figure 9: The melt pool depth of the IN718 LPBF parts fabricated on top of support structures with different types (AT53G80, CT53G80, PT53G80) and geometries (AT 33G80, AT80G80, AT33G100, AT60C100). Among seven samples, AT80G80 has the highest total cost of $171.71 and the highest total support cost of $61.44; AT80G80 demonstrates the lowest level of Vickers hardness (as shown in Figure 9). This indicates an interesting opportunity for improving the hardness level and reducing cost simultaneously. In addition, different types of the support structure (AT53G80, CT53G80, and PT53G80) show dramatically different levels of hardness (from around 330 to around 460, indicating almost 40% improvement) but they have similar total costs (from $171.68 to $171.69). In other words, adjusting the type of support structure could achieve better hardness \"for free\". Next, we focus on the same support structure but different geometries, i.e., different thicknesses (AT53G80, AT33G80, and AT80G80; AT33G100 and AT60G100) and different gap (AT33G80 and AT33G100). Samples with different thicknesses demonstrate evidently different levels of hardness and different total support costs while having similar total costs. For example, AT80G80 ($61.44) has around 35% more total support cost than AT33G80 ($45.54), and AT80G80 also demonstrates a lower hardness (around 325) than AT33G80 (around 380). On the other hand, samples with different gaps have a similar level of hardness and similar costs. AT33G80 and AT33G100 have similar averaged hardness (around 380) with different standard deviations, and similar total cost and total support cost. Figure 11: The total cost distribution between the main part and support structure for seven specimens. Figure 10: The cost distribution for specimen AT53G80.
# Affinity Propagation Clustering Using Path Based Similarity
## 2 Method
### Affinity Propagation Clustering Using Path-Based Similarity
```
Input: S: negative Euclidean distance based similarity between \(n\) samples \(p<0\): preference value of a sample chosen as the cluster center (or exemplar) \(\lambda\): damping factor for APC \(k\): number of clusters Output: \(clust(i)\): the cluster membership for sample \(i\in\{1,2,\cdots,n\}\)
1: Initialize \(\mathbf{A}=\mathbf{0},\mathbf{R}=\mathbf{0}\) and set each diagonal entry of \(\mathbf{S}\) as \(p\). 2: Iteratively update \(\mathbf{R}(i,j)\), \(\mathbf{A}(i,j)\) and \(\mathbf{A}(j,j)\) using Equations (5)-(7), respectively. 3: Identify exemplars based on the summation of \(\mathbf{A}\) and \(\mathbf{R}\). 4: If the number of exemplars is larger (or smaller) than \(k\), then reduce (or increase) \(p\) and goto line 1. 5: Compute the path-based similarity matrix \(\mathbf{P}\) using Equations (8) and (9). 6: Assign non-exemplar sample \(i\) to the closest cluster (i.e., \(e\)) based on \(\mathbf{P}\) and set \(clust(i)=e\). ```  
**Algorithm 1**APC-PS: APC Based on Path-Based Similarity  
Figure 1: A toy example: 202 samples distributed in two curves. “e1” and “e2” are the exemplars of the lower cluster and upper cluster, respectively.
# Hadoop vs. Spark: Impact on Performance of the Hammer Query Engine for Open Data Corpora
## 4 Map-Reduce: Approach and Frameworks
### Hadoop vs. Spark  
Currently, the two most-popular open-source frameworks for executing Map-Reduce processes are _Hadoop_ and _Spark_.  
_Hadoop_ is the first popular Map-Reduce framework. The reader can refer to [27] as a complete guide, to understand technicalities of the framework. Here, we summarize its main features. It has been designed to be a general framework suitable for various types of Big Data applications, possibly based on iterative algorithms. Two layers compose _Hadoop_:  
* a data storage layer called _Hadoop Distributed File System_ (HDFS) [28], which provides storage support to process, both for input data and for intermediate results;* a data processing layer called _Hadoop Map-Reduce Framework_, which distributes and coordinates computation on the cluster; the basic component, which actually handles task execution, is named _YARN_ (Yet Another Resource Negotiator) [29].  
Data in HDFS are organized in blocks; single blocks are distributed to Map tasks (for each block, a processing task is created), as well as they are dispatched to one or many Reduce tasks. Iterative algorithms perform multiple _Hadoop_ Map-Reduce flows; data are shared through HDFS: input data of each iteration are read from HDFS, as well as output data must be written back to HDFS, for each iteration. Consequently, the large amount of disk accesses and data exchanges through the network cause a significant overhead that is paid in terms of execution times. In this respect, many works, such as [9; 30; 31], studied the behavior of iterative algorithms within _Hadoop_.  
_Spark_[11] is a more recent Map-Reduce framework than _Hadoop_. It has been designed [32] to overcome the issue exhibited by _Hadoop_ in terms of high execution times, in particular for iterative algorithms, caused by the fact that the only way to share data is through the distributed file system HDFS.  
The abstraction provided by _Spark_ to overcome this issue is the concept of _Resilient Distributed Dataset_ (RDD). It is a block of data that is shared to processing tasks through the main memory of computational devices in the cluster. Computational flows are performed as described hereafter.  
