sadat2307/MSciNLI huggingface dataset:
Domain: Networks
Sentence 1: |C||N | Theorem 1 shows that an ISP s Shapley value is inversely proportional to the number of ISPs of the same type, and proportional to the number of ISPs of the opposite type.
Sentence 2: the aggregate Shapley value revenue of both types of ISPs are inverse proportional to the number of ISPs of each type, i.e.
Relationship: entailment
Domain: Networks
Sentence 1: /24 /27 /28 /29 /30 /31 Ø n/o /24 2 1 1 /25 1 1 /27 2 2 /28 18 4 5 4 4 1 /29 25 8 9 1 4 3 /30 86 4 1 67 7 7 /31 16 2 13 1  Table 1: Comparison of genuine and inferred Inter-Figure 4: Sample network net2 subnets 12 subnets smaller than they were due to the lack of other IP addresses.
Sentence 2: all observed IP addresses of those subnets were accurately clustered but they formed smaller /x subnets, i.e., longer same x bit pre.xes, than the actual.
Relationship: entailment
Domain: Networks
Sentence 1: We denote Pn =(Xn,Fn) as the problem after the nth iteration completes execution.
Sentence 2: p0 =(X,F), which is the original speci.ca­tion of the problem; Pm =(Ø,{(ai,Ø),ai ./C}), the empty problem and GreedySetCover(Pm)= Ø.
Relationship: entailment
Domain: Networks
Sentence 1: Deploying the initial con­sequence detectors guarantees that all the attack paths, from the initial state so to one of the goal states s .
Sentence 2: ss in the AGT, is cut (de­ dp tected) in at least one detection point exploit e. if an attacker tries to compromise the machine while it is operating with the initial IDS set turned on, he or she would be detected and stopped by at least one of the deployed intrusion detectors.
Relationship: entailment
Domain: Networks
Sentence 1: An action pair (x,y) * is a Nash equilibrium if and only if each of the actions x * and yis a maxminimizer [25].
Sentence 2: * minyEA2 u1(x,y) 2 minyEA2 u1 (x, y) for all x E A1, and * minxEA1 u2 (x, y) 2 minxEA1 u2 (x, y) for all y E A2.
Relationship: entailment
Domain: Networks
Sentence 1: The main take away from this study is that the event and query rates must scale suf.­ciently slowly with the network size if scalable performance is de­sired.
Sentence 2: an important scaling condition is ensuring that 1/2 2/3 q · m be O(N1/4)for unstructured networks, and that q · m be O(N1/2)for structured networks.
Relationship: entailment
Domain: Networks
Sentence 1: On the other hand, the mobile device traf.c contains long period of in­active phase.
Sentence 2: in high speed 3G networks pack­ 6 6 5 5  1 1 0 80 10 20 30 40 50 60 70 Figure 3: Modeling for end-to-end delay analysis: (a) IPP withon-off period for network source simulation, (b) a sequence ofqueuing system for network route simulation, (c) M/M/1/K .­nite storage system for bottleneck hop buffer simulation.
Relationship: entailment
Domain: Networks
Sentence 1: For a neighbor w2nbr(v), we can de.ne the force Fv!was the discrete derivative of Vwith respect to the link metric as (V(v);V(w)) Fv!w=(1) c vw The packet pis now directed to the neighbor x2nbr(v)for which the force Fv!xis maximum and positive.
Sentence 2: each packet fol­lows the direction of the steepest gradient downhill to reach its destina­tion.
Relationship: entailment
Domain: Networks
Sentence 1: Categories and Subject Descriptors C2.0 [Computer-Communication Networks]: Data communications General Terms Design, Economics, Experimentation Keywords Proximity sensing, Relative location-based services, Proximity networking, FM Radio, Social gaming 1 Introduction Mobile phones, thanks to their ever increasing computing power, sensing capabilities, and rich networking interfaces, have become the primary information device that one uses regularly.
Sentence 2: the location sensing capability of the mobile phone has triggered the recent blossom of location­based services (LBS).
Relationship: entailment
Domain: Networks
Sentence 1: Assumption 1 For any cross-traf.c .ow jthat enters the path from link Li, the cumulative traf.c arrival process {Vi(fj,t)} has ergodic stationary increments.
Sentence 2: for any d> 0, the d-interval traf.c intensity process {Yi,d (fj,t)}is a mean-square ergodic process with time­invariant distribution and ensemble mean xj.
Relationship: entailment
Domain: Networks
Sentence 1: A necessary condition for a successful GPS group spoof­ing attack is that .Vj ,Vk, .si, bj ijk =|Pj - Pk| .
