esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Wireless Communication Technologies
### Long-Range Communication Technologies
#### 3.2.3 Fifth Generation (5G)  
Following 2G (GSM), 3G (UMTS), and 4G (LTE/WiMAX), fifth generation, or 5G, is the latest generation of cellular mobile communication. Its prominent features include a high data rate, reduced latency, energy saving, enhanced system capacity, and ubiquitous connectivity. The International Telecommunication Union (ITU) foresees the launch of 5G mobile networks by 2020. Such systems will have, per user, a speed of 100 GB/s with a capacity stretching as much as 1000 times [23]. Owing to such features, 5G technology is poised to play a critical role in UAV communication systems and, thus, pave ways for novel applications. For instance, in the case of UAVs in a 5G environment, less dynamic network segments of the FANET architecture could be interconnected with the core network. This will ease the provision of services such as surveillance multimedia streaming. The requirement of backhauling for seamless connectivity, however, still eclipses the inherent clarity of UAVs in a 5G network [24; 25; 26]. Actions such as connecting UAVs with a base band unit (BBU) and involving a macro-cell base station (MBS) do ensure fulfillment of the backhauling requirement.  
Further, the connectivity does vary depending on the factors related to service provision and network planning. In [27], the authors proposed a multi-layer hierarchical architecture with distributed features that smoothly enables the integration of UAVs with next-generation wireless communication networks. Moreover, authors in [28] provided an overview on some of the recent research endeavors in UAV communication systems that addresses 5G techniques from the perspectives of the physical layer, the network layer, and joint communication, computing, and caching. The case where UAVs carry base stations (BSs) for providing 5G network connectivity in rural settings is explored in [29].
## 2 Methods
### Method for Determining Early Warning Parameter Values  
#### 2.2.1 Velocity Calculation in CDS  
The accurate determination of \(v_{m}\) is essential to the calculation of the normalized tangent angle, the core of which is the determination of CDS. Most related studies [5; 10; 14] depend on empirical determination, which is unreliable. We propose a quantitative calculation method to determine the CDS accurately, which is based on the characteristic that the acceleration in the CDS approaches 0. First, collect all the data acquired from the moment that the deformation of a landslide monitoring station breaks through \(S_{ia}\) until the current moment, and then divide the period into several sections evenly, and use Equation (8) to calculate the acceleration of each section to get an acceleration sequence. Finally, the longest sub-sequence that is less than the threshold (generally 1) is obtained. The start and end time of the sub-sequence is the start and end time of the CDS.  
After determining the CDS, \(v_{m}\) can be calculated by the following three methods:  
Average value method (AVM): Divide the entire CDS into several parts and calculate the velocity of each part. The average value is taken as \(v_{m}\).  
Regression fitting method (RFM): The least-squares method is used to fit Equation (6) to the entire CDS, and the fitting coefficient \(a_{1}\) is \(v_{m}\).  
Gaussian kernel density estimation method (GKDEM): Scan the entire CDS with a certain time window, and use the least-squares method to calculate the velocity in the time window to form a speed sequence \(v=\{v_{0},v_{1},\cdots,v_{n}\}\). Then use the following formula to calculate the kernel density estimation function:  
\[\hat{f}_{h}(v)=\frac{1}{nh}\sum_{l=1}^{n}K(\frac{v-v_{i}}{h}) \tag{14}\]  
where \(\hat{f}_{h}(v)\) is the kernel density estimation of the distribution subject to the variables; \(h\) is the window width, and \(K\) is the kernel density function. Generally, the Gaussian kernel function is used:  
\[K(v)=\frac{1}{\sqrt{2\pi}}e^{-\frac{v^{2}}{2}} \tag{15}\]  
The velocity at which the kernel density estimate is the maximum is \(v_{m}\).