* Input data are transferred from persistent memory to main memory, subdivided in many RDDs; these RDDs are read-only for next tasks.
* Computational tasks are performed by _worker nodes_; they access RDDs and generate new RDDs;
* A RDD is resilient because the framework keeps track of the process that has generated it. In case of loss of its content (due to space limitations in main memory), the framework rebuilds the RDD, by re-executing the tasks that have generated the lost RDD.  
This way, _Spark_ is expected to be much more efficient than _Hadoop_, in particular for iterative algorithms, provided that the main memory actually available on the cluster is sufficient to store all RDDs. The reader can refer to [12] for a comparative analysis of performance with _Hadoop_, where a Page Rank algorithm is used as a testbed.
# Simultaneous Indoor Tracking and Activity Recognition Using Pyroelectric Infrared Sensors
## 3 Sensor Node Design
### Sensing Model  
Our sensing model originated from the reference structure tomography [25]. The illustration of our sensing model is shown in Figure 1. The monitored space is the space where the human target moves and performs different activities. The measurement plane is the plane where the PIR sensors are located. The reference structures are located between the monitored space and measurement plane and are used to modulate the visibility of the sensor array.  
Assume that there are \(M\) PIR sensors in the measurement plane, and the monitored space can be divided into \(L\) sampling cells. The visibility of the same sampling cell to the sensor array is identical [26]. Thus, the output of the PIR sensor array can be represented in vector form as:  
\[\mathbf{m}=\mathbf{Vs} \tag{1}\]  
where \(\mathbf{m}=[m_{j}(t)]\in\mathbb{R}^{M\times 1}\) is the output vector of the PIR sensor array and \(m_{j}(t)\) the output of the \(j\)th PIR sensor. \(\mathbf{s}=[s_{i}(t)*H(t)]\in\mathbb{R}^{L\times 1}\) is the radiation status vector of the sampling cells; \(H(t)\) denotes the impulse response function of the PIR sensor; \(*\) is the convolution operator; \(\mathbf{V}=[v_{ji}]\in\mathbb{R}^{M\times L}\) is the measure matrix. If the \(i\)th sampling cell is visible to the \(j\)th sensor, \(v_{ji}=1\); \(v_{ji}=0\) otherwise.  
Thus, the thermal variation of the monitored space is represented by the output of the PIR sensor vector. The reference structure plays the key role of radiation embedding; the spatio-temporal characteristics of the human target activities are cast into the low-dimensional sensor data stream. Because the PIR sensors can only detect the movement of thermal objects, non-thermal objects such as balls or infrared remote controllers will not trigger the PIR sensors.
## 5 Performance Results of RACD Strategy
### Performance Results
#### 5.2.1 The Impact of the Priority Level on Performance  
Figure 4 shows the effect of the priority level (\(k\)) on the performance of the resource allocation strategy in terms of hit ratio and hop reduction ratio. We can see that, when the priority level \(k\) increases, the results of the university data (UD-RA) decrease, and the results of the other data (OD-RA) increase. Here we observe that, from 0.1 to 0.4, there is an increase in the hit ratio and in the hop reduction ratio for university data compared to non-university affiliated data. The reason is that, as the priority coefficient increases, the space given to non-university data increase until it reaches nearly the same size for both types of data when \(k=0.4\). Starting from \(k=0.5\), We can see a clear superiority for OD-RA reaches almost 90% as a difference in the hit ratio, and also for the hop reduction ratio. Notice that, as the \(k\) coefficient increases, the space allocated to OD-RA also increases. The reason why OD-RA takes more storage space compared to UD-RA even with the priority coefficient \(k\) in the ranges from 0.5 to 0.9 is because there is a large difference between the amounts of data consumed in the two classes, as the amount of data that belong to the university represent only 4% of the total resource consumption. This, in turn, has an impact on space allocation.
# Traffic Sign Detection Based on the Improved YOLOv5
## 3 Experimental Validation and Result Analysis
### Quantitative Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **P/\%** & **R/\%** & **mAP/\%** & **FPS/(f/s)** \\ \hline YOLOv5 & 73.2 & 74.2 & 75.7 & 26.88 \\ SSD300 & 76.5 & 62.3 & 76.3 & 14.1 \\ Faster R-CNN & 69.8 & 53.6 & 80.9 & 0.6 \\ Zhu & - & - & 81.6 & 5.8 \\ YOLOv3 & 26.3 & 37.9 & 58.1 & 29.6 \\ YOLOv4 & 54.0 & 64.6 & 74.2 & 41.7 \\
[25] & - & - & 75.2 & 31.3 \\ Ours & 81.9 & 77.2 & 82.0 & 30.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the improved algorithm with other algorithms. As shown in Table 3, compared to the SSD300 and Faster R-CNN algorithms, the improved algorithm in this paper is improved more obviously. The mAP of the Faster R-CNN algorithm is 80.9%, the accuracy P is 69.8%, and the recall R is 53.6% when detecting traffic signs in the TT100K dataset. Compared to the Faster R-CNN method, this algorithm has a better detection rate.