Sentence 2: the difference bj ijk of the perceived pseudoranges of each signal s A at any two spoofed victim locations Ljj and Lj ik must be smaller than or equal to the distance between the victims physical locations Pj and Pk.
Relationship: entailment
Domain: Networks
Sentence 1: A k-active .oodingprotocolis a .ood­ingprotocolinwhich each nodeforwardsthesource message only during the k time steps immediately following the step at which the node receives the message for the .rst time.
Sentence 2: by setting It (k) as the set of all nodes which hold the message at time t of the execution of the k-active .ood­ (k)(k)(k)(k) ing protocol, we have I= I.Nwhere Nis the t+1 tt t set of all nodes that are neighbors in Gt of at least one node (k) (k) in It It-k.
Relationship: entailment
Domain: Networks
Sentence 1: By adopting a pricing mechanism, we .rst characterize the interactions between the primary and secondary users using a buyer/seller model.
Sentence 2: the price is .rst determined by the primary network, and on the buyer side, the secondary users com­petitively access the spectrum and employ the primary re­lay node to forward their packets, and correspondingly, make payments to the primary network based on the receive signal to interference plus noise ratio (SINR) and the the speci.ed price.
Relationship: entailment
Domain: Networks
Sentence 1: Based on [11], the downlink power consumption of user i in the cellular network is: (i) (i,lte) Wlte = ßlte + arx Rrx , (12) where ßlte is the baseline power, arx is the downlink power (i,lte) consumption per M bps, and Rrx is the average data rate received by user i over the LTE interface: (i, lte) M (i) (i) Rrx =pbkt, (13) k=1 k k where bktk (i) is the average data rate of user i when its MSC is k, bk is the per-symbol throughput achieved with that MCS, and the value of tk (i) represents resources allocated to user i, which depend on the scheduling policy adopted (see Section 4).
Sentence 2: under cluster-based schedulers, only clus­ter heads receive packets from the base station, so that the power consumption of user i strongly depends on the prob­ability of that user being the cluster head when it can use (i|k) MCS k, namely Ph .
Relationship: entailment
Domain: Networks
Sentence 1: Proposed requirement of strong DAD is de.ned more formally below: Strong DAD: Let Ai(t) be the address assigned (tenta­tively or otherwise) to node i at time t. Ai(t)is unde.ned when node i has not chosen any address at time t.For each address a =unde.ned, de.ne set Sa(t)= {j | Aj(t)= a}.
Sentence 2: sa(t) is the set of nodes that are assigned address a at time t. A strong DAD algorithm must ensure that, within 1By repeated application of this requirement, when more than two nodes have chosen the same address, eventually all but one (if not all) nodes that have chosen the duplicate address will detect the duplication.
Relationship: entailment
Domain: Networks
Sentence 1: The VPN operator authenticates users and takes logs, and connections are encrypted, thus nonrepudiation is ensured.However, there might be large overhead in case that a user connect MIAKO net­work from a distance, because the user can access to the network only via the VPN server, even if users are side by side in real environment.
Sentence 2: in MI-AKO network, the connection of users are always via the VPN server.
Relationship: entailment
Domain: Networks
Sentence 1: 1.2 Rent s Rule Locality Fortunately, we have empirical evidence, in the form of Rent s Rule [9], for example, which suggests there is addi­tional structure to typical computations which we can ex­ploit to avoid this limit.
Sentence 2: the evidence from Rent s Rule suggests that the bisection bandwidth for a design does not need to be O(N), but rather, can be O(Np)with p< 1 for typical designs.
Relationship: entailment
Domain: Networks
Sentence 1: Then we de.ne sbfPyx (t) as the supply bound function associated to the parent component of Gyx that computes the minimum bandwidth supply provided to its children at instant t. As referred, one important aspect that we are consider­ing in this analysis, that was not considered in [12], is that preemption is allowed between packets, only, i.e., packets transmission is non-preemptive.
Sentence 2: any higher priority components that become ready during the trans­mission of the last packet (of a stream or server instance) will not be able to preempt it and thus will not impact on the component response time.
Relationship: entailment
Domain: Networks
Sentence 1: Handshaking procedures become slightly more involved when a sender must contact multiple receivers within a single session.
Sentence 2: the following aspects must be taken into account: i) whether a sender should transmit a single RTS for all receivers, or rather multiple RTSs, one for each receiver; ii) how many CTSs should be received before a handshake is considered complete; iii) how to handle the case where no CTSs are received.
