sadat2307/MSciNLI huggingface dataset:
Domain: Networks
Sentence 1: To study this issue, we .rst de.ne a method for calculating the load on each mesh node under a hypothetical population­based model.
Sentence 2: we consider the o.ered load of alocation i to be a function of the client demand qi (in kilo­bits) at i and the expected time required to serve a .xed sized packet, di, (measured in seconds per kilobit) at i.Our framework provides the estimate for di based on the pre­dicted modulation rate at location i, which is in turn based on the SNR at the location.
Relationship: entailment
Domain: Networks
Sentence 1: As expected, the NNS algorithm (heuristics) needs less resources than our VCC algorithm with lower density, but with higher one it needs much more than VCC.
Sentence 2: in any settings with 0.7 or higher density, NNS needs more resources than VCC and with 1.0 density (which is the severest case where stations are very populated) in Fig.
Relationship: entailment
Domain: Networks
Sentence 1: These features make Nemo ideal for measuring the power consumption of sensor networks under a wide range of applications and op­erating conditions.
Sentence 2: as many sensor networks operate under low duty cycles and stay in sleep state (with just a few uA current consumption) most of the time, the .ne measurement resolution (~0.01uA) of Nemo enables accurate assessment of the system lifetime.
Relationship: entailment
Domain: Networks
Sentence 1: (b) ACK-based feedback Figure 10: Two different ways for the receiver to notify the mobile client of the best antenna for symbol-based an­ tenna selection With PHY support, all the antennas are examined in a single packet.
Sentence 2: a mobile client transmits a series of PHY training periods, each from one of the antennas so that the receiver can estimate the RSSI of all antennas and notify the mobile client of the best.
Relationship: entailment
Domain: Networks
Sentence 1: [13]) to obtain a(1 -e)-approximation to the linear relaxation of Alg5-IP in time polynomial in N, C and 1/e.
Sentence 2: maxWeight-Alg5 then rounds the solution to this linear relaxation by choosing a single set Si to assign to user i.we set Si = S with probability XSi .
Relationship: entailment
Domain: Networks
Sentence 1: Instead, Snooze only decides how many antennas should be used commensurate with the client s traf.c load, leav­ing it to the rate control algorithm to decide which anten­nas are used and what rates and channel widths are assigned to each antenna.
Sentence 2: snooze limits the NIC s con.guration search space, and thereby directly controls the power consumption of the NIC, which depends upon how many antennas (or, more precisely, RF-chains) are enabled.
Relationship: entailment
Domain: Networks
Sentence 1: Since {g1.g4 s-a-1, g6.g7 s-a-1} are in the intersection of both implications, they are testable only if g5 is 0.
Sentence 2: f(wr=0)= {g1.g4 s-a-1, g6.g7 s-a-1}.
Relationship: entailment
Domain: Networks
Sentence 1: In this paper, we will be concerned with customer us­age as determined from direct measurements of customer traffic at routers or other network elements.
Sentence 2: we take usage to mean the total number packets or bytes in those packets (or possibly some combination of these) that are observed during some billing interval, these totals be­ing differentiated at some accounting granularity, e.g., by customer, service class, source and/or destination IP ad­dress, application, or some combination of these.
Relationship: entailment
Domain: Networks
Sentence 1: m=k) and m=2 h , we can show that the prob­ tlns2o/yn W ability of a counter being -to be 1.4e-6 which is minuscule for our purpose.
Sentence 2: one in a million counters can over.ow Fast Hash Table Naive Hash Table Analysis Simulation Analysis Simulation basic pruning balancing 1 19.8  18.8) 2 0 740.32 734.45 2 3 l 3 0 0 28.10 27.66 3 22 l U 0 0 0 0.72h2 0.70 4 0 0 0 32- 32- 5 26 0 0 0 2u 32 6 3 .
Relationship: entailment
Domain: Networks
Sentence 1: The SINR model for UWB networks was first given in [4], which is different from that for narrowband networks [6].
Sentence 2: the achieved signal-to-interference­plus-noise ratio at the receiver of link i can be represented as: (,i xj ) Pd x a SINR i = i = ß N 0 a Ri[.+ Tfs2 .Pk ( k , x j d x ) ] k =1,k .i where Pi denotes the average transmission power of link i s transmitter, xi ; Ri denotes link i s data rate, and R = 1/( NN T ) ; N denotes the i shcs number of pulses per symbol, Nh the number of timeslots per Pulse Repetition Interval (PRI), and Tc the pulse duration; Tf is the PRI, and Tf = Nh Tc; s2 is a parameter depending on the shape of the monocycle; .