## 4 Results  
The results are generated using real-world, known occurrences of CVT failure, arcing, and DFR sampling error events within an operational power transmission system. All three events are voltage-related; only the voltage waveforms are used. However, the presented IFP approach can be used for current waveform manifesting events. The voltage waveforms are sampled at \(\hat{f}_{s}\) = 960 Hz or 16 SPC. The residual, RMS, and frequency histograms are used for CVT failure, arcing, and DFR sampling error events' IFP, respectively. The other histograms are neglected because their corresponding metrics did not provide sufficient actionable information to enable IFP. Table 1 lists each use case, the histogram used for IFP, the IFP time before the DFR-triggering event occurs, the amount of Random Access Memory (RAM) required to store the histogram types, and the processing time for each IFP calculation. The histograms are generated using data collected over a twenty-four-hour period. Three CVT failure and arcing events are present in the DFR-recorded data, and their presence is confirmed by power utility personnel. The DFR-triggering events are marked using vertical red lines in Figures 4 and 5. To enable comparative assessment and determine the minimum amount of time over which histograms must be generated to facilitate IFP, histograms are generated every five, ten, thirty, and sixty minutes. When comparing the memory required to store the unprocessed, raw waveforms to our histogram-based approach, the amount of memory needed to store the histograms is 95.65%, 97.69%, 99.18%, and 99.58% lower when generating them every five, ten, thirty, or sixty minutes, respectively. The memory and processing times, as shown in Table 1, for array-based IFP calculations require 131 kB of RAM memory and 6.19 \(\upmu\)s of Central Processing Unit (CPU) time. The vector-based metrics calculations require only 4 kB and 1.63 \(\upmu\)s. The results show that actionable information is retained while the data size is significantly reduced.  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Use Case** & **Metric Used** & **Lead Time** & **Memory Used (kB)** & **Processing Time (μs)** \\ \hline CVT Failure & Residual Histogram Correlation & 1 h & 131 & 6.19 \\ Loose Connection (Arcing Event) & RMS Histogram Variance & 7 h & 4 & 0.76 \\ DFR Sampling Error & Frequency Histogram Tolerance & N/A & 4 & 1.63 \\ \hline \end{tabular}
\end{table}
Table 1: Table listing each use case, the corresponding histogram used for the IFP of the selected use case, and the IFP approach’s predicted time before the DFR-triggering event occurs.  
Figure 4: _Use Case #1: CVT Failure Event IFP:_ The average per-sample cross-correlation calculated between the reference residual histogram and the residual histograms generated for a twenty-four-hour period. Histograms are generated every five, ten, thirty, and sixty minutes.  
Figure 5: _Use Case #2: Arcing Event IFP:_ The absolute difference in RMS histogram variance between two 161 kV transmission lines for twenty-four hours. Histograms are generated every five, ten, thirty, and sixty minutes.
# Estimating PQoS of Video Conferencing on Wi-Fi Networks Using Machine Learning
## 5 Related Work
They collected packet loss ratio, network throughput, and RTT as QoS parameters, while utilizing the Skype quality meter feature to categorize calls as being of good, medium, or poor quality. Their results achieved 66% accuracy when estimating quality classes using the decision tree algorithm [23]. The review of the literature presented here indicates the need for a more efficient quality assessment approach than external measurements provided because existing approaches may be inconvenient and increase the operational cost of data analytics for ISFs. As newer 802.11 standards have commercialized, the literature continues to design and test their quality assessment approach exclusively for legacy Wi-Fi networks. In addition, the literature has often utilized small datasets to train ML algorithms, which can cause dataset overfitting or underfitting and may produce results that do not extend well to unseen conditions. Finally, existing approaches collect all measurements on the service provider side, which raises privacy concerns regarding data storage, access, and analytics purposes. This study presented a novel approach to address the abovementioned shortcomings by using ML techniques trained with 802.11-specific network performance parameters on the edge of networks in the form of edge computing.
