sadat2307/MSciNLI huggingface dataset:
Domain: Networks
Sentence 1: Salient features of a Stealth DHT are that it maintains a single overlay, and that any source node (including stealth nodes) uses the original routing decision process found in traditional DHTs to choose the .rst hop of their message.
Sentence 2: the original DHT rout­ing is used for super-peer selection on a per message basis, meaning there exists no single point of failure in the Stealth DHT approach preserving all of the bene.ts of traditional DHTs whilst enabling super-peering.
Relationship: entailment
Domain: Networks
Sentence 1: Let Xi be a (time-varying) matrix denoting the local view of the available chunks at peer i.
Sentence 2: xi(t) = [x(jk i)(t)], (2) where xjk (i)(t) = 1 if peer j is connected to peer i (yj = 1) and peer j has chunk k. Otherwise x(i)(t) = 0.
Relationship: entailment
Domain: Networks
Sentence 1: 6 Performance of the PFCR algorithm In this section, we present simulation results to compare the fairness characteristics of the Proportional Fair Contention Resolution (PFCR) algorithm, with some existing medium access protocols that were discussed earlier in Section 2.
Sentence 2: we have chosen three protocols with different con- tention resolution schemes: IEEE 802.11 [4], MACAW [2] and CB-Fair [6], for comparison.
Relationship: entailment
Domain: Networks
Sentence 1: In this work, we propose two methods to perform topological downscaling.
Sentence 2: starting from a complex network, we create a smaller version of it with fewer links and nodes, observe the behavior of a sample of the original traf.c on this network, and extrapolate the observed performance back to the origi­ 1SHRiNK: Small-scale Hi-.delity Reproduction of Network Kinetics.
Relationship: entailment
Domain: Networks
Sentence 1: This allows us to stress-test the topology.
Sentence 2: a tra.c matrix (TM) T de.nes the tra.c de­mand: for any two servers v and w, T (v, w) is an amount of requested .ow from v to w. The throughput of a network G with TM T is de.ned as the maximum value t for which T ·t is feasible in G. we seek the maximum t for which there exists a feasible multicommodity .ow that routes .ow T (v, w)·t through the  Random Matching Longest Matching Figure 1: Relative throughput of computer networks under di.erent tra.c matrices network from each v to each w, sub ject to the link capacity and the usual .ow conservation constraints.
Relationship: entailment
Domain: Networks
Sentence 1: We thus face the problem of validating a model with a given loss rate against multiple simulation runs with varying loss rates.
Sentence 2: since our goal is to validate our model for a given value of p, we select simulation runs with loss rate close to p. we select the runs with loss rate in the range of (1±)p, where < 15%, for model valida­tion.
Relationship: entailment
Domain: Networks
Sentence 1: In the G-differential consensus problem (~5- DC for short), the Validity requirement is strengthened to specify that the value finally agreed on must be of certain plurality among the correct players: - 5-Differential Validity: If the correct players decide on v, then there is no value w E 79 such that #gw > #gv+ 6.
Sentence 2: 0-Differential Validity means that the final value must be one of the input values that is most common among the correct players.
Relationship: entailment
Domain: Networks
Sentence 1: In this paper, we examine the two key elements necessary for the realization of vehicular VLC (V2LC) networks: (i) the feasibili­ty of realizing V2LC networks in working conditions under con­straints posed by noise and interference sources and (ii) the capa­bility of V2LC network services to satisfy the performance re­quirements of vehicular applications.
Sentence 2: we make the following contributions.
Relationship: entailment
Domain: Networks
Sentence 1: (h ' ) Moreover, t,h ' > 1, canbe expressed as[38]: j-1,j h ' -1 t() (h ' )(h * )(h * )(h)(h) t= X+q+ w-w, (10) j-1,j j-1,j jj-1 h=h*+1  where H(h *) , 1 = h * = h ' -1, is thelasthopprecedingH(h ' ) for which pj arrives before pj-1 s full departure from the node.
Sentence 2: both belong to the same busy period of the H(h * )(h * ) queue at [38].
Relationship: entailment
Domain: Networks
Sentence 1: By (11), we have l ln ( d ) J Mu Umax = .. (12) ln (1 -W ) For each node i .N ,we draw Mu circles centered at node i, with each circle having an increasing radius Du[h], 1 = h = Mu .
Sentence 2: based on (2), there is a circle with a radius of Dd that cuts off the charging area for node i.when DiB (p)= Dd , UiB (p)= d (see Fig.