Relationship: entailment
Domain: Networks
Sentence 1: (1), and the superposition principle that holds for linear systems, we will derive the performance of E2DCA by considering a step disturbance: di(n)= d0 ·1(n), where 1(n) is the unitary step function.
Sentence 2: e2DCA gives to the ith queue a way to compute ui(n) at the beginning of the successful enhanced contention, (i.e., to determine the amount of bandwidth obtained with the n th successful enhanced contention) in order to drive the queuing delay ti to the desired target ti T .each station evaluates this bandwidth independently from other stations, that is, E2DCA is a distributed bandwidth alloca­tion algorithm.
Relationship: entailment
Domain: Networks
Sentence 1: As NCDNs control both the content distribution and the network infrastructure, the costs and objectives of their in­terest are di.erent both from a traditional CDN and a tradi­tional ISP.
Sentence 2: an NCDN is in a powerful position to place content in a manner that shapes the tra c de­mand so as to optimize both network cost and user-perceived latency.
Relationship: entailment
Domain: Networks
Sentence 1: Our goal is to exploit correlations between these signals and identify groups of variables a.ected by the same event.1 2.2 Exploiting Correlations As discussed previously, we expect that if a set of pre.xes that are a.ected by the same event, updates for these pre­.xes would reach a given vantage point at approximately the same time.
Sentence 2: the updates for pre.xes a.ected by the same event will share a lot of temporal sim­ilarity.
Relationship: entailment
Domain: Networks
Sentence 1: To address these issues, inherent to syntactic approaches, we propose an inference algorithm that takes into account the semantic associated with message parts.
Sentence 2: our approach does not limit the semantic de.nition of a mes­sage part neither to its data type (ASCII or binary) [6] nor to its appearance probability [26] but instead leverages contex­ tual and environmental information to improve both mes­sage clustering and message format inference.
Relationship: entailment
Domain: Networks
Sentence 1: The algorithm is randomized and runs in polynomial-time.
Sentence 2: theorem 1 shows that we can solve the covering and maximization problems (1) and (2) to provide a sensor place­ment for which the communication cost is at most a small factor (at worst logarithmic) larger, and for which the sensing quality is at most a constant factor worse than the optimal solution.
Relationship: entailment
Domain: Networks
Sentence 1: In an effort to understand the surprising ef.ciency of this simple algorithm, Dimakis and Walrand have identi.ed a suf.cient condition for the algorithm to achieve the entire capacity region of the network for single-hop traf.c [6].
Sentence 2: they have shown that if the network topology and interference structure sat­isfy a condition known as local pooling, then the LQF algorithm achieves the entire capacity region.
Relationship: entailment
Domain: Networks
Sentence 1: Second, the mean-square ergodicity implies that, as the observation interval dincreases, the random variable Yi,d (fj) converges to xj in the mean-square sense.
Sentence 2: the variance of Yi,d (fj) decays to 0 as d.8, i.e., [ ] ( )2 lim E Yi,d (fj) -xj =0.
Relationship: entailment
Domain: Networks
Sentence 1: In our analy­sis, we consider only the additional risks posed by QoS-Guided Route Discovery, and not those caused by the underlying ad hoc network routing protocol.
Sentence 2: because QoS-Guided Route Discovery can be implemented with only one additional packet type (E STAB L I SH FLOW), and because the authentication of that packet can generally be performed in the same way as for ROUT E REQUE STs, we ignore general routing attacks such as corrupted node lists, wormhole attacks, routing message replay, spoofed signaling, fabricated routing messages, routing loop for­mation, and the participation of unauthorized nodes.
Relationship: entailment
Domain: Networks
Sentence 1: The s values of each cell are increased by the delay of thepacket(i.e., the currenttime at receiver minus the timestamp embedded in the packet at sender), while n values are increased by one (i.e., maintains a count of the packets that hashed to that cell).
Sentence 2: s values contain the sum of all packet delays that hit that cell, while n values represent packet counts.
Relationship: entailment
Domain: Networks
Sentence 1: The techniques proposed include reducing the slot time and SIFS based on the actual maximum air propagation time within a WLAN, in addition to exploiting advances in PHY technologies and thus facilitating faster MAC processing and carrier sensing.
Sentence 2: it was found that a reduction in the time allocated for air propagation delay, MAC processing, and channel sensing can provide non-trivial throughput performance increases of up to 26.8 % and 30 % at PHY bit-rates of 54 Mb/s and 216 Mb/s respectively for the best case scenario.
Relationship: entailment
Domain: Networks
Sentence 1: Hence, after the transmission, each receiver can extrapolate how the performance on a certain subchannel would have been if all subcarriers had been allocated to any of the m transmitters, since it knows how the performance was for one (or more) subcarrier of that transmitter in the subchannel.
Sentence 2: we use a static allocation of subcarriers which allows us to infer after the transmission how any pos­sible allocation of subchannels would have been.
Relationship: entailment
Domain: Networks
Sentence 1: We de.ne network performance as the maximum throughput on the network under heavy traf.c conditions based on a gravity model [38].
Sentence 2: we consider .ows on all source­destination pairs of edge routers, such that the amount of .ow Xij between source i and destination j is proportional to the product of the traf.c demand xi, xj at end points i, j, Xij = axixj , where a is some constant.
Relationship: entailment
Domain: Networks
Sentence 1: Without cooperation, an opportunistic system must in­crease the sensitivity of its detection algorithm by an amount equal to that of the deepest shadow/fade under which it must detect.
Sentence 2: while an unshadowed system might only need to detect a 0dB signal to avoid interfering, it would need to detect a -56dB signal to account for the possibility of a 56 dB shadow/fade.
Relationship: entailment
Domain: Networks
Sentence 1: From the attackers point of view, all the nodes in .at l are equally likely to be IPd with prob­ 11 ability = , all the nodes in C-1 IPai are un­ |IPal | Li=0 likely to be IPd, and the rest nodes are equally likely to al 1-|.
Sentence 2: |/L 1-./L be IPd with probability X t = X , where X = al N - LC-1 |IPai |-|.| = (2ß - l)L - .. attackers i=0 t assign the following probabilities: swarms to satisfy diverse address-privacy requirements is an ... .. open problem worthy of further study.
Relationship: entailment
Domain: Networks
Sentence 1: We observe that for 20% of the replication in­tervals the following properties hold: (i) the interval includes only a single packet (and its phantoms), (ii) the number of replicas in the interval is less than in the adjacent intervals, and (iii) the repli­cation levels in the adjacent intervals are equal.
Sentence 2: these intervals represent a slight depression in an otherwise steady replication level, which likely re.ects measurement loss.
Relationship: entailment
Domain: Networks
Sentence 1: Additionally, the values C1 and C2 are selected in a way that considers the expected additional coverage EAC.
Sentence 2: c2 (sparse network threshold) should be in a way larger than C1 (dense network threshold) in order for the node to have a higher chance to rebroadcast in a sparse area given that the EAC of a sparse network is higher than that of a dense network.
Relationship: entailment
Domain: Networks
Sentence 1: The main issue is that our underlying execution model (PTS) by means of which the semantics of IML is de.ned uses external attackers and CryptoVerif uses internal attackers.
Sentence 2: in IML the attacker is part of the semantics, whereas in CryptoVerif it is part of the process, running in parallel with the process representing the protocol.
Relationship: entailment
Domain: Networks
Sentence 1: On the other side, the wireless router will use IP address(es) from the RLOC address space on the interface(s) used to communicate with the other routers.
Sentence 2: in this context, an EID-to-RLOCmapping allows toidentify a mobile node(the EID) andlocateitinthewireless network(theRLOC).mappings say which mobile node is associated to which router.
Relationship: entailment
Domain: Networks
Sentence 1: RVIP seeks to implement the same behavior on RVIP-Real and RVIP-Virtual.
Sentence 2: when RVIP-Real sends out a live packet, the RVIP-Virtual sends out an equivalent packetin theSVN.Likewise, whenRVIP-Virtualreceives a virtual packetfrom other virtualnodesintheSVN,theRVIP-Real receives an equivalent packet.
Relationship: entailment
Domain: Networks
Sentence 1: This implies that ImGlobal is at least as good as a strategy with optimal backward message propagation, and whose starting time of the geometric process at node i is delayed such that the leftmost 1-node in the network is at least c positions away from i.
Sentence 2: denoting with StartIG(d,¯t¯) the same event as Start(d,¯t¯) when strategy ImGlobal is used for message dissemination, we can write P rob(StartIG(d,¯t¯)) = P rob(Start(d¯+ c, t¯)).
Relationship: entailment
Domain: Networks
Sentence 1: First, similar to the result of the CE model, the ratio of fC : fT : fB is .xed when the ratio |C| : |T | : |B| is .xed.
Sentence 2: we have the scaling effect of the normalized Shapley value function as: .B(b, t, c)= k.B(kb, kt, kc) .
Relationship: entailment
Domain: Networks
Sentence 1: 3.4 Problem formulation and high-level view of stepsfollowed to solveit The goal of this paper is to evaluate the potential bene­.ts that o.oading techniques could bring to operators.
Sentence 2: theirinterestis to compare whatis the resource consumption in their network without applying o.oading and when applying o.oading on a large scale (see Section 7).
Relationship: entailment
Domain: Networks
Sentence 1: Grouping these pre.xes the router switched between two comparable routes (e.g., because of a change in the IGP path costs).
Sentence 2: we group events for different destination pre.xes that (i) belong to the same category (using the taxonomy from Section 5.2), (ii) undergo the same kind of transition (from better to worse, or worse to better), and (iii) start no more than threshP seconds after the .rst event.
Relationship: entailment
Domain: Networks
Sentence 1: The results also con.rm the importance and effectiveness of our model in assisting operators to reason about network-wide CoS operations.
Sentence 2: we have demonstrated the potential of the tool in extracting all possible .ow treatments across a given set of devices, identifying non-standard and potentially anomalous .ow treatments and shadowed con.guration policies, and deriving network-wide views of CoS designs.
Relationship: entailment
Domain: Networks
Sentence 1: Finally, an optimised solution combining the RRM results and the Trading Module output is obtained, enabling Spectrum Broker to sell/assign TVWS frequencies to the corresponding secondary systems under the RTSSM regime/policy.
Sentence 2: spectrum Broker is responsible for obtaining the best-matching solution, through an optimisation-based process, which constitutes a NP-hard problem, thus an approximation algorithm is required in order to solve the auction process.
Relationship: entailment
Domain: Networks
Sentence 1: While the conventional QoS vision puts this kind of traffic in the second highest priority class, that is, AC 2 (corresponding to AC_VI class in the vision of 802.11e/WMM), we allocate this traffic typology to the best effort class (AC 1).
Sentence 2: while the conventional QoS definition focuses solely on delay sensitivity of an application, we have further considered bandwidth requirement of an application, in addition to its delay sensitivity, in our traffic class definition.
Relationship: entailment
Domain: Networks
Sentence 1: Thus, the utility of link ek under pro.le d by payment scheme p becomes p k(d ) - ck = p k(d) - ck = p k(d) - (pk(d) - d 2 ) = - d 2 < 0.
Sentence 2: under pro.le d , when link ek reports its true cost, it gets a negative utility under payment scheme p .
Relationship: entailment
Domain: Networks
Sentence 1: n = 2m+1, it fol­ = 2m+1 lows that the event {Xm+1 > 2m .
Sentence 2: xm+2 } occurs with probability at least e -2e -c , whichgoesto oneforlarge positive c. the algorithm is terminated by the end of the m +2-th phase w.h.p.
Relationship: entailment
Domain: Networks
Sentence 1: Figure 4: The rectangles and policy subunits after the subnet­level reachability setshavebeendecomposed.
Sentence 2: we refer to theprojections of the rectangles unto the x-axis(i.e., thesourcesubnets) as policy subunits ,alsoshowninFigure4.eachpolicy subunit is a maximal contiguous sequence of addressesinthesourcesubnet all of which canreach the same portions of the destination subnets.
Relationship: entailment
Domain: Networks
Sentence 1: Given that equation 1 seems to systematically underes­timate higher latencies, exactly the error that we want to avoid, we realized that we could modify the equation to reduce these errors.
Sentence 2: we experimented with several modi.cations, including a linear multiplier, but one simple approach is: function Modi.edEqnOne(RTT, MSS, Length,w4m, p, CompWeight) temp = EquationOne(RTT, MSS, Length, wm, p); return(temp + (temp*temp*CompWeight)); we overpredict by a term proportional to the square of the original prediction.
Relationship: entailment
Domain: Networks
Sentence 1: 30 0.75 ..... ..... 0.5 0.25 1000 2000 3000 4000 5000 Assured Rate (Kb/sec) Figure 12: Service differentiation with over-subscribed case 4.2 Effect of assured rate A In this section, we study the effect of assured rate A on the achieved rate of a TCP session.
Sentence 2: we examine whether it is pos- sible to achieve service differentiation across a set of TCP sessions by choosing different assured rates.
Relationship: entailment
Domain: Networks
Sentence 1: However, besides the di.culty of acquiring local host/server/access information, our main goal is to identify to what extent the IPv6 Internet may itself be responsible for a slow IPv6 adoption.
Sentence 2: given the lack of meaningful incentives to entice exist­ing IPv4 content providers to become IPv6 accessible5 , avoiding disincentives is vital; and poor connectivity quality would certainly qualify as a disincentive (see [5] for a related discussion).
Relationship: entailment
Domain: Networks
Sentence 1: Therefore, in order to understand the phenomenon of non-cooperative systems Koutsoupias and Papadimitriou [14] initiated investigations of the coordination ratio, which is the ratio between the worst possible Nash equilibrium and the social (i.e., overall) optimum.
Sentence 2: this analysis seeks the price of uncoordinated sel.sh deci­sions ( the price of anarchy ).
Relationship: entailment
Domain: Networks
Sentence 1: zm = f(.m) - P h£.£ m,m =1,...,M, £=0 s 2( PL £=2 h£e(e- 1).£-2)= y ' Vy (11) V : 0 zm = 0,m =1,...,M, with decision variables z1,...,zM , the elements of V,and h0,...,hL.
Sentence 2: (11) minimizes the following L1 norm ||(f(.1),...,f(.M ))-(u(.1),...,u(.M ))|| and ensures that u(·) is convex and underestimates f(·)at .1,...,.M .
Relationship: entailment
Domain: Networks
Sentence 1: Vision Daniele Tarchi Romano Fantacci Dania Marabissi Department of Electronics and Department of Electronics and Department of Electronics and Telecommunications Telecommunications Telecommunications University of Florence University of Florence University of Florence Firenze, Italy Firenze, Italy Firenze, Italy daniele.tarchi@uni..it romano.fantacci@uni..it dania.marabissi@uni..it ABSTRACT Wireless communications have achieved a great attention during the last decades due to the easy implementation, pos­sibility of delivering multimedia services to rural communi­ties, suitability for public safety and emergency communi­cations.
Sentence 2: a wireless network designed for an emergency scenario has to be capable of managing all the phases of an emergency situation, from the prevention to the post-emergency management.
Relationship: entailment
Domain: Networks
Sentence 1: Streaming Quality Qk, the streaming quality re­ceived at Hk, measuredasnumberofreceivedlay­ers.
Sentence 2: qm is used to denote the number k of layers received from the supplying peer Hm.Con­sequently, Q0 k is thenumberoflayersreceivedfrom server H0.
Relationship: entailment
Domain: Networks
Sentence 1: A Multi-Channel VANET Providing Concurrent Safety and Commercial Services Tony K. Mak Kenneth P. Laberteaux Raja Sengupta Department of Civil and Toyota Technical Center Department of Civil and Environmental Engineering Ann Arbor, MI 48105 Environmental Engineering University of California Berkeley, CA 94720-1740 klaberte@acm.org University of California Berkeley, CA 94720-1740 tonykm@path.berkeley.edu raja@path.berkeley.edu ABSTRACT In this paper, we propose a medium access control (MAC) protocol to support the multi-channel operation for dedi­cated short range communication (DSRC).
Sentence 2: we focus on the challenge of providing potentially high­bandwidth for non-safety applications provided by road­side infrastructure, without compromising safety commu­nication occurring in a separate channel.
Relationship: entailment
Domain: Networks
Sentence 1: In other words, it can be much more elaborate than just the simple switching ASIC that is often postulated in SDN discussions, and so is deserving of more sophisticated abstractions.
Sentence 2: the use of FPGA (programmable logic) technology in switching hardware offers scope for soft hardware , with the potential to blur the distinctions between traditional capabilities and roles of hardware and software, and hence open the door to Softly Defined Networking that involves a less restrictive view of hardware-software partitioning.
Relationship: entailment
Domain: Networks
Sentence 1: It thus pro­ of frame size improves RSE of |C |.
Sentence 2: when the vides us scalable authentication scheme for large-scale RFID number of tags is 1,000,000, the RSE is 0.73 with f=32 and  1  Relative Standard Error 0 Reciprocal Counterfeit Ratio Reciprocal Counterfeit Ratio (a) Fixed total transmission cost at 20KB.
Relationship: entailment
Domain: Networks
Sentence 1: R.S According to [34], this implies that, in the actual binomial case,  -3 -2 PrIR = ß|S |/2= (|n-U(t-1)|)·O(n )= O(n ).
Sentence 2: r.S with probability 1 - O(n -2), there are at least a 2ß f fraction of all the r-cells in which nodes belonging to U(t - 1) can not receive m in time slot t. Meanwhile, by some standard Cherno.
Relationship: entailment