## 3 Application of the Design Approach to the Definition of the Innovative Test Rig
### Design of the General System ('Test Rig')
#### 3.1.1 Conceptualization of the General Idea of a System (\(\rightarrow\) Step 1.a)
According to this subdivision, the tests required to characterize the stability of a vehicle will therefore be of two types:'static' (motionless vehicle) and 'dynamic' (vehicle moving along a circular trajectory with a constant speed). These two scenarios can be further investigated by considering other variables, e.g., the angle between the vehicle longitudinal axis and the support plane maximum slope direction, the angulation of the steering wheels, any additional mass if required (frontal/rear/lateral mounted implements, carried payloads). A special case could be represented by all those situations that involve a spatial relocation of the vehicle masses in relation to the support polygon, e.g., due to the presence of: a moving loader on the front, a moving backhoe on the back, a moving crane boom with a suspended load, or a moving manipulator arm. In this case, although the inertial force would still have an effect, with the kinematic condition on the CoG used as classification key we can still consider the situation as static. As will be discussed in the following paragraphs, the proposed classification is fundamental for stating the type of tests that the test rig will be able to perform, and hence its architecture. Finally, to complete the possible scenarios of overturning, the elastic properties and plasticity limits of the terrain must be considered since it can compact unevenly under vehicle load, and the support offered to the vehicle below each tire or track can fail, thus worsening the situations described in each of the three presented scenarios. Figure 4: Forces acting in the transversal plane on a vehicle involved in a turning maneuver upon an inclined surface (according to the vehicle frame of reference); \(F_{g}\) is the gravitational force and \(F_{c}\) is the inertial force (specifically: the radial component of inertial force, i.e., the centrifugal force). It is possible to demonstrate that the condition of incipient side-overturning, corresponding to a null total reaction force on the contact point on the lifting side (point \(B\)), corresponds to the passage of the line of action of the force R (resultant of only the forces applied to the center of gravity) for the contact point \(A\) (hence: \(d_{crit}=a\) or \(C\blacksquare A\)). Therefore, it is possible to state that overturning occurs when the direction of the resultant of the forces acting on the vehicle center of gravity intercepts the support plane outside of the support polygon (defined by the footprints of the vehicle supports).
## 3 Results and Discussion  
Radiated EME measurements in the frequency range from 150 kHz to 30 MHz were conducted for 34 different system combinations, resulting in 570 spectra. A selection of these measurements is presented in this article, representing the major findings from these measurements. Results are presented with the help of a system summary graph and frequency histogram.
## 5 Cloud and Bandwidth Resource Allocation Problem
### NFVI-PoP, Network and Traffic Model  
We represent the NFVI-PoP and network infrastructures with the graph \(\bar{G}=(\bar{V},\bar{L})\), where \(\bar{V}=\bar{V}_{A}\bigcup\bar{V}_{S}\bigcup\bar{V}_{NFVI}\) is a set of nodes that represents the access nodes in which the Service Function Chains are generated/terminated (\(\bar{V}_{A}\)), the network switches (\(\bar{V}_{S}\)) and the NFVI-PoPs (\(\bar{V}_{NFVI}\)). \(\bar{L}\) is the set of edges interconnecting the nodes of the set \(\bar{V}\) and models the network links interconnecting the  
Figure 3: A segment routing based data/control plane for the scalable NFV orchestration architecture.  
NFVI-PoPs, access nodes and switches. We denote with \(C_{\tilde{e}}\) the capacity of the link \(\tilde{e}\in\bar{L}\) while \(c_{b}\) is the cost unit (Dollars/KmGb) of carrying one traffic Gb on any link of length 1 Km.  
The NFVI-PoPs host the cloud resources needed to implement the network service. In this paper, we consider only processing resources, though our study can be easily extended to the case in which memory and disk resources are also considered. We assume that every NFVI-PoP is equipped with a total number \(N_{core}\) of processing cores. Virtual Machines (VM) are activated in the NFVI-PoP to instantiate VNFIs supporting service functions as Network Address Translation (NAT), firewall (FW), Load Balancer (LB), etc. If we denote with \(F\) the number of service function (SF) types, we assume that the VNFI hosting the \(i\)-th (\(i=1,\cdots,F\)) SF is implemented with a software module providing the processing capacity \(C_{i}^{pr,max}\) and requiring the allocation of \(n_{i}^{c}\) cores [7]. Next, we denote with \(c^{core}\) the cost unit (Dollars/hour) of renting for one hour one processing core.  
