sadat2307/MSciNLI huggingface dataset:
Domain: Networks
Sentence 1: Again, not all these metrics are reported here for the lack of space.
Sentence 2: deceleration pro.les and distributions are missing, though theycon.rm all other results and show the additional ben­ 1: list KnownP ackets 2: list SendQueue 3: 4: on InitProgram(): 5: KnownP ackets .Ø 6: SendQueue .Ø 7: ScheduleEvent(SendPackets, 100ms) 8: 9: on ReceiveAggregatedEEBLPacket(aggregatedeebl): 10: for alleebl in aggregatedeebl do 11: ReceiveEEBLPacket(eebl) 12: endfor 13: 14: ReceiveEEBLPacket(eebl): 15: if KnownP ackets.Contains(eebl)then 16: if SendQueue.Contains(eebl)then 17: SendQueue.Remove(eebl) 18: end if 19: return 20: else 21: KnownP ackets.Insert(eebl) 22: ProcessPacket(eebl) 23: if eebl.ttl .=0 then 24: p .
Relationship: entailment
Domain: Networks
Sentence 1: Corretness.IfVishonest,thenthereonstruted valueisalwaysequaltotheserets(i.e.,foranyhoieof theunorruptedplayers'randominputsandadversary's randomness).
Sentence 2: commitment.EvenifVisdishonest,anyexeution ofthesharingphasedeterminesauniquevalues * whih willbereonstrutedatthereonstrutionphase.thesamevalues * willbereonstrutedregardlessof theviewsVi* providedbytheadversaryasinputstoRe.
Relationship: entailment
Domain: Networks
Sentence 1: For the pipeline to be stable, then, each stage must be able to complete its processing of data chunks in less than t time, on average (Figure 2).
Sentence 2: t1 < t, Ts < t, Tw < t, and Tc < t. In the analysis that follows we work with these steady-state assumptions; as future work, we would like to explore the use of a queueing model to bet- ter understand the dynamic properties of this system, such as the buffer requirements (queue lengths).
Relationship: entailment
Domain: Networks
Sentence 1: The greedy choice is to sample the point closest 0, call that point u.Let gi denote the event that the closest neighbor of u to 0 is at distance i.Now Pr[Fd = k]= Pr[fi] · Pr[Fi = k- 1] i Since u is closer to0than v we know that for every i, i Pr[gj] = i Pr[fj ].
Sentence 2: now since Pr[Fd = k] is mono­ j=0 j=0 tonically decreasing we have: Pr[gi]Pr[Fi = k- 1] = Pr[fi]Pr[Fi = k- 1] i i the best thing Acan do is sample the greedy point, which implies that the greedy algorithm dominates any other 1-local algorithm.
Relationship: entailment
Domain: Networks
Sentence 1: (a) Infected Nodes (b) Query Delay Figure 1: Numerical Results for the In.nite Buffer Scenario We use Pi and Pu to denote the probabilities of interested nodes and uninterested nodes to initiate the query.
Sentence 2: then for one speci.c data, its expected query delay at time t, EQ(t), can be estimated as EQ(t)=E(query delay of interested node) · Pi (2) + E(query delay of uninterested node) · Pu if the query is initiated by one interested node, this node can get the data either from its friends (interested nodes) or from its strangers (uninterested nodes).
Relationship: entailment
Domain: Networks
Sentence 1: 6.2 Scheduling Scenario 2 (SC2) In SC2, a comparison study among our proposed NSA scheme and RSS, SDiff, CDMA_Only and WLAN_Only algorithms in terms of overall normalized integrated network utility performance is presented.
Sentence 2: considering new users that sequentially enter the system requesting both NRT and RT services in a random manner, Fig.4 illustrates the overall normalized integrated network s utility achieved under NSA, RSS, SDiff, CDMA_Only and WLAN_Only network selection schemes, as a function of the number of users in the network (i.e.
Relationship: entailment
Domain: Networks
Sentence 1: Although these tools can be quite useful, for example, it has been noted that the con.guration analysis tool NetDB provides AT&#38;T signi.cant cost savings [47], these tools are inherently limited in the problems that they can detect.
Sentence 2: since con.guration .les alone do not determine the behaviors of a network, analyzing only the con.guration .les based on an abstract model of the network and equipment behaviors may leave many problems undetected.
Relationship: entailment
Domain: Networks
Sentence 1: In this pa­per, we perform a comprehensive study of content-agnostic char­acteristics of spam campaigns, e.g., duration and source-network distribution of spammers, in order to ascertain whether and how they can assist the simple label-based clustering methods in identi­fying campaigns and generating campaign signatures.