Relationship: entailment
Domain: Networks
Sentence 1: Second, as shown in figure 3, the node A, in the center, is fully covered by nodes B, C, D and E but unfortunately such case is ignored by the central angle method mentioned in the C-PNSS because nodes C, D, E will not be considered as neighbors.
Sentence 2: because the node A will not be off­duty but rather considered as an edge node, holes can emerge in the intersections among A and C, D or E when C, D, E are out of energy.
Relationship: entailment
Domain: Networks
Sentence 1: Proof: By Corollary 1, there exists a matching M * that matches all critical ports.
Sentence 2: it matches i.Con­sider now the symmetric di.erence MLM * , which contains 1 1 2 2 3 3 4 N-2 N-1 N-1 NN Figure 4: An example where MWM, GMM, MSM and MWM-0+ are not clearance-time optimal node-disjoint paths and cycles; since i is not matched by M, i will be the endpoint of a path P in MLM * .If P is of odd length, it is an augmenting path, and we are done.
Relationship: entailment
Domain: Networks
Sentence 1: DATA DISSEMINATION AS A DIFFUSION-CONTROLLED PROCESS This section discusses our initial e.orts to model data dis­semination as a di.usion controlled process using theory from random walks and environment [14] and the kinetics of di.usion-controlled chemical processes [22].
Sentence 2: we use the di.usion in a medium with randomly distributed static traps to model the FIS approach of data dissemina­tion.
Relationship: entailment
Domain: Networks
Sentence 1: A feasible allocation K0 is said to be Max-Min Fair (MMF) [18] if for any other feasible allocation K, then for all SPs d such that kd >kd0 , there exists some SP d' such that kd, <kd0 , = kd0 .
Sentence 2: allocating more than the MMF allocation to any SP, can only be done at the expense of reducing the allocation to some smaller SP.
Relationship: entailment
Domain: Networks
Sentence 1: TAP7 Figure 1: Illustration of Multihop Wireless Backhaul Unfortunately, current protocols are severely inadequate in achiev­ing the design goals of multihop wireless backhaul networks.
Sentence 2: existing protocols result in severe unfairness, poor per­formance, and in some cases, starvation, for users located more than one hop away from the wired entry point.
Relationship: entailment
Domain: Networks
Sentence 1: While intra-cluster netMIMO transmission can be devised by migrating the 802.11ac MU-MIMO modulation algorithm that ex­ploits multiplexing gain [5, 7], we .nd this could severely degrade netMIMO s performance.
Sentence 2: netMIMO may encounter unbalanced topologies where clients are concentrated near a few dAPs (which is common in, for example, conference rooms).
Relationship: entailment
Domain: Networks
Sentence 1: Once a proper mapping is established, the ef.ciency of a CDN is pri­marily a matter of how well the individual servers can man­age their cached contents: To serve as many of the incom­ing requests as possible.
Sentence 2: each server is ex­pected to maintain a dynamic collection of popular content while both incurring as little cache-.ll traf.c as possible and redirecting minimal requests to other (less preferred) CDN servers for that user IP.
Relationship: entailment
Domain: Networks
Sentence 1: Under the unicast model, the data dissemination problem is a generalization of the well-studied uncapacitated facility location problem [21, 12, 25, 22], many variants of which have also been studied [9, 23, 3, 16].
Sentence 2: when we have a single source and many sinks, our problem reduces to uncapacitated facility location.
Relationship: entailment
Domain: Networks
Sentence 1: Although the sites have suf.cient computational resources, such as CPU, memory, and so on, the total processing performance might be de­creased when the network path between communicating nodes is congested.
Sentence 2: the network throughput for a grid ap­plication would become a major performance bottleneck because the applications deal with petascale data, and total processing time as well as task processing would also be affected.
Relationship: entailment
Domain: Networks
Sentence 1: Let node 1 be in stage 0 and let node 2 be in stage j. Node 2 picks a slot and if there is no collision, node 2 goes to stage 0 and chooses a slot from stage 0 until collision occurs.
Sentence 2: it is assumed that a node that is at stage j starts from the stage 0 after any number of its subsequent successful attempts.
Relationship: entailment
Domain: Networks
Sentence 1: However, spread spectrum bears a spreading cost.
Sentence 2: the wireless users consume more resource than no spreading by a factor that is proportional to the number of channel options that the users have for channel access, because the process of spread­ing symbols entitles either transmitting redundant informa­tion (in case of code-based spreading) or reserving more re­source than the user uses at a time (in cases of frequency or time-based spreading), and thus has a negative impact on the throughput rate performance.
Relationship: entailment
Domain: Networks
Sentence 1: Furthermore, PFR is a multi­path protocol which is shown (see [10]) to be very e.cient when µ(R) 31.5, thus in this work both protocols are evaluated together for the .rst time in novel simulation set­tings, in a fair way.
Sentence 2: for the case where nodes are deployed randomly and uniformly, using the equation (npR2) of [7], µ(R)= A , the density in our simulation in­stancesisset to {14.726, 19.634, 24.543, 29.452, 34.361} for n = {300, 400, 500, 600, 700}.
Relationship: entailment
Domain: Networks
Sentence 1: Conversely, in our scenario there is an initial time where the collision probability is greater than 0 (all nodes that are queried start transmitting), followed by a transitory state where it decreases over time (each success­ful node stops attempting to access the channel) and .nally followed by a steady state where collision probability is zero (all nodes have either successfully transmitted or failed).
Sentence 2: our system reaches the equilibrium when no node is transmitting anymore because all packets have al­ready either gone through or failed.
Relationship: entailment
Domain: Networks
Sentence 1: We proposed a DRAM-based counter architecture that can e.ectively maintain wirespeed updates to large counter arrays exploiting advanced archi­tecture features that are readily available in modern com­modity DRAM architectures.
Sentence 2: we presented a randomized DRAM architecture that can harness the per­formance of modern commodity DRAM o.erings by inter­leaving counter updates to multiple memory banks.
Relationship: entailment
Domain: Networks
Sentence 1: 3.3.2 Historical Records of Past Reservations Each station v keeps historical records of successful and failed reservations that have been made with it in the past.
Sentence 2: it maintains a FIFO queue of past reservations Hvu = {(ti,di,xi)|i =0, ··· ,n - 1} for each reservation source station u.
Relationship: entailment
Domain: Networks
Sentence 1: We now show that there may exist F 1 ... F N , where F i .Fi for all i, such that even though the system (N, .1 ... .N , F1 ... FN ) is stable, the .ltered system (N, .1 ... .N , F 1 ... F N ) is unsta­ble.
Sentence 2: there exist routing systems with next-hop rankings for which a stable path assignment exists, but introducing .ltering can yield a system where no stable path assignment exists.
Relationship: entailment
Domain: Networks
Sentence 1: )ss I + .swI), (25b) white noise is analogous to the noise enhancement produced in (22).
Sentence 2: it is seen that the SNR matrix em­ployed in the estimator-correlator in nonuniform sampling is equal to the SNR matrix in the estimator-correlator in uniform sampling with an equivalent SNR matrix given by (29), i.e., a linear combination of the true second-order con­ditions S1 and a contribution proportional to the average SNR .
Relationship: entailment
Domain: Networks
Sentence 1: Based on (1), the mean change indicator curve is constructed as MC(k) versus t(k), where MC(k) is the value of W (A 1 -A 2)2 calculated for the window containing ratings {x(k -W ), ···,x(k + W -1)}.
Sentence 2: the test in (1) is performed to see whether there is a mean change at the center of the window.
Relationship: entailment
Domain: Networks
Sentence 1: k N(D(k)) * ED(k) (2) where ED(i) isdefnedas theaverageMSEifabitinthedistortion group D(i) is decoded incorrectly, and N(D(i)) is the number of bits indistortion group D(i).
Sentence 2: the fraction of space allocated to a distortion group is proportional to its importance, as well as the number of bitsinthedistortiongroup.
Relationship: entailment
Domain: Networks
Sentence 1: 2.2.2 Successful SACK recovery phase When L segments are lost in a round of cwnd segments the necessary condition for the SACK-based loss recovery mechanism to be triggered is that three dupacks are received, therefore: cwnd L L+3 (6) -- = However the condition (5) is not sufficient to guarantee that the SACK loss recovery mechanism will be able to continue.
Sentence 2: if the variable pipe remains equal or above floor[cwnd/2] when the first partial ACK is received the SACK loss recovery mechanism will stall because no segments can be sent even if one or more segments have been determined as lost.
Relationship: entailment
Domain: Networks
Sentence 1: First, the high-level alerts generated by security systems like IDS s lack context.
Sentence 2: alerts typically provide infor­mation about the speci.c vulnerability exploited, but no information about the actions that preceded or followed the exploit (e.g., Did the attacker perform any reconnaissance before attacking the system?What did the attacker do to the compromised system after the exploit?).
Relationship: entailment
Domain: Networks
Sentence 1: INTRODUCTION Existing paradigms of dynamic spectrum access (DSA) impose a foe relationship between the primary users (PUs) and the secondary users (SUs).
Sentence 2: sUs may ac- Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro.t or commercial advantage and that copies bear this notice and the full citation on the .rst page.
Relationship: entailment
Domain: Networks
Sentence 1: For our test-bed nodes, we have validated that all of them are able to achieve the maxi­mum TXR when CCA is turned o..
Sentence 2: the TXR are between 79.25 packets per second (PPS) and 79.95 PPS for all 784 transmitters of trials 2.10-2.13 with only 3 out­liners.
Relationship: entailment
Domain: Networks
Sentence 1: For the latter case, we simply let v I = v. Ac­cording to the blowing-up feature of the transformation f(·) (which will be quanti.ed by Proposition 4), there al­ways exists an fI(·) such that fI(pi) lies on the convex hull of f I(PI) {v I}, as far as there is no point in PI that lies on the line segment between v I and pi.
Sentence 2: if pi is on the external boundary of P, it is visible from some viewpoint v I .
Relationship: entailment
Domain: Networks
Sentence 1: To limit the overhead of this .ood, each node typ­ically forwards only one ROUTE REQUEST originating from any Route Discovery.
Sentence 2: existing on-demand routing pro­tocols, such as AODV [32], DSR [20], LAR [23], Ariadne [16], SAODV [45], ARAN [39], AODV secured with SUCV [6], and SRP [31], only forward the REQUEST that arrives .rst from each Route Discovery.
Relationship: entailment
Domain: Networks
Sentence 1: For count and retrieve predicates, we assume that the network density is known and that a function E(h) for the expected num­ber of nodes within h hops (the so-called h-neighborhood) can be estimated from deployment parameters1 .
Sentence 2: now consider a count predicate c of the form: count(scope) { pred } rel lim where the nested predicate pred is in disjunctive normal form and contains only simple sub-predicates ck we can compute ij.
Relationship: entailment
Domain: Networks
Sentence 1: A semi-maximal .lter is a maximal .lter which uses only the source IPaddress of a packet to carry out its .ltering (i.e., a projection of Fe).
Sentence 2: fe(s, t)isa semi­maximal .lter with respect to R if 0, if e .R(s, v)for some v .V ; Fe(s, t)= 1, otherwise.
Relationship: entailment
Domain: Networks
Sentence 1: 3.3.1 Temporarily Disabling Congestion Control (R1) Since we have mentioned that OOO is likely caused by route changes and not by congestion, one way to avoid the undesired congestion control e.ects is for the TCP sender to temporarily disable congestion control action whenever an OOO condition is detected.
Sentence 2: for a time period T1 after detecting an OOO, TCP sender will keep its state variables constant, such as the retransmission timer (RTO) and the congestion window size.
Relationship: entailment
Domain: Networks
Sentence 1: The contribu­tion of this demo is to illustrate these .ndings on a real EH-WSN test bed.
Sentence 2: for di.erent classes of WSN applications (i.e., applications having di.erent performance requirements) as well as under several energy harvesting sce­narios, we show how ODMAC supports sustainable opera­tion of nodes powered by ambient energy.
Relationship: entailment
Domain: Networks
Sentence 1: From the connectivity graph, then a weighted conflict graph for each channel of the useable set is constructed.
Sentence 2: each vertex in the conflict graph for channel s corresponds to an edge (link) in the connectivity graph realized using the channel s. whenever a new link is established using the channel s, a new vertex, representing the link, is added to the conflict graph for the channel s. Now, if there are two channel s links are there within the radio interference range (i.e.
Relationship: entailment
Domain: Networks
Sentence 1: We present a more efficient proto- col with round complexity of (n/t) °(t), where n is the number of processors in the network and t is an upper bound on the number of Byzantine processors in the network.
Sentence 2: our protocol is polynomial when the number of Byzantine processors is O(1), and for every t its round complexity is bounded by 2 °(~).
Relationship: entailment
Domain: Networks
Sentence 1: Furthermore, any node on one side of the orthogo­nal line passing through u can not cover 2d-zone(u) s farther orthogonal boundary.
Sentence 2: proof: First, consider a circular belt of width W , its inner circle being of radius R. the curvature at any point of the belt s inner boundary is of magnitude 1/R.Con­sider an L2-zone, with a line of length r,anarc of length L1, .A, .B as shown in Figure 16.
Relationship: entailment
Domain: Networks
Sentence 1: Discrete Entropy is frequently used in coding because the entropy H(X)gives a measure of the number of bits required to code the values of X.
Sentence 2: if we had a large number n of randomly­generated instances X1,X2,...,Xn and needed to represent this stream as compactly as possible, we could represent this stream us­ing only nH(X)bits, using entropy coding as practiced for example in various standard commercial compression schemes.
Relationship: entailment
Domain: Networks
Sentence 1: We assume that the Web cluster has three back-end servers offering two different types of requests, both server A and server B can serve type-1 requests and server C can serve only type-2 requests, and the threshold of LARD/RC scheduling is set to 3.
Sentence 2: if the number of connection exceeds the threshold, then the subsequent requests will be dispatched to another back-end server in which the number ofactive connections does not exceed the threshold.
Relationship: entailment
Domain: Networks
Sentence 1: We emphasize that this work is not another damping algorithm itself, but a complementary addition to anyexistingrout .apdampingscheme.
Sentence 2: in all the previous works, one common issue is that the pre.x .apdetectionisbundledwithroute suppression.apre.xissuppressed immediatelyonceitis detected un­stable.
Relationship: entailment
Domain: Networks
Sentence 1: In contrast, the opportunistic connections of the graph of Figure 1(c) allow for higher transfer rates, but simultaneously demand a more careful level of orchestration between end-systems to achieve those rates.
Sentence 2: any pair of end-systems in a peer-to-peer relationship must be able to determine which packets lie in the set difference of their working sets, and subsequently make an informed transfer of those packets, i.e.
Relationship: entailment
Domain: Networks
Sentence 1: For d =3, this distance D3 is a sum of squares of di.erences between the current and target numbers of wedges and triangles, and we can generalize it to Dd for any d. A potential problem with dK-targeting d ' K-preserving rewiring is that it can be nonergodic, meaning that there might be no chain of d ' K-preserving Dd-decreasing rewirings leading from the initial d ' K-graph to the target dK-graph.
Sentence 2: we cannot be sure beforehand that any two d ' K-graphs are connected by a sequence of d ' K-preserving and Dd-decreasing rewirings.
Relationship: entailment
Domain: Networks
Sentence 1: Because the APs are now in charge of measuring network characteristics of the C-to-C data path for the appli­cation as a whole, individual .ows can employ that informa­tion and make rate adjustments con.dent that peer .ows of the same application are getting the same information and are also appropriately responding.
Sentence 2: endpoints see the same bandwidth availability estimates, round trip time measurements, loss rate statistics, and other network-based statistics.
Relationship: entailment
Domain: Networks
Sentence 1: As in the case of the random waypoint simulations, we varied each parameter separately and we performed the .ve statistical tests.
Sentence 2: we varied pmove from 0.7 to 1 in steps of 0.1, pstat from 0 to 0.5 in steps of 0.25, and m from 0.01 to 0.1 in steps of 0.03.
Relationship: entailment
Domain: Networks
Sentence 1: As discussed previously, the average value of the distance between nodes is not always representative of the entire statistics of every order, which might be hence interesting to be characterized through the cdf.
Sentence 2: the plot traces reported in the figure might be then useful for example to evaluate the amplitude of the oscillations in the group size or even higher order statistics of the average inter-node distance obtained by using our model.
Relationship: entailment
Domain: Networks
Sentence 1: We now present our method for calculating the .rst term in Equation (5).
Sentence 2: first, for each counter in the counter sub­vector of f, whose value is contributed by the time stamps of the packets in .ow f and those of the packets in other .ows, we use Theorem 3 to extract an estimate of the value that is contributed by only the time stamps of the packets in f. we eliminate the noise introduced by the pack­ets of .ows other than f from the counter subvector of f. Second, we statistically simulate the process of multiplying the time stamp of a packet in f with the random variable Gz and then adding the multiplication result to the corre­sponding counter in the counter subvector of f. By repeating this process a statistically su.cient number of times, we ob- L tain an accurate estimate of .z(uf,R[z]-uf,S [z])2 based on Theorem 4.
Relationship: entailment
Domain: Networks
Sentence 1: We note that for any w where X(w) = 1 and any i,j such that 0 < j < i < K, it follows from Lemma 1 that Yj(w) < Y/(w).
Sentence 2: there is some w for which X(w) = 1 where for some i, Y/(w) = 0 while YK(W) = 1.
Relationship: entailment