sadat2307/MSciNLI huggingface dataset:
Domain: Networks
Sentence 1: 2.2 Deducibility algorithms Thesearealgorithmsfordeterminingwhetheranintruder can de­duceatermfroma setoftermsalreadyinits possession,giventhat termsobeyagivenequationaltheory.Inthis case, onestartswitha set T ofterms knowntotheintruder, aterm t theintruderistrying tolearn,asetofinferencerules describing operationstheintruder can perform, and an equational theory E.Thededucibility algo­rithm is a procedure for determining whether or not the intruder can derive a term from s from T such that s =E t.Anumber of algorithmshave beendevelopedfordifferentclassesof equational theories,including associative-commutative and homomorphic op­erators[1].Asurveyofdeducibility withrespecttoequationalthe­ories may befoundin[12].
Sentence 2: algorithmsforaclassof theories known as sub­term convergent(convergenttheoriesforwhichtheright-handside isaeither anirreduciblegroundterm or subtermof theleft-hand side)havebeen developedin[15] andforalarger classof conver­genttheoriesthatinclude encryption homomorphicoverafree op­erator[8] andhave beendevelopedandimplementedinthetools KISS and YAPA, respectively.
Relationship: entailment
Domain: Networks
Sentence 1: (2) i N(u, i) 0.7 Modeling the transitions within a cluster amounts to try­ ing to isolate the salient features of how transition probabil­ ities are distributed for intra-cluster moves.
Sentence 2: an interesting question is whether all transitions are roughly equally likely, which would be the case for a random walk model, or whether there exists some other law governing this Cumulative distribution 0.6 0.5 0.4 process.
Relationship: entailment
Domain: Networks
Sentence 1: Opportunistic Real-Time Routing in Multi-Hop Wireless Sensor Networks Junwhan Kim ECE Dept., Virginia Tech Blacksburg, VA, 24060, USA  junwhan@vt.edu ABSTRACT Wireless sensor networks (WSNs) are subject to signi.cant resource constraints.
Sentence 2: routing protocols for low­rate WSNs su.er from maintaining routing metrics and sta­ble links of paths.
Relationship: entailment
Domain: Networks
Sentence 1: If the log Li is a se­quence (..., op1,change1,change2,change3,op2, ...), the log Li can be changed with (..., op1,change3,op2, ...) since change is an overwriting operation.
Sentence 2: a delete operation of a subobject r compensates an add operation of the subobject r. the state obtained by performing add and then delete on a state s of a replica oi is the same as the previous states on which add is per­formed.
Relationship: entailment
Domain: Networks
Sentence 1: (n) when we partition the network and implement a local­ized scheduling algorithm in each partition, we will prove .l(n)= T(.g(n)) (6) is possible if we partition the network and design the local­ized scheduling algorithms appropriately.
Sentence 2: given .g(n)=T(f(n)), we will demonstrate the way to achieve .l(n)= T(f(n)), regardless of the function f(n) that depends on the particular assumptions on the interfer­ence, location and communication models.
Relationship: entailment
Domain: Networks
Sentence 1: Now, it may not always be possible to ensure complete coverage for some deployment scenarios.
Sentence 2: for a particular combina­tion of forward-reverse paths, and a given constraint on the max­imum allowable link load, we may not have a feasible solution to ensure that each covc =1.3 In this case, we want to minimize de­tection misses and thus, we introduce a new term in the minimiza­tion objective to model the fraction of traf.c that suffers detection misses because we cannot monitor both sides of the connection.
Relationship: entailment
Domain: Networks
Sentence 1: These de.nitions coincide with previous property-based de.nitions in some cases, and are different in other cases.In particular,theanalysisoftheAtEparadigmleadstoagen­eral formalization of the malleability of con.dential channels and encryption schemes, and the previous notions of non-malleability appear as special instances of the proposed formalization.
Sentence 2: the AtE-transformation is sound for all encryption schemes with suitably restricted malleability.
Relationship: entailment
Domain: Networks
Sentence 1: The second binding (between the name and key) can be established using an external authority such as a Certi.cate Authority (CA).
Sentence 2: in our example above, the string CNN in the name would be suf.cient for the user to determine that the RWI associated with the name is CNN, and a CA will verify that a key belongs to the name containing CNN.
Relationship: entailment
Domain: Networks
Sentence 1: Another approach to QoS routing is to exploit the dependencies between the QoS parameters and solve the path selection problem assum- ing specific scheduling schemes at network touters [26, 29].