Sentence 2: from a .ve-month trace collected by a relay sinkhole, we manually identi.ed and then analyzed seven URL-based botnet spam cam­paigns consisting of 52 million spam messages sent over 2.09 mil­lion SMTP connections originated from over 150,000 non-proxy spamming hosts and destined to about 200,000 end domains.
Relationship: entailment
Domain: Networks
Sentence 1: = 3 is valid for a wide-range of ISP network sizes and SRLG failure probabilities.
Sentence 2: for typical ISPs with up to 10, 000 SRLGs, and realistic SRLG failure probabilities of about 10-5 [11], the probability of Shrink being worse than the brute force algorithm is always less than 10-4 and often less than 10-8 .
Relationship: entailment
Domain: Networks
Sentence 1: Main Results We .rst derive an upper bound on the multicast capacity of a wireless ad hoc network along the lines of [14].
Sentence 2: in the Gupta-Kumar model, the number of source-destination pairs is n. To compare and contrast our result with theirs, we too assume that the number of S-D pairs in our network with multicast .ows is n. we assume that the E number of multicast sources is nfor some E> 0, and the 1-E number of receivers per multicast .ow is n .We express our results in the order sense.
Relationship: entailment
Domain: Networks
Sentence 1: This is because of its better reuse of source routes already cached and not yet evicted.
Sentence 2: dSR-NCache (and similarly DSR-Link) bene.ts from local reuse (using routes discovered for one destination for another destination) and improved remote reuse (using routes cached by other nodes) as compared to DSR-Path and AODV-LL.
Relationship: entailment
Domain: Networks
Sentence 1: 4.2 The Inequality Problem with DSR Route Cache To achieve a fair comparison under the same ad-hoc net­work scenario, the route lengths/delays should be similar at the same time for di.erent TCP variants.
Sentence 2: if the route from S to R at time t is three hops under TCP-DOOR but four hops under base TCP, the performance re­sults would be incomparable.
Relationship: entailment
Domain: Networks
Sentence 1: Similarly, for the symmetric case, we can introduce x1, ..., xn 1sic2 y mean­ing that y can be derived from x1, ..., xn with the help of decryp­tion oracles and encryption oracles.
Sentence 2: the algorithm trying to compute y from the x1, ..., xn is allowed to submit strings to the oracles for encryption and also for decryption.
Relationship: entailment
Domain: Networks
Sentence 1: In CrossROAD, assuming a proactive rout­ing protocol for the network topology management, every node knows all the others taking part to the network, and becomes aware of topology changes with minimum delays, depending on the frequency with which control messages are sent on the network.
Sentence 2: through the de.nition of a cross-layer architecture for MANET [9], all protocols of the stack can bene.t from the knowledge of status informa­tion shared by other layers.
Relationship: entailment
Domain: Networks
Sentence 1: 0, let Zn(a, -x, b, y) de­note the zone that is an extension of Zn(a, b) by a length of x in the left direction and a length of y in the right direction.
Sentence 2: if l1 and l2 are the left and right boundaries of Zn(a, -x, b, y), then Vl1 = Vll(a) - x and Vl2 = Vrl(b) + y (Figure 3 shows an example.)
Relationship: entailment
Domain: Networks
Sentence 1: APPENDIX In this section, we formally show that the forwarding rules due to pseudo-random duty cycling do not alter the resul­tant transition probabilities, which are the same as those for simple random walk on .nite graphs.
Sentence 2: a duty cycling random walk amounts to .rst selecting a neighbor with equal probability and then waiting (while sleeping) for the number of slots until that neighbor wakes up.
Relationship: entailment
Domain: Networks
Sentence 1: Search cost and route quality Before considering how mobility a.ects search cost, it is in­structive to observe that route quality and search cost do not necessarily go hand in hand.
Sentence 2: the same route can be obtained at low search cost, if the encounter tables of the nodes are such that route discovery is done over many small searches, or it could be obtained at high search cost, if route discovery makes few large searches.
Relationship: entailment
Domain: Networks
Sentence 1: The AP popularity metric M1 calculates, for each cluster |Pop(C)| C, the fraction of popular APs, , and averages it |C| over all clusters of size |C|;an AP is in Pop(C) if the total number of incoming intra-cluster transitions to the AP in the trace exceeds the average number for all APs in that cluster.
Sentence 2: if we denote the average popularity of an AP in a cluster C as AvP op(C)= avgu.C( N(i, u)) (4) i then M 1(c)= avg|C|=c|{u .