Relationship: entailment
Domain: Networks
Sentence 1: 160 (a) Geant false-positive rate geant Figure 6 provides further support for the conclusion that the relative properties of tra.c aggregation formalisms ap-140 pear to hold across networks.
Sentence 2: for a given topk 120 value but across all thresholds and both networks, the input links aggregation generally detects more potential anoma­ lies than OD-.ows, which detects more potential anomalies than ingress routers (see .gures 6(b) and 6(d)).
Relationship: entailment
Domain: Networks
Sentence 1: Perhaps the most naive assumption is that if for player j, .C(j, ij ) > 0, then the price it will ask player i to pay is Pij = max{.C(j, ij), 0}.
Sentence 2: if it is ben­e.cial for player j to establish a link, it will not ask for a payment in order to do so.
Relationship: entailment
Domain: Networks
Sentence 1: ware, this can increase the possibility of bugs in the overall system.
Sentence 2: our goals for the design are that (i)the design is simple, implementing only a minimal set of func­tionality, reducing the set of components that may contain bugs, and (ii) the design is small, opening the possibility of formal veri.cationof thehypervisor a more realistic task than verifying an entire routing software implementation.
Relationship: entailment
Domain: Networks
Sentence 1: [9]: E[N]isasymp­totically bounded by O(log M), where N is the number of collisions a node encounters between its two consecutive suc­ cessful transmission.
Sentence 2: we estimate M by keeping track of the number of collisions and using the following equation: E[N] M=10 K ' , (47) where K ' is the same value of K in Eq.
Relationship: entailment
Domain: Networks
Sentence 1: i,j   quality, and further that it strictly increases with the trans-i=1 j=1 mission power.
Sentence 2: the collision-free data-rate Hm(q m), which stands for R1m,j <Rm 2,j < ··· <RmIm,j , (1) the average data-rate of successful transmissions, namely JM j .
Relationship: entailment
Domain: Networks
Sentence 1: Feasible rate In a wireless ad hoc network with n nodes in which each source transmits its packets to m destinations, a throughput of .m(n) bits per second for each multicast session is feasible if there is a spatial and temporal scheme for scheduling and network coding transmissions, such that, by operating the network in a multi-hop fashion and bu.ering at intermedi­ate nodes when awaiting transmission, every source node can send .m(n) bits per second on average to its m chosen des­tination nodes.
Sentence 2: there is a T< 8 such that in every time interval [(i - 1)T, iT ] every node can send T.m(n) bits to its corresponding destination nodes.
Relationship: entailment
Domain: Networks
Sentence 1: Each property in the dK-series provides information on connectiv­ity within groups of d nodes with degrees k1, ..., kd,where d takes values from 0to the total number of nodes, n, in the graph.
Sentence 2: the dK-series describes correlations amongst degrees of nodes in subgraphs of size d,for d =0, 1, ..., n. Each element in the dK-series allows us to generate dK-graphs that reproduce the speci.c correlation among the degrees in all d-sized subgraphs of the given graph.
Relationship: entailment
Domain: Networks
Sentence 1: We present an extensive empirical study performed in an o.ce building and provide key insights on the de­sign of robust topology control algorithms in such en­vironments.
Sentence 2: we analyze the correlation between RSSI/LQI and PRR and show that RSSI and LQI are not always robust indicators of PRR in indoor environments with signi.cant multi-path e.ects.
Relationship: entailment
Domain: Networks
Sentence 1: Thus consider that we are trying to generate synthetic traces for a cluster C. The equations can be used to .nd out which values go into the transition probability matrix M for C. Unfortunately they do not tell us where in the matrix these values go, i.e., they do not tell us M(u, v) for some speci.c u, v .
Sentence 2: c. simply populating the rows of the transition probability matrix in a skewed man­ner, as described above, is not su.cient.
Relationship: entailment
Domain: Networks
Sentence 1: To avoid continuous polling on resources and also to en­able near real-time applications, we support HTTP callbacks (also known as Webhooks5) for resources representing sen­sor readings.
Sentence 2: as soon as the value of a sensor changes, an HTTP POST request is sent by the node to a pre-speci.ed URL, containing the updated value, i.e., the current sensor reading, in the message body.
Relationship: entailment
Domain: Networks
Sentence 1: Selection of the angle a 3.3.2 Classification of Concentric Circular Bands Because the proposed protocol trades off energy savings with source-to-sink delay, we propose to classify the different CCBs of the transmission range of a sensor node based on the values of their exterior radii.
Sentence 2: the set of CCBs will be divided into three categories as shown in Figure 1.
Relationship: entailment
Domain: Networks
Sentence 1: [4, 6, 9, 10, 14, 15, 22, 25] and the surveys of [11, 30]) have been in a model where the routing scheme designer may assign his/her own O(log n)-bit or sometimes O(log 2 n)-bit node la- bels, dependent on network topology.
Sentence 2: when a packet destined for i arrives, 'T' has been renamed, not by some ar- bitrary permutation P but by the routing scheme designer, *Supported in part by NSF grant IIS-0099446.
Relationship: entailment
Domain: Networks
Sentence 1: Equation (1) can be re-written as, 1 d Imin maxGmin = - 1 - (3) q nq We note that code constructions that produce codes with larger d n ratio for a given dimension q and I will have min / max larger guarantees.
Sentence 2: larger are Gmin Gmin possible for codes with more relative minimum distance.
Relationship: entailment
Domain: Networks
Sentence 1: With an uncertainty relationship of the form of (18), the in­herent measurement noise(see equation 6), unknown mobile user acceleration/driving command and the uncertainty in the initial condition are considered as bounded deterministic uncertain inputs.
Sentence 2: the measurement equation with the standard norm bounded uncertainty can be written as (equation 6) y = C(x)+ dC(x)+ v0 where |d|=.
Relationship: entailment
Domain: Networks
Sentence 1: In contrast, Internal-BGP [10, 11] (or I-BGP) is used for exchanging this external routing information among routers within the same AS.1 It has been observed in practice that persistent route oscillations can occur when using I-BGP [16, 19] with route re.ec­tion [1] or confederations [20].
Sentence 2: some subset of the routers within an AS may exchange routing information forever without being able to settle on a stable routing con.guration.
Relationship: entailment
Domain: Networks
Sentence 1: Even minimizing the makespan without regard to fairness a special case of fairness-ef.ciency tradeoff is NP-hard.
Sentence 2: we note that minimizing the makespan of a packet schedule can be mod­eled as a multi-stage .ow shop problem [6, 20, 23] studied in oper­ations research, where the equivalent of a packet is a job, and the equivalent of a type of resource is a machine.
Relationship: entailment
Domain: Networks
Sentence 1: In addition, AS X needs to ensure that the packets, upon reaching router R2, are decapsulated and forwarded via the egress link to Provider 1, even if R2 normally forwards packets via Provider 2 to reach this destination.
Sentence 2: r2 needs to decapsulate the packet and still forward the packet based on the tunnel identi.er2.The customer, in turn, must install the necessary state to ensure that packets entering the network are diverted to the appropriate tun­nel.
Relationship: entailment
Domain: Networks
Sentence 1: The results depicted in Table 3 show that the differences in energy consumption levels between the context-based opportunistic and the optimum configurations are dependent on the density of mobile nodes within the cell and the probability with which the presence of a MR is guaranteed within the search area.
Sentence 2: the results reported in Table 3 show that the 3 The upper limit of the distances shown in Figure 4 coincides with the radius of the search area (9).
Relationship: entailment
Domain: Networks
Sentence 1: From there, the lookuprequest canbe routedtowards the smaller levels, as described in the previous section.
Sentence 2: using this approach, we .nd t quickly if it is close to s.we prove that the lookup time is linear in the distance between s and t. As for the search areas, we use an extended version of the levels of node s, who issued the lookup request.
Relationship: entailment
Domain: Networks
Sentence 1: In the next day however paths are more dispersed; some pass through AS3549 (Global Crossing), some through AS3257, some only through AS174 (Cogent), and some pass only through other ASes, not in the .gure, which connect directly to AS29632.
Sentence 2: aSes are switching from AS6939 to other ways to reach AS29632.
Relationship: entailment
Domain: Networks
Sentence 1: In follow-on work [2], we proposed enhancements to the basic ESP service that enabled its use for network monitoring and management;we call the resulting ser­vice ESP-NM.The primary enhancement enables access to the network management information base (MIB) at each node in the network.
Sentence 2: in addition to pro­viding access to ephemeral state values at a node, ESP instructions can reference MIB variables at a node.
Relationship: entailment
Domain: Networks
Sentence 1: Tegola Tiered Mesh Network Testbed in Rural Scotland Giacomo Bernardi Peter Buneman Mahesh K. Marina g.bernardi@sms.ed.ac.uk opb@inf.ed.ac.uk mmarina@inf.ed.ac.uk School of Informatics The University of Edinburgh, UK ABSTRACT Many rural and remote communities around the world see them­selves on the wrong side of the digital divide.
Sentence 2: there is evidence to suggest that there is a growing digital divide between urban and rural areas in terms of broadband Internet access with people living in rural areas having fewer choices and pay higher prices for slower speeds.
Relationship: entailment
Domain: Networks
Sentence 1: It can be measured as a fraction of the short­est paths between pairs of vertices in a network that pass through the node.
Sentence 2: the betweenness can quantify the importance of a vertex to the network, which can be de.ned as follows: .. s(i, u, j) Bu = , (8) s(i, j) ij where s(i, u, j) is the number of shortest paths between ver­tices i and j that pass through vertex u, s(i, j) is the total number of shortest paths between i and j, and the sum is over all pairs i, j of distinct vertices [6].
Relationship: entailment
Domain: Networks
Sentence 1: in the lemma when setting H = m 2J, a =(mJ)-4 , b =(mJ)-1, and q = (2em)-1 .
Sentence 2: thus the lemma shows that Pr [F(t) = k] = 1 - m1 2J -k at any time t. we have E [F(t)] = m 2J at any time t. As F is an upper bound on the lengths of queues for failed packets, this completes our proof of Theorem 3.
Relationship: entailment
Domain: Networks
Sentence 1: The key message is that error patterns .uctuate widely depending on several parameters, e.g., time of the day, choice of channel, location of the measurements, distance between the sender and the receiver, behavior of other users, etc.
Sentence 2: unlike other technologies, e.g., GSM-based systems, where the quality of the channel is more or less stable, one cannot make statements of the form the average 802.11b channel has an LTER of X%and an EBLof Y packets.
Relationship: entailment
Domain: Networks
Sentence 1: Since ROMA uses 3 802.11b/g and 3 802.11a channels among 3 gateways, as opposed to only 1 802.11a channel in the common channel con.guration, it achieves the highest aggregate throughput.
Sentence 2: rOMA s median aggregate throughput is 1.4× and 2.1× that of the common and identical channel con.gurations, respectively.
Relationship: entailment
Domain: Networks
Sentence 1: There exists strong correlationsbetween session size(i.e., the number of bytes of a session) and state machine char­acteristics.
Sentence 2: figure 13 indicates that large ses­sions tend to have high fraction of DCH occupation time (PDCH)as well as low PFACH and PPROMO = PIDLE.DCH + PFACH.DCH values.
Relationship: entailment
Domain: Networks
Sentence 1: Figure 9: Nearest neighbors in square-grid space Calibration-based algorithms are most effective when RSSI values acquired at center grid positions and their nearest neighbors exhibit clearly distinguished statistical patterns.
Sentence 2: if mean RSSI values of a center position and its primary nearest neighbors are the same, measurements at such grid alone contribute errors up to g. This inclusion of errors is known as aliasing in signal space, where RF properties of grid positions are blurred by physically different grid positions.
Relationship: entailment
Domain: Networks
Sentence 1: Table 1 summarizes the parameters set­ tings for all measurement trials.
Sentence 2: we varied the RF channel (CH), the transmission power (PW), the pro­cessing of CRC corrupted frames, whether the channel clear assessment (CCA) and calibration (CA) was activated, and whether the measurements took place at a weekend (WE) or during daytime (DT).
Relationship: entailment
Domain: Networks
Sentence 1: We consider a sequence of such graphs indexed by n. The maximum expected degree m and the average expected de­gree d may, andindeed typically will,depend on n. In mod­els of real networks, we can typically expect d to remain bounded or to grow slowly with n, saylogarithmically,while m growsmorequickly,say assomefractionalpowerof n. In this paper, we only assume the following: .. v m 1 d = d> 0,d = o(m),m = nd, = on.-1 .
Sentence 2: (7) d Here, d is a constant that does not depend on n. the average expected degree is uniformly bounded away from zero.
Relationship: entailment
Domain: Networks
Sentence 1: Therefore, in this section we reduce EEC under the constraint that EDR of the forwarding sequence is above threshold R. Unlike optimizing EED, in Equation 5, where i represents the index of forwarding node in the forwarding sequence, the i value changes for each already selected forwarding node as we backwardly add early nodes.
Sentence 2: the decisions made to include or exclude an early node in the forwarding se­quence does affectthe expected energy of laternodes.
Relationship: entailment
Domain: Networks
Sentence 1: Its ac­tivity is split into o.ine activity at .ow lifetime timescale which performs the more complex activity and online ac­tivity at the frame transmission timescale.
Sentence 2: the online activity executes the enforcement policy with a periodic scheduler based on Earliest Deadline First (EDF) algorithm [20] plus Stack Resource Policy (SRP) [6] in order to take into account the non-preemptability of frame trans­missions that are considered critical sections.
Relationship: entailment