We assume that the ISP has to route and to allocate resources for \(N\) SFCs--each one characterized by a 5-tuple \(<\bar{v}_{i},\bar{w}_{i},M_{i},\bar{s}_{i},b_{i}>(i=1,\cdots,N)\) where:  
* \(\bar{v}_{i}\in\bar{V}_{A}\) and \(\bar{w}_{i}\in\bar{V}_{A}\) denote the originating and terminating access nodes the SFC;
* \(M_{i}\) is the number of service functions to be executed for the SFC according to a given order; we assume that the SFs belong to a set of \(F\) types;
* \(\bar{s}_{i}\) is a 1's or 0's matrix of size \(M_{i}\times F\); the component \(s_{i}(j,k)\) assumes the value 1 if the \(j\)-th SF to be executed is a \(k\)-th type SF;
* \(b_{i}\) is the bandwidth offered by the \(i\)-th SFC.
# A Survey on Efforts to Evolve the Control Plane of Inter-Domain Routing
## 4 Issues in the Evolution of the Inter-Domain Routing Control Plane
### Conflicts and Uncertainty in Inter-Domain Routing Policies  
The BGP incorporated routing and policy requirements into one seamless protocol. For inter-domain routing, each BGP router has its own view of the network state and applies routing policies based on its local configuration. This led to the concept of the routing policy, which can be understood as how routing decisions are made to compose the network reachability state [16]. The difference between routing and policy control is that the first is more related to an engineering effort and the second is strictly about business [35]. Both functions are embedded into the BGP current protocol version, despite the fact that its original design was only for routing purposes.  
Moreover, different types of ASs deploy different types of routing policies into their domains, and that diversity of policies can eventually lead to conflicted ones. An example is the application of inbound traffic engineering techniques using the BGP techniques. The main idea of an AS to control its inbound traffic is to fulfil the AS' businesses interests. However, applying the BGP mechanisms for inbound traffic control (such as _Selective advertisement_ or _AS-path prepending_[30]) is a counter-productive approach because it is not possible to guarantee the effectiveness of the desirable results.  
In fact, the BGP techniques try to influence the routing decisions of external ASs, in order to obtain their desired inbound traffic distribution. Hence, a network operator that uses those approaches leads to a trial-and-error process, since other ASs can easily ignore or withdraw the BGP _suggestions_ coming through BGP update messages. Indeed, the problem of inter-domain routing traffic engineering can be seen as a conflicting one, in which the interactions between ASs can be modeled as game theory and nonlinear programming [30].  
Besides, it is very tricky to discover what the network traffic distribution of the inter-domain environment will be when policies between domains have to change. The prediction of how much bandwidth will be consumed or how the traffic will be distributed when a domain desires to change its inter-domain routing policies for a given prefix are difficult to estimate [40]. Therefore, proposals to evolve the control plane of inter-domain routing have to provide schemes, mechanisms or protocols to overcome the BGP limitations to express new and refined policies that avoid conflicts and uncertainty in the inter-domain routing policies.
# Two Medoid-Based Algorithms for Clustering Sets
## 3 Medoid-Based Approaches
### Java Implementation
```
... //clusters' queues for saving belonging data objects ConcurrentLinkedQueue<DataObject>{} clusters = new ConcurrentLinkedQueue[K]; for(int c = 0; c<K; ++c) clusters[c] = new ConcurrentLinkedQueue<>{};... seeding(INIT_METHOD); //initialize medoids do{ for(int c = 0; c<K; ++c) clusters[c].clear(); //partitioning step: assign data objects to clusters Stream=DataObject>do_stream = Stream.of(dataset); if(PARALLEL)do_stream = do_stream.parallel(); do_stream.map(dO ->{ double md = Double.MAX_VALUE; for(int k = 0; k<K; ++k) { double d = dO.distance(medoids[k]); if(d<md) { md = d; dO.setCID(k); } } clusters[ dO.getCID() ].add(dO); //add data object dO to its partition cluster return dO; }).forEach(dO->{}); //only to trigger the map operation //update medoids step for(int h = 0; h<K; ++h) { Stream<DataObject> c_stream = clusters[h].stream(); //open stream on cluster[h] if(PARALLEL) c_stream = c_stream.parallel(); final int H = h; //turn h into an effective final variable H DataObject neutral = new DataObject; neutral.setDist(Double.MAX_VALUE); DataObject best = c_stream.map(dO ->{ double c = 0D; for(DataObject q: clusters[H]) { if(q! = dO) c = c+ dO.distance(q); } do.setDist(c); //save the distance sum to other objects of the cluster return dO; }) reduce(neutral, (d1,d2)>{ if(d1.getDist()<d2.getDist()) return d1; return d2; }); newMedia[h] = new DataObject(best); newMedia[h].setN(clusters[h].size()); ]//for(int h = 0;...... )while(!termination());...
```  
**Algorithm 5**An excerpt of the K-Medoids algorithm in Java  
In the partitioning step (see also Algorithm 1), the nearest medoid to each data object is determined and the label of the medoid (its index) is assigned to the data object. As part of the \(map()\) operation, references of all the objects which belong to the same cluster are collected into distinct partition lists, to be used in the second step of medoids update. A critical issue concerns the modification of a shared partition list. To avoid data race conditions, partition lists are purposely realized as \(ConcurrentLinkedQueue\) lists which are totally lock free and can be safely accessed simultaneously by multiple threads. Partition lists can be processed in parallel in the update medoids step. As shown in Algorithm 5, for each data object \(dO\) of a cluster, first, the sum of distances from \(dO\) to all the remaining objects of the same cluster is accumulated in \(dO\) as part of a \(map()\) operation. Then, a \(reduce()\) operation is used which identifies and returns the data object (\(best\)) which has a minimal sum of distances to the other objects of the same cluster. In Algorithm 5, new medoids are temporarily defined and are compared with current medoids in the \(termination()\) method, which checks termination (by convergence or by maximum number of executed iterations) and, finally, makes new medoids current medoids for the next iteration.
## 5 Results and Analysis
### Stress Strait and Relative Breakage Rate in the Crushing Process
The change rule of the volumetric strain and the breakage rate of the sample with the breakage development was further investigated. Figure 11 presents the relationship between the ratio (\(\varepsilon_{v}/B_{r}\)) of the volumetric strain and the relative breakage rate and the stress level, indicating that, with the development of grain breakage, the increase of \(\varepsilon_{v}/B_{r}\) remains almost constant with the growth of \(\sigma_{v}\) at the fractal distribution stage, and the constant value of \(\varepsilon_{v}/B_{r}\) of each sample is relatively appropriate, indicating that \(\varepsilon_{v}\) and \(B_{r}\) are directly correlated and that \(\varepsilon_{v}/B_{r}\) is slightly affected by the initial distribution and particle size of the particles. The imaginary line in Figure 11 indicates that the constant value of \(\varepsilon_{v}/B_{r}\) corresponding to Hardin index is about 1.45 and the constant value of \(\varepsilon_{v}/B_{r}\) corresponding to Einav index is about 1.03. In fact, early scholars have observed similar phenomena in the triaxial and ring-shear tests on the calcareous sand [9; 10]. Actually, \(B_{r}\) adopts the Hardin index and it is found that the constant status of \(\varepsilon_{v}/B_{r}\) lasts until the particle breakage terminates. However, since there is no detailed investigation on the particle size distribution in the crushing process, there is no such analysis that it is an important characteristic of the fractal distribution stage for \(\varepsilon_{v}/B_{r}\) to keep constant. In the process of particle crushing, for the granular soils which tend to be of fractal distribution, \(\varepsilon_{v}/B_{r}\) remains constant and is slightly affected by the initial status. There are two potential functions: (1) it provides a criterion for the fractal distribution trend of particle size distribution in particle crushing process; (2) it provides a simple method for estimating the particle size breakage rate. Because if the particle size distribution is fractal, it is only necessary to measure the volumetric strain in the  
Figure 11: Evolution of \(\varepsilon_{v}/B_{r}\) with stress level. Figure 10: Influence of volumetric strain on relative breakage. crushing experiment and there is no need to terminate the test to carry out grain size analysis. The relative breakage rate of particles can be estimated roughly through the constant ratio of the volumetric strain and the breakage rate.