Sentence 2: if Weighted Fair Queueing (WFQ) scheduling is being used and the constraints are bandwidth, queueing delay, jitter, and loss, then the problem can be reduced to a standard shortest path problem by representing all the con- stralnts in terms of bandwidth.
Relationship: entailment
Domain: Networks
Sentence 1: In IPNL, the IPNL header is the end-to-end addressing header, and the IPv4 header is delegated the role of an encapsulat­ing link header.
Sentence 2: at every nl-router hop, the IPv4 header of the incoming packet is stripped away, and a new IPv4 header is attached to the outgoing packet.
Relationship: entailment
Domain: Networks
Sentence 1: For a node j to be a jammer a node which can con.ict with the transmission but cannot be heard Sj > Si -T and Sj < Ni-1 +T , where Ni-1 is the hardware noise at mi-1.
Sentence 2: its signal strength must be within T of mi s received signal strength at mi-1 to con.ict and also be within T of the noise .oor such that it can never be heard.
Relationship: entailment
Domain: Networks
Sentence 1: Because this disk D can be covered by smaller disks of radius dij in such a way that every point in the plane is covered by at most two small disks, it follows that 2p(c1 +2)2d2 B = pd2 |ij |= C log n · ij =2(c1 +2)2C log n. ij Bounding the cardinality of B> is signi.cantly more in­ ij volved.
Sentence 2: we need to distinguish three kinds of blocking links in B> depending on which line of the ij , check(eij, Lt) subroutine caused the returning of false.
Relationship: entailment
Domain: Networks
Sentence 1: This paper addresses the problem of anonymity in mobile opportunistic routing schemes and argues that using only social network or label similarity for forwarding can be damaging for maintaining sender s anonymity due to routing protocols repeatedly using similar nodes for forwarding and anonymising their messages.
Sentence 2: we propose to Ivan Vaghi EarlyMorning 20127 Milano, Italy ivan@earlymorning.com design a new adaptive anonymisation overlay in mobile opportunistic networks that aims to maximise the quality of anonymisation while maintaining high success ratios of answered queries and low delays.
Relationship: entailment
Domain: Networks
Sentence 1: A FairCast .ow should then, in a similar manner, increase its threshold to be more tolerant to loss.
Sentence 2: here follows the algorithm: min{thrr(t)+ a, 1}, if .c(t) > .r(t) thrr(t + 1)= (17) max{thrr(t) -b, 0}, otherwise where a and b are .xed parameters we will shortly investigate, .c(t) the highest of the lambdas received from competing nodes at .ow r and .r(t) the lambda computed by .ow r. if nobody complains, .ow r keeps tightening up its threshold.
Relationship: entailment
Domain: Networks
Sentence 1: It is worth noting that, although the algorithm has been shown to perform well in a variety of scenarios, it has recently been found that it does not completely eliminate free-riding [16, 17, 23].
Sentence 2: a peer may improve its download rates by download­ing from seeds, acquiring a large view of the peers in the torrent, or bene.ting from many optimistic unchokes.
Relationship: entailment
Domain: Networks
Sentence 1: Since we focus, in this work, on pure VoIP scenarios, we make the following simpli.cations: i) the various delay measures col­lapse into a single one, the mouth-to-ear delay, and, ii) the default values proposed in [4] are used for all parameters in the expres­sion of Idother than the delay itself.
Sentence 2: the in.uence of echo is supposed negligible.
Relationship: entailment
Domain: Networks
Sentence 1: 5.2 Decomposition of the traf.c load series 0.2 0 -0.2 0.2 0 -0.2 -0.4 A bene.t of the above eigenload-based categorization is -0.4 that it results in a decomposition of any given tra.c load 0.4 0.4 series (normalized or not) into its principal features.
Sentence 2: we can reconstruct each time-series in terms of two con­ stituents: the contributions made by the deterministic and the noisy eigenloads.
Relationship: entailment
Domain: Networks
Sentence 1: returns a typing environment G '' such that the formulas in G '' entail C and the variables yl+1,... ,ym, corresponding to the public messages that are returned by the veri.cation destructor, have types Tl+1,... ,Tm in G '' .
Sentence 2: g '' proves that the zero-knowledge proof has the stronger type ZKProofn,m,S ((y1 : T1,... ,ym : Tm){.x1,... ,xn.C}) speci.ed by the user.
Relationship: entailment
Domain: Networks
Sentence 1: As already mentioned, the simulation is based on the UDelModels tools [13, 24] for the computation of the nodes mobility and propagation.
Sentence 2: uDelModels is used to generate realistic node trajectories and to compute the communication channel loss between any two radio stations in the three-dimensional urban scenario with the granular­ity of 1 s. The UDelModels results are used as input by our ad-hoc simulator whose major functionalities are: to simulate the radio contacts among the nodes as far as the channel loss is below a threshold a;  to implement the management of the bu.er B;  to simulate the exchange of collected items among the nodes in radio contact;  to simulate the mobility and radio propagation of R nodes, that are not available in UDelModels; the di­rection of the trajectory of the R nodes is updated randomly every second and their speed is drawn ac­cording to a uniform distribution;   Figure 1: A zoom of the simulated urban area: nodes ROI are depicted as squares.
Relationship: entailment
Domain: Networks
Sentence 1: This is because in a squarer network, paths tend to cross each other more than in a rectangular network, which increases x 10 MANETs.
Sentence 2: 0.8 we see that performing congestion-aware 0.6 routing can improve some metrics in a MANET, but it re­ 0.4 mains to be seen about how we can incorporate our insights 0.2 into the routing protocol design for improving MANET rout- Pkts.
Relationship: entailment
Domain: Networks
Sentence 1: In this .ltered set, 48 % of users are .ne-grained users, i.e., reporting only .ne-grained geotags, 21% are coarse-grained users, i.e., reporting only coarse-grained geolocation, and 31% are hybrid­grained users.
Sentence 2: about 79% of users report at least some highly accurate geo-coordinates.
Relationship: entailment
Domain: Networks
Sentence 1: Bavailable(K) is a good example to show XLEngine s components coopera­tions.
Sentence 2: it is evaluated by LocalCLE and is given by relation (1): Bavailable(K)= ChCap - Bself (K) - BN (K) - BCSN (K)(1) ChCap is the overall channel bandwidth read from CLIC.
Relationship: entailment
Domain: Networks
Sentence 1: When the views are not truly random, there is a possibility that while most nodes receive all messages, a small number of nodes do not receive messages at all or receive only a small fraction of all messages.
Sentence 2: if there are some nodes (e.g., low degree nodes) that are not uniformly distributed among other nodes views, those nodes will be constantly denied messages and thus suffer from probabilis­tic starvation.
Relationship: entailment
Domain: Networks
Sentence 1: delay( d¯acc(j)) of a link l for a tra.c class j, takes into account the success rate, number of contending nodes and their tra.c loads, and tra.c priority.
Sentence 2: it measures the duration of time that includes the time that the medium is detected busy (because of successful or un­successful transmissions from contending nodes), the time for unsuccessful transmission or collided transmission and the random backo.
Relationship: entailment
Domain: Networks
Sentence 1: (using (3)) (13) 2D2 The theorem suggests that there is a minimum investment Similarly, but using (12) instead of (11), we arrive at: that is required in order for the infrastructure to have an P [] 1+ E 1-d bi > n D1 <a exp[-e.ect on the performance of the network.
Sentence 2: if there are n wireless nodes, more than n 1 2 access points are f(E) 1-d ].
Relationship: entailment
Domain: Networks
Sentence 1: The analysis is aimed at identifying the factors that need be considered when designing an IEEE 802.16 based WMN.
Sentence 2: the fol­lowing network and system configuration parameters are consid­ered: frame duration and XmtHoldoffExponent (Section 3.2), network size (Section 3.3), and two-hop neighborhood size (Sec­tion 3.4).
Relationship: entailment
Domain: Networks
Sentence 1: While the models in [11, 12] seem to be reasona­ble for analyzing the performance of low-vehicle-density DTNs like the UMass DieselNet [7], they are unlikely to be applicable for commercial Drive-thru Internet systems to be deployed in a dense­ly-populated urban city like New York City.
Sentence 2: with the high-density of vehicles (which are often packed with one or more bandwidth-hungry wireless users or applications), any performance model for the corresponding Drive-thru Internet systems must ad­dress the issues of (a) potential resource contention amongst multiple vehicles concurrently covered by an AP and (b) the dy­namic changes in the membership of the group of vehicles being served by an AP due to vehicular movement.
Relationship: entailment
Domain: Networks
Sentence 1: low17 medium of IXP members, but does not distinguish them in terms of high size or type.
Sentence 2: there are several reasons why one and the same AS can show up in the membership list at a number of di.erent IXPs; e.g., increased resilience, con­ nectivity with a particular member, connectivity in a par­ ticular location served by the IXP.
Relationship: entailment
Domain: Networks
Sentence 1: In fact, in order to take full advantage of the control plane .ex­ibility provided by OpenFlow, the forwarding plane must be designed with an equivalent level of .exibility.
Sentence 2: we need to extend the SDN concept also to the lower network layers, generalizing the recent advances in software­de.ned radio [12], programmable wireless devices [13] and software-de.ned photonics [14] to a cross-layer and holistic scenario.
Relationship: entailment
Domain: Networks
Sentence 1: In the attempt to turn o. a radio, we must clearly control the e.ect of moving .ow away from some links on the total utilization of the collision domains of other links.
Sentence 2: we require that the total utilization of all the col­lision domains be less than the threshold .0, except for the collision domain having a total utilization above .0 before solving the LP, for which the total utilization must not in­crease above the current value.
Relationship: entailment
Domain: Networks
Sentence 1: In order to relieve the user from a tedious device discovery and configuration process, we propose an out-of-band signal­ing mechanism, based on Near Field Communication (NFC) connectivity, for enhancing IUT operations.
Sentence 2: we specify the utilization of an intuitive NFC touch interface for (a) user and device discovery and (b) negotiation of me­dia stream properties (e.g.
Relationship: entailment
Domain: Networks
Sentence 1: We demonstrate with examples that, contrary to common belief, devices implementing the same IEEE 802.11b/g standard often have differing MAC level behavior.
Sentence 2: we demonstrate that in certain cases, devices may not be conformant with the standard (e.g., due to incorrect implementations), where as, in other cases, they may differ in details that are not a part of mandatory specifications of the 802.11 standard.
Relationship: entailment
Domain: Networks
Sentence 1: As a result, in NCR-DSA, SUs and PUs make friends with each other via NC to create a unique win-win situa­tion for both.
Sentence 2: nCR-DSA o.ers incentive to PUs (for embracing DSA and allowing spectrum access by SUs) through increased throughput and reduced delay of PU tra.c.
Relationship: entailment
Domain: Networks
Sentence 1: For practical applications in tra.c monitoring, we require that the relative error be low (say less than 2­3%), so that the accuracy of applications such as anomaly detection and tra.c clustering is not a.ected.
Sentence 2: an accurate estimate of S may not necessarily give an accurate estimate of H.when H is very small and S is close to its maximum value, a small relative error estimate of S may not correspond to a small relative error estimation of H.Let S be the estimated value of S and H the estimated value of H computed from S, i.e., H = log (m) - Suppose we have an algorithm to compute S/m.
Relationship: entailment
Domain: Networks
Sentence 1: MessageLength do 7 eCount[j ]=sum(Et[startPos +(j - 1) * bitPeriod, startPos + j * bitPeriod]) 8 foreach rotatedInstance in rotatedMesgVec do 9 currentDist = 0; 10 foreach ith bit in rotatedInstance do 11 if ith bit == 1 then 12 currentDist = currentDist + max(0, 1e -eCount[i]) 13 else 14 currentDist = currentDist + max(0,eCount[i]-0e) 15 // Update Min distance 16 if currentDistance < minDistance then 17 minDistance = currentDist; 18 RxCandidate = rotatedInstance; 19 return RxCandidate Algorithm 1 .rst converts the discrete timing event information to a event/no-event time series data.
Sentence 2: if the received se­quence of event is Ediscrete = {E1,E2, ..., Em} in which Ei is ith event, it will be represented by a vector in the form: Et = [Et1,Et2, ....Ettmax ] where Eti =1 if there exists an event Ek such that Ek = i,and 0 otherwise.
Relationship: entailment
Domain: Networks
Sentence 1: This means that forwarding tables at lower tiers may be more regular and more compressible (to the point that a single-homed AS s routing state is compressible to zero bits in theory), suggesting that these ASes su.er 0.5 0.25 way lower scalability stress than those at top tiers.
Sentence 2: from the lower tiers we see a much more heterogeneous Internet, with most of the pre.xes avail­able via only a few primary upstream providers (which therefore concentrate lot of pre.xes, introducing a huge bias into the next-hop distribution in the routing func­tion) and customer and peer ASes contributing very little to the entropy.
Relationship: entailment
Domain: Networks
Sentence 1: Transitive-trust dependencies also nullify e.ectiveness of DNSSEC, [RFC4033-RFC4035], and impede its adoption [1, 3].
Sentence 2: if name servers or other resources of a signed zone are placed under unsigned domains, the DNS re­ CDF (%) 100 90 80 70 60 50 40 30 20 Legend alexa tld tld w tt 10 1 2 4 8 16 32 64 128 256 #Collocated Zone Files Figure 4: Zones coresidence on name servers among 25K-top Alexa domains and TLD, and servers in transi­tive trust dependencies of Alexa domains and TLDs.
Relationship: entailment
Domain: Networks
Sentence 1: For example, we might require (for additional se­curity), that a user have the ability to access ob ject ob1 at location l1 and time t1, and then be at location l2 at time t2 in order to access object ob2.
Sentence 2: without having been to (l1, t1) and having accessed ob1, the user would not have the requisite access control information needed in order to access ob2 at location (l2, t2).
Relationship: entailment
Domain: Networks
Sentence 1: Denote the ef.ciency of wireless charg­ing by µ(DiB (p)), which is a decreasing function of DiB (p),the distance between node i and the WCV located at p. Then the wire­less charging model is as follows [10]: µ(DiB (p)) · Umax if DiB (p) = Dd UiB (p)= (2) 0 if DiB (p) >Dd , where Umax is the maximum output power for a single sensor node and Dd is the charging range of the WCV, beyond which wireless charging will not occur.
Sentence 2: dd is de.ned in a way such that the power reception rate at a sensor node is at least over a threshold value d.  2.3 Dynamic Data Flow Routing Recall that the base station is co-located at the WCV and all data generated from the sensor nodes shall be delivered to the base station.
Relationship: entailment
Domain: Networks
Sentence 1: The emergence of customer-installed cellular networks led us to ask the ques­tion: can we design 100% self-sustained cellular networks?
Sentence 2: is it possible to design a network that provides the services users expect from today s GSM/3G networks and grows autonomously without any centralized infrastructure or planning, yet allows su.cient control and management in order to satisfy the operational, legal, and regulatory re­quirements of the environment in which it is deployed?
Relationship: entailment
Domain: Networks
Sentence 1: Otherwise, there will be a gap along the edge of C that cannot be covered by any Ci (1 =i =n).
Sentence 2: ci s (1 =i =n) must overlap in order to fully cover C. The theorem below gives the probability that a sensor is completely covered by its n random neighbours: Theorem 2: n(n -1) Pr{A} =1 -nPr{A1}+ Pr{A1A2}.
Relationship: entailment
Domain: Networks
Sentence 1: within one hierarchical structure Therefore, unlike most other MIPv6 based micro-mobility schemes, the macro-mobility of CUIP is enabled through CUIP itself and the MNs are still addressed by the universal address.
Sentence 2: as long as the originating and destination wireless access networks are CUIP enabled, macromobility can be manipulated without relying on MIPv6.
Relationship: entailment
Domain: Networks
Sentence 1: Under this speci.c condition, we analyze the size of the population ul­timately remaining in the system, in the limit where the initial population size, N, goes to in.nity.
Sentence 2: we show that the size of this ultimate population decreases geometrically with the total number of coupons, K. when the ratio K/log(N) is above a critical threshold, we prove that this number of left-overs is of order O(log(log(N))).
Relationship: entailment
Domain: Networks
Sentence 1: (Lattice color repetition)Becausetheperiodic coloringisob­tained by repeating the color pattern, the nodes W such that U UW is alinear combination of UUV2havethe same color asU.
Sentence 2: uV1 and Uthe setof nodes W having the same color asU forms a lattice of Z2 withgenerator vectors UUV2: the vector U UV1 and UUW canbe written as UW U= a UUV2 with a and ß inZ2 UV1+ ß U.
Relationship: entailment
Domain: Networks
Sentence 1: To compare the impact uniformly, we shall choose ap > 1 for positively aligned parameters and 1/ap for negatively Figure 4: 802.11, Line, Flooding, 24 Mbps radio  aligned parameters.
Sentence 2: for the remainder of this section, 802.11, Mesh, Unicast, Radio Rate=54 Mbps we will use a =2for W, and a =0.5for .l.
Relationship: entailment
Domain: Networks
Sentence 1: Now assume towards a contradiction that packet p is still at the tail of edge e at the end of time step t +LwrJ.
Sentence 2: there are LwrJother packets that crossed edge e in [t +1,t +LwrJ].As before this identi.es LwrJ+1 distinct packets that require edge e, are present in the network at the end of time step t or later, and are injected by time step t +LwrJ-1.
Relationship: entailment
Domain: Networks
Sentence 1: Unlike the .tness scalarization and lexicographical ap­proaches, Multi-Objective Evolutionary Algorithms [10] are based on the assumption that all the ob jectives of a problem should be considered at the same time.
Sentence 2: in order to capture the complexity of the search space of two con.icting objectives, an algorithm cannot provide a single optimal solution, but rather should generate a Pareto front, that is, a set of non-dominated solutions such that none of the ob jective functions can be further improved without degrading some other ob jective value.
Relationship: entailment