sadat2307/MSciNLI huggingface dataset:
Domain: Networks
Sentence 1: Since the contents of the UII~ depend on the previous IR, each client has to receive the previous IR in order to use the UIR to validate its local cache.
Sentence 2: if a client missed the last IR when it receives an UIR (by comparing the timestamp associated with the UIR and the timestamp of the last received IR), it cannot answer any query until it receives the next IR, and use the received IR to validate its cache.
Relationship: entailment
Domain: Networks
Sentence 1: NAMING AND SECURITY Network security is often broken down into four components: integrity, con.dentiality, provenance, and availability.
Sentence 2: the security measures in the network infrastructure and end-host network stacks must ensure that data is delivered reliably (avail­ability), comes from the appropriate original source (provenance), and has not been tampered with (integrity) or read by others (con.dentiality).
Relationship: entailment
Domain: Networks
Sentence 1: Specifically, our experimental results indicate that (i) using a combination of request sizes and air interface data rates is desirable, (ii) time multiplexing users in continuous bandwidth conditions is superior to code multiplexing, and (iii) when user rates are discretized, a combination of time and code multiplex- ing with a dominant time muliplexing component, results in broadly satisfactory performance with various measures.
Sentence 2: eDF schemes based on the stretch and flow metrics, such as RMAX and SRPT, strike an excellent bal- ance between network and user throughputs.
Relationship: entailment
Domain: Networks
Sentence 1: If a signal is bandlimited with B, then the cyclic spectrum Sx(a, f ) can be nonzero only for a diamond­shaped region around the origin of the bi-frequency plane, as shown in Figure 2.
Sentence 2: the potential cyclic spectrum support for a bandpass waveform is directly relat­ed to its center frequency and bandwidth, and the ISM band during one round scanning can be reasonable seen as non­busy which implies signal sparseness.
Relationship: entailment
Domain: Networks
Sentence 1: However, in this case, the payoff the player gets is the net change of the weighted-delivery debt.
Sentence 2: the payoff is an N -dimensional vector v =[v1,v2,...,vN ], where vn =(qn - 1)/pn if the server ac­complishes a job for client n in the interval, or vn = qn/pn if not.
Relationship: entailment
Domain: Networks
Sentence 1: Whatisrelevantto us,however,istheimpact ofdy­namically generated data to the scalability of the forwarding and cachingplane.
Sentence 2: there is no bene.t in caching dynamically gener­ated data that are accessed only once, as it reduces the speed of theforwarding nodesdue to cachelookup andit causes eviction of otherdatathatmightbeaccessed morethanonce.
Relationship: entailment
Domain: Networks
Sentence 1: np For such a k = T(1/np), we insure that G(k) has a giant n,bp,p component of size at least g(d)n and diameter bounded by O(k log n )= O(klogn).
Sentence 2: after O(log n ) time steps, log n. np we can apply the result of the previous case, and, after ad­ditional log n steps, .ooding is complete.
Relationship: entailment
Domain: Networks
Sentence 1: However, Gaussian distribu­tions have certain attractive properties which makes them a good choice for modeling priors.
Sentence 2: they yield another Gaus­sian posterior distribution with easily computed parameters which enables performing the cleaning at the sensor level, they are known to be analytically tractable, they are useful for query processing and yield closed form solutions as we will show in Section 4, and .nally they also have the maximum entropy among all distributions [9].
Relationship: entailment
Domain: Networks
Sentence 1: .1(d/va) vaE[N2(d/va)] vb vb - 1 - ln va va (53) It is noteworthy that (53) is completely determined by the ratio vb/va and is independent of the transmit range r and connection time limit c. With reference to Figure 5(a), we observe that the ratio .1(d/vb)/.1(d/va) is always larger than 1 for all choices of va and vb.
Sentence 2: when the difference vb - va is large, say va =2 and vb =30, the ratio is as large as 2.25.
Relationship: entailment
Domain: Networks
Sentence 1: Clearly, this needs to change if IPv6 is to become a viable solution for an impending scarcity of IPv4 ad­dresses.
Sentence 2: failure to convince the exist­ing IPv4 Internet to adopt IPv6, i.e., become acces­ 1See http://www.nro.net/news/ipv4-free-pool-depleted.