Relationship: entailment
Domain: Networks
Sentence 1: One naive decision rule is to perform hypothesis testing on the aggregated pick counts in the past few seconds, that is, to compute the ratio of likelihood for the two hypothesis: 1) that there is an event (p = p1), and 2) that there is no event (p = p0).
Sentence 2: the naive decision rule performs the test Binomial(k; n, p1) = r Binomial(k; n, p0) where n is the total number of sensors in the system, k is the number of picks observed in the past few seconds and r is the decision threshold chosen to satisfy a constraint on the system false positive rate.
Relationship: entailment
Domain: Networks
Sentence 1: This observation leads to the question whether the truncation-induced reduction of risk and utility is asymmet­ric with respect to internal and external addresses.
Sentence 2: wehypothesizethat anomaliesdetectablein external address distributions are more resistant to truncation than those of internal distributions because the characteristics of external /32 pre.xes are less impaired by truncation of the .rst 8 bits(seeFig.2).
Relationship: entailment
Domain: Networks
Sentence 1: Compiler analysis for data filtering and our algorithm We define our problem as a computation-mapping problem; that is, for each sensor node in the wireless network, given a fixed amount computation to be performed (whose result needs to be communicated to the base station), we divide the computation between the sensor node and the base station.
Sentence 2: we decide what parts of the computation should be mapped to the base station and what parts of it to the sensor node.
Relationship: entailment
Domain: Networks
Sentence 1: We lack of a large data set, and the fact that these are truncated can suggest a simple reason why one might see such gaps: (by only using data sets for which the total time is roughly Cisco s implementation of the MinRouteAdverTimer is not less than one hour), means that one can model the tail almost per pre.x , but rather per peer .
Sentence 2: the router will as accurately using a power-law distribution.
Relationship: entailment
Domain: Networks
Sentence 1: For example, if each REQUEST includes in the node list location information and time information for each forwarding node, a node can keep a database of previous location information, and .nd two location claims that signi.cantly exceed the maximum speed achievable by legitimate nodes.
Sentence 2: if location in­formation is accurate to d, and time information is consistent to ., and maximum speed is ., then two locations claimed t time apart is maliciously claimed if the distance between the two locations is greater than 2d + .
Relationship: entailment
Domain: Networks
Sentence 1: Let ....be the normalized weight vector and .. be the original information signal; thus, transmitting .... can be expressed as ....... For maximum ratio transmission (MRT) algorithm, the weight vector is [9] ....=....* ....          (4) where · represents the Euclidean norm, and * represents conjugate transpose.
Sentence 2: the weight vector for AST is all zero but only one entry equal to 1 [6].
Relationship: entailment
Domain: Networks
Sentence 1: This sce­nario has a strong and a weak re.ection in the backscat­ter at delays of t1, t2 respectively and the corresponding strengths of the re.ected signals are a1, a2.
Sentence 2: if we transmitted signal x(t), the backscatter is given by a1x(t - t1) + a2x(t - t2).
Relationship: entailment
Domain: Networks
Sentence 1: For all multi-user beamforming techniques using linear precoding, it is su.cient for beamforming an­tennas to have a relatively accurate estimation.
Sentence 2: as long as each base station antenna s CSI estimation deviates from the real CSI by the same multiplicative factor, multi­user beamforming will still result in the same beam pattern.
Relationship: entailment
Domain: Networks
Sentence 1: For about 25% of the cost of the original system (using typical current PC component costs of 10% for a new processor, 10% for a new disk drive and 5% for added memory), the user can substantially improve the system performance.
Sentence 2: progress in storage and processing technologies during the two years (i.e., Moore's law) doubles processing power per dollar and triples storage per dollar.
Relationship: entailment
Domain: Networks
Sentence 1: The maximum NVA value is 3, because at NVA=3 all sensor nodes can reach the base station directly, hence, position dependent maximum transmission range constraint is effectively lifted for NVA= 3.
Sentence 2: any value of NVA larger than three will be meaningless since trans­mitting over a distance larger than RS is unnecessary for a disk shaped network, where the base is located at the center.
Relationship: entailment
Domain: Networks
Sentence 1: INTRODUCTION Emerging information-centric networking (ICN) architec­tures are currently changing the landscape of network re­search.
Sentence 2: named data networking (NDN) [1], or content-centric networking (CCN)[2], is a proposed network architecture for the Internet that replaces the traditional client-server model of communications with one based on the identity of data or content.
Relationship: entailment
Domain: Networks
Sentence 1: Note that for the case of 1 neighbor, there is no alternative next hops.
Sentence 2: the reachabilityguaranteeisprovidedeitherby covering pre.xesorotherwiseunstablepre.xesundamped,andallthe overheadreductionisduetodampingnoisypre.xeswiththe existence of coveringpre.xes.
Relationship: entailment
Domain: Networks
Sentence 1: ()H <1( We use ( )to denote the time for checking this property, where n is pn the number of the links in the H matrix), and we say the power vector P* =(I -H )-1·.is optimal in the sense that each transmission power in this P* vector is the minimum power that must be used to satisfy all the Q simultaneous transmissions.
Sentence 2: in order to successfully schedule all these Q links, each transmitter must use power larger than or equal to the corresponding power in P*.
Relationship: entailment
Domain: Networks
Sentence 1: [12] When k = .1 · ar2 , the area covered by the transmitting disks of a multicast tree is almost surely at least (2 ·a 2 , thus, T(a 2).
Sentence 2: v t kra, when k = .1a 2/r2 | Area(T )|= c0 (8) (2a 2 , when k = .1a 2/r2 v Notice that when k = O(a 2/r2), kra = O(a 2); when k = v O(a 2/r2), kra = O(a 2).
Relationship: entailment
Domain: Networks
Sentence 1: In order to obtain a polynomial time algo­rithm, we relax the domination condition so as to obtain a smaller number of candidate schedules.
Sentence 2: we de­.ne a pseudo-domination relation among schedules, accord­ing to which a schedule S1 pseudo-dominates schedule S2 ,if T1 R2 ,and |P1 |> |P2 |, where Ti, Ri, |Pi |are the total transmission power, the residual energy of the broad­cast nodes and the cardinality of the set of nodes covered by schedule Si, i =1, 2, respectively.
Relationship: entailment
Domain: Networks
Sentence 1: Then we may ask what is the probability, ac­counting for both the random locations {Xk} and medium access states {ek} of all other nodes, that (2) holds in a ran­dom time slot?
Sentence 2: if a random con.guration {Xk} is observed in a random time slot, what is the prob­ability that (2) holds, given the distance d = ||xj - xi||?
Relationship: entailment
Domain: Networks
Sentence 1: We can observe that the Pactual of them increases with m, but PDGRS outperforms GKMPAN.
Sentence 2: the Pc for PDGRS is much higher than that of GKMPAN with m. From the above analysis, however, we see that the actual local connectivity depends on the amount of space available on a node for storing keys, therefore, when the node resource is limited, we will improve the Pc by directly increasing the node degree d. PDGRS uses the following two ways to increase d. The first is that a node u can use its neighbors which have shared keys with u, to establish a secure channel with other nodes in u s one-hop communication range.
Relationship: entailment
Domain: Networks
Sentence 1: In other words, the answerer MAY channge formats in the middle of the session, making use of any of thhe formats listed, without sending a new offer .
Sentence 2: after establishing an initial sess sion between two peers A and B engaged in a multimedia (video or voice or both) session, then when one node moves, it is allowedd by RFC 3264 to modify the data stream and piggyback the neww session settings with the new stream.
Relationship: entailment
Domain: Networks
Sentence 1: INVARIANCE PROPERTY: NOT JUST FOR CONTACT-BASED METRICS Analytical and simulation results in [5, 7] have shown an invariance property of contact-based metrics, including con­tact time and inter-meeting time, under the class of corre­lated random walk (CRW) [1] as well as random walk (RW) mobility models [8].
Sentence 2: the average contact and inter-meeting time are invariant with respect to the degrees of correlation in CRW mobility models and step length dis­tributions in RW mobility models, under both Boolean and SINR interference models.
Relationship: entailment
Domain: Networks
Sentence 1: The advantage of this starting point for intrusion detection is that it is rooted in a community­centric essence of normality.
Sentence 2: rather than de.ning intrusion in terms of locations in physical space (where normality is harder to characterize), this hypothesis de.nes intrusion in terms of locations in social space, where normality often has well-understood proper­ties.
Relationship: entailment
Domain: Networks
Sentence 1: Let Ih(v) denote the set of neighbors of vertex v including v,and let Ih(A) denote the set of neighbors of ver­tices in A, i.e., Ih(A):= .v.AIh(v).
Sentence 2: let wv(t, h)denote a weight associated with vertex v. we de.ne the weight of vertex v as the product of queue length Qv(t)and transmission rate rv h.Let w * (t, h) denote the largest weight, i.e., w * (t, h):=maxv.V wv(t, h).
Relationship: entailment
Domain: Networks
Sentence 1: RELATED WORK There has been extensive prior research on fault-tolerant consensus in synchronous (see [27]), partially synchronous (e.g., [14]), asynchronous with failure detectors (e.g., [7, 24]) and fully asynchronous (e.g., [16]) message passing systems with reliable or eventually reliable point-to-point channels.
Sentence 2: [14, 24] overcome message loss by assuming that eventually there is a connected majority component.
Relationship: entailment
Domain: Networks
Sentence 1: 0.15 -3 x 10 11 We then reduce the size of Di to the set of interfering BSSs 5 of BSS i.
Sentence 2: d1 = {BSS1,BSS2,BSS3}, D2 = D3 = {BSS1,BSS2,BSS3,BSS4}, and D4 = {BSS2,BSS3,BSS4}.
Relationship: entailment
Domain: Networks
Sentence 1: In essence, HEAP uses a modified HMAC-based algorithm [10] that utilizes two keys.
Sentence 2: with the initial bootstrapping phase, every node (I) shares a pair-wise secret hash key, called okey, with each of its neighbors, and (II) generates one common secret hash key, called ikey, and securely distributes it to all of its one-hop neighbors.
Relationship: entailment
Domain: Networks
Sentence 1: We use the same topology as before, set cache sizes ac­cording to the formula: Ni × T SB (see Table 2) and ob­serve the performance di.erence in case of heterogeneous caches.
Sentence 2: if a core router caches one second worth of tra.c and we assume larger caches towards the edges of the network (which we denote as c(ore) .
Relationship: entailment
Domain: Networks
Sentence 1: In particular, we use the lexico­graphically order.
Sentence 2: we say that a vector y = (y1,...,yk )islexicographically less than a vector z = (z1,...,zk ), and write y <L z,if y1 <z1,or if y1 = z1 and y2 <z2,or if y1 = z1, y2 = z2 and y3 <z3,or ...,if y1 = z1,...,yk-1 = zk-1 and yk <zk .y <L z if in the .rst component in which y and z dif­fers, that component of y is less than the corresponding component of z.We write y =L z if either y <L z or y = z.
Relationship: entailment
Domain: Networks
Sentence 1: As it is hard to maintain a central controller in a prac­tical ad hoc network and a node can not be a transmitter and receiver at the same time, our distributed scheduling algorithm has two phases, namely transmitter nodes selec­tion and stream allocation.
Sentence 2: a set of nodes are .rst selected to be transmitter nodes based on their priority and the current network topology, then the streams with higher priority and/or better quality are allocated from the selected set of transmitter nodes.
Relationship: entailment
Domain: Networks
Sentence 1: As a .rst step to understand the impact of mobility in CRNs, we study theperformance of spectrum sensing with mobile sensors via a theoretical study.
Sentence 2: we show that, when sensing is scheduled multiple times, sen­sor mobility canyield asigni.cantperformancegainby ex­ploitingspatio-temporaldiversityin receivedprimary signal strengths.
Relationship: entailment
Domain: Networks
Sentence 1: This is an extension of epidemic where a mes­sage copy is only forwarded from node i to node j if node j has a higher forwarding quality than node i.
Sentence 2: in our simula­tion, we use a mean inter-meeting time Ik,d with the desti­nation d as the forwarding quality of a node k. node j has a higher forwarding quality than node i if Ij,d <Ii,d.
Relationship: entailment
Domain: Networks
Sentence 1: Rather than treating each switch as an independent entity as done by all prior work, perhaps given their starting point using fully decentralized control protocols like IGMP we leverage ideas from scale-out storage systems to par­tition the multicast address space and distribute address partitions across cooperating switches.
Sentence 2: all core switches in the datacenter network act as one cooperating set, as well as each set of upper-layer switches in each datacenter network pod .
Relationship: entailment
Domain: Networks
Sentence 1: Let us consider the symbolic execution of the .rst case (case of B): For the protocols B(a, b) and B(a ' , b), the .rst published message is {(pk, na)}ra (compared to Example 4, we switched a pkb here to a from a ' as it does not matter because of the symmetry of the problem), besides the public keys.
Sentence 2: we let f0 = pk, pkl , pkb, {(pk, na)}ra .
Relationship: entailment
Domain: Networks
Sentence 1: 5.1 Initialization In the PCSS algorithm there is no need for a separate initialization procedure to start communication, since the pseudo random gener­ation of checkpoints is de.ned such that once a master slave node pair share the same master s clock and slave s MAC address infor­mation, it is guaranteed that the same pseudo random sequence will be produced at each node.
Sentence 2: it is guaranteed that two nodes starting checkpoint generation at different time instants with differ­ent checking intensities will be able to communicate.
Relationship: entailment