Relationship: entailment
Domain: Networks
Sentence 1: Note thatthis nullingat rx1does notprevent tx2fromdelivering its packet to its own receiver rx2.
Sentence 2: say tx1 is trans­mitting the symbol p and tx2 is transmitting the symbol q. Intu­itively, since rx2has two antennas, the received signallivesin a2­dimensional space.In this space, the two symbols p and q lie along two different directions, as shown in the bottom graph in Fig.
Relationship: entailment
Domain: Networks
Sentence 1: Abstract One of the major challenges in today s economy concerns the reduction in energy usage and ....2 footprint in existing Public buildings and Spaces without signi.cant construc­tion works, by an intelligent ICT-based service monitoring and managing the energy consumption.
Sentence 2: interoperability between heterogeneous devices and networks, both existing and to be deployed is a key features to create ef.cient services and holistic energy control policies.
Relationship: entailment
Domain: Networks
Sentence 1: When the 3G module adopts various bandwidths in a time interval, the total amount of transmitted data is the sum of the multiplications of each adopted band­width and its corresponding time duration.
Sentence 2: when th and tl are the time intervals that a 3G module operates under Bh and Bl, respectively, then the total amount of transmitted data is Bhth + Bltl.
Relationship: entailment
Domain: Networks
Sentence 1: Theorem 3.3 implies that given that the maxima] cycle length in the Mobility graph G is n, a sufficient condition under which D*(zl, 12) -d(zl, =2), where 11 and 12 are cells visited by the user, is that R = [.~2].
Sentence 2: if G is a ring graph of length n, a sufficient condition is that R -- [~_Z].
Relationship: entailment
Domain: Networks
Sentence 1: The problem of ensuring strong connectivity while minimizing some measure of energy consumption has also been considered in a more theoretical framework, where it is referred to as the ranged assignmentd problem.
Sentence 2: it has been shown that determining an optimal range assignment is solvable in polynomial time in the one-dimensional case, while it is NP-hard 1 A directed graph G=(V,E) is said to be stronglydconnected if, for every u,v.V, there exist directed paths from u to v and from v to u. in the two and three-dimensional cases [3,8] 2.
Relationship: entailment
Domain: Networks
Sentence 1: Never­theless, [2] s approach is based on cryptographic indistinguishabil­ity which measures security in regard to key length n. Our ap­proach is different from this earlier work as we seek to prove that the failure probability of a secure routing protocol is negligible in regard to a network metric x, which is the network scale N in this paper.
Sentence 2: the larger the network scale is, the more secure the network is in an asymptotic manner.
Relationship: entailment
Domain: Networks
Sentence 1: Temporary schedule inconsistencies During the time of multiple frame durations it takes for a schedule (control or data) to reach from the root to the remaining nodes, over several hops, the network is in a tem­porarily inconsistent state.
Sentence 2: some nodes are follow- Figure 4: LiT MAC operation: an illustration ing the old schedule while some others have learnt the new schedule.
Relationship: entailment
Domain: Networks
Sentence 1: For example, in Figure 1, we show how closely we can approximate xln(x) 1.05 - x 0.95 using 10(x ) within the interval [1,1000].
Sentence 2: if all transport-layer .ows in an OD .ow S have less than 1000 packets, we can estimate the entropy norm of the 8000 7000 6000 5000 4000 3000 2000 1000 0  Figure 1: Comparison of entropy function and the approximation OD .ow as -||S||0.95 It turns out that this 10(||S||1.05 0.95 ).
Relationship: entailment
Domain: Networks
Sentence 1: We simulated the tra.c pro­duced by a six-electrode ECG sensor, which sends 10-byte packets (a timestamp and 1 byte per electrode) at a rate of  Table 1: Comparing energy cost of three protocols SlyFi SPINS Adapt-lite Overhead 256-bit 64-bit variable Security &#38; Privacy1 Yes No Yes Avg.
Sentence 2: power (mW) 27.60 14.74 13.11 Battery life (hr) 25.49 47.74 53.68 1whether the protocol achieves the security and privacy goals described in Section 5.
Relationship: entailment
Domain: Networks
Sentence 1: To go further we also evaluated how this correlation between a user and her friends impacts the probability for this user to be connected by taking into account the aggregated behavior of her friends.
Sentence 2: we wanted to answer the following question: is the probability of a user to be connected at a given time impacted by the fact that her friends are also connected at this particular time or are these two independent events?
Relationship: entailment
Domain: Networks
Sentence 1: = .max/.min, as a function of a = 1, corresponding to the link margin al­located for fading.
Sentence 2: -10 log10 a is the link margin in dB; for example, 3 dB corresponds to a = 12 .As showed in Figure 2, we note that a link margin of 5 dB is enough to reduce the outage probability to 2% for antenna separations bigger than 5.. 0.35 where .max and .min correspond to the eigenvalues of the Wishart matrix HHH .Note that .
Relationship: entailment
Domain: Networks
Sentence 1: The 140 nodes we did measure still provides ample room v for optimization savingafactorof 1 140 could still reduce band­ 4 width to one third of what the naive approach would require.10 Moreover, the savings would continue to grow with more nodes.
Sentence 2: we expect that one of the most effective applica­tions of our routing algorithm will be to the Skype scenario that we outlined in Section 2.
Relationship: entailment
Domain: Networks
Sentence 1: 3.2.1 Entropy Timeseries Previous work [13] has demonstrated that entropy time­series of the four main IP header features (source IP, desti­nation IP, source port, and destination port) is a rich data type to analyze for tra.c anomaly detection.
Sentence 2: for every measurement vector v i at time i there are four mea­surements vi,j ,...,vi,j+3 for every ingress router (for exam­ple).
Relationship: entailment
Domain: Networks
Sentence 1: It is our strong feeling that, considering that p is likely to be relatively high in realistic VANET scenarios (say, p = 0.75), the dominating factor in the above described stochas­ tic process is the starting time ti of the geometric process.
Sentence 2: we believe performance is optimized when ti is reduced as much as possible, and the best possible value of p¯is obtained after minimizing ti.
Relationship: entailment
Domain: Networks
Sentence 1: Finally, we point out that even though our discussion in this paper caters to ISPs and their retaining control over the Internet, it also lays the seeds for new entrants to enter the .eld as pro­posed in [1].
Sentence 2: iSPs might choose to sell their will­ingness to route anycast to the third-party Next Generation Service Providers (NGSPs) proposed in [1].
Relationship: entailment
Domain: Networks
Sentence 1: We deal primarily with the global behavior that emerges when an event of interest is detected by a randomly chosen sensor node in torus lattice T containing sink nodes at concentra­tion c. This event should be then reported to one of the sink nodes through random walks.
Sentence 2: we study thedelay timein numberofhops experiencedby thepacket thatbearsthateventtobetrappedby the .rstencountered sink node.
Relationship: entailment
Domain: Networks
Sentence 1: [6] Given various medical sensors and algorithms for localization, Heracleia researchers have created a pervasive environment of sensors in order to improve the quality of life, provide better safety for the aged and support the caregivers of Alzheimer s patients.
Sentence 2: they have developed distributed localization algorithms, which, combined with up to date sensor information, provide information on patient movements.
Relationship: entailment
Domain: Networks
Sentence 1: In contrast to ping and traceroute (as well as other mid­dlebox detection tools like tracebox), we do not leverage out-of-band mechanisms (e.g., ICMP) in our design so as to avoid complications inherent in relying on external depen­dencies.
Sentence 2: note the di.culties with Path MTU Discovery as TCP operation is linked with ICMP traver­sal [33, 36].
Relationship: entailment
Domain: Networks
Sentence 1: Assuming that MDC and MRC have the same coding ef.ciency, i.e., they share the same parameters L and N per delivery segment within which we perform NC, the bene.t of applying NC is the same for both schemes.
Sentence 2: mDC-C4isexactlythesameasMRC-C4andtheresults forMDC-C4applyinthesamewaytoMRC-C4too.Thiscanbe explainedbythe propertiesofthe canonical scheme.Ifanyhigher numberedlayerisdecodedbyMDC-C4,alllayersbelowitarealso  (a)MDC-C4vs.
Relationship: entailment
Domain: Networks
Sentence 1: As benchmark scheme we consider an epidemic-based data dissemination  1 0.8 Hit Ratio 0.6 0.4 0.2 0 Figure 7: Hit ratio of the least popular channel for e.cient.
Sentence 2: we have shown that our model is suf­ .ciently accurate to capture the transient and steady-state behaviors of key performance indexes of the data dissemi­nation system, such as data replication levels and hit ratios.
Relationship: entailment
Domain: Networks
Sentence 1: Theorem 1 For any a>d = 2, there exists a constant 2a·3d c1(a, d) = depending only on a and d such that a-d Tp = c1(a, d) for wireless multicast.
Sentence 2: there is a constant which depends only on the power gradient and the dimension of the space, that bounds the coding advantage for minimizing the power needed for multicast.
Relationship: entailment
Domain: Networks
Sentence 1: For bounded growth graphs, i.e., graphs where, for any node u and any radius r the number of nodes within distance 2r from u is at most a constant times the num­ber of nodes within distance r, our augmentation process proceeds with high probability in O(log n log D)communi­cation rounds, with O(log n log D)messages of size O(log n) bits sent per node and requiring only O(log n log D)bit space in each node, where n is the number of nodes, and D the diameter.
Sentence 2: with the only knowledge of orig­inal distances, greedy routing computes, between any pair of nodes in the augmented network, a path of length at most O(log2 n log2 D)with probability 1 - O(1/n),and of expected length O(log n log2 D).Hence, we provide a dis­tributed scheme to augment any bounded growth graph into a small world with high probability in polylogarithmic time while requiring polylogarithmic memory.
Relationship: entailment
Domain: Networks
Sentence 1: To characterize the inter-service time, we introduce a counter Tl for each link l, namely Time-Since-Last-Service (TSLS), to keep track of the time since link l was last served.
Sentence 2: each Tl increases by 1 in each time slot when link l has zero transmission rate, either because it is not scheduled, or because its channel is unavailable, i.e., Cl[t] = 0, and drops to 0, otherwise.
Relationship: entailment
Domain: Networks
Sentence 1: (5) Ti.F,Rj.F Based on the notion of worst-case intrusion, problem (5) is of great interest for the intruder detection problem.
Sentence 2: solving problem (5) allows us to answer an im­portant question: How many transmitters and receivers are needed and where do we place them for providing the re­quired coverage such that at least one BR will receive an SNR above some prede.ned threshold, regardless of the in­truder s path?
Relationship: entailment
Domain: Networks
Sentence 1: CONCLUSION In this paper, we studied di.erential games in networked environments with constrained communication resources lead­ing to delays, losses and .nite rates in information state up­dates.We limited our focus to a pursuer-evader game known in the literature as asset protection , and formulated opti­mal strategies under communication constraints, established bounds on the information requirements of these strategies, and derived scaling laws for these bounds.
Sentence 2: we showed that the min-max optimal pursuer strategy of the full information game extends to networked games, provided that the sampling period and the delay for the evader state information updates scale linearly with the pursuer-evader distance.
Relationship: entailment
Domain: Networks
Sentence 1: The fusion algorithm in Section 4 accomplishes this task by using mea­surements from n> 3 sensors.
Sentence 2: it exploits the availability of more sensors to produce a robust solution in the presence of noise (i.e., background radiation), statistical errors (due to the random nature of the source being mea­sured), and measurement errors (due to the limited sensor e.ciencies).
Relationship: entailment
Domain: Networks
Sentence 1: The insight behind thisisthat,with multipleco-located staticsensors, n their spatialRSSdiversities arelimitedtotheirgeographical area, whereas a single mobile sensor canfully exploit spatio­  (1-.n)M +2.n NM Ms (11) temporalRSSdiversityasitkeeps moving aroundtheentire =+ , sn 2 2NoPR1+.n cell, the area of which is much larger than the correlation n o where sn 2 is the variance of the test statistic,i.e., sn 2 = N2 .
Sentence 2: ms we would like to express PMD in terms of sensor speed, i.e., vn.
Relationship: entailment
Domain: Networks
Sentence 1: We demonstrate that our multiclass DCF analysis is a versa­tile analytical framework when combined with the IP-layer queueing analysis, where the QoS performance, statistical multiplexing gain, admission region, contention window design, fairness in resource sharing can all be investigated.
Sentence 2: we show through numerical analysis that the proper selection of contention windows plays an important role in improving the MAC capacity for a larger admission region, while the optimal contention window for each class and the maximum admission region can be jointly solved in our analytical model.
Relationship: entailment
Domain: Networks
Sentence 1: We are exploiting our past experiences on the field to achieve this goal.
Sentence 2: two prototypes have been realized over the past two years: one for BT, which implements the LSSH scheme (see section 4.1.1) along with NCSOCKS1 (see 4.2.2), and the other for Wi-Fi, implementing the handoff detection (see 4.1.2) and the Multimedia Streaming facility2 (see 4.2.3).
Relationship: entailment
Domain: Networks
Sentence 1: This leads to the notion of robust .-sM The symbolic RCF-execution SExec: This semantics is de.ned by taking the de.nition of the computational RCF-execution, and by replacing all computational operations by the corresponding symbolic operations.
Sentence 2: the symbolic and the computational RCF­execution are essentially the same algorithm, one oper­ating on terms, the other doing the corresponding op­erationsfrom the computationalimplementation.
Relationship: entailment
Domain: Networks
Sentence 1: In our system, we capi­talize on an infrastructure with a ubiquitous, wide-area net­work to help enable the creation of a local mobile ad-hoc network in an e.cient, scalable, evolvable, and manageable way.
Sentence 2: in an architecture inspired by software­de.ned networking, we decouple the control plane and data plane in the mobile devices and shift the control plane to a centralized controller.
Relationship: entailment
Domain: Networks
Sentence 1: Lemma 1 can be easily proved based on the de.nition of opti­mal fair rate allocation, as in De.nition 3.
Sentence 2: lemma 1 reveals an important property of optimal fair rate allocation; this property will be used later to establish the equivalence between op­timal fair rate allocation and optimal QoM-aware rate allocation under some settings.
Relationship: entailment
Domain: Networks
Sentence 1: Together with the computational soundness of encryptions and digital signatures in CoSP, this implies the computa­tional soundness of the applied p-calculus with encryptions and signatures.
Sentence 2: the result shows that CoSP is capable of embedding a formal calculus that is well un­derstood and accepted by the scienti.c community, that is expressive enough for expressing and reasoning about state­of-the-art protocols, and that is accessible to state-of-the-art veri.cation tools.
Relationship: entailment
Domain: Networks
Sentence 1: The choices of the nodes in V  R are inde­pendent of the choices made by nodes in R. Hence, we can use the principle of deferred decisions as if all choices made by nodes in V  R are made after R has been determined.
Sentence 2: first, the choice w of a gossip partner that u contacts at round r has constant probability of being outside R. Sec­ond, applying Lemma 4.2, we obtain that round r su.ces in expectation for w to obtain m from v in a path P that does not intersect R. Pw(r) contains a path P orig­inating from v that does not intersect R. Accordingly, with constant probability u obtains in round r a new path for m that does not intersect P u(r).
Relationship: entailment
Domain: Networks
Sentence 1: The transmission range of a base station is also assumed to be r. In other words, a base station can only directly serve nodes within distance r. The total data rate that a base station can serve all ordinary wireless nodes is at most Wc ­bps with Wc = Wa.
Sentence 2: a base station can serve at most W. c .ows if each .ow requires a data rate .. Each base station is connected to its adjacent base stations (at most 4) by wired lines or wireless channels (using frequency dif­ferent from the frequency used between ordinary wireless nodes).
Relationship: entailment
Domain: Networks
Sentence 1: Combing (12) and (13), we see that .N (gI ,s)-S(gI ,s)=0when gI =a2.
Sentence 2: the .rst two linear segments on Fcoincide with S. When gI <a2, Property 4 implies that the deviation .N (gI ,s)-S(gI ,s)is positive.
Relationship: entailment
Domain: Networks
Sentence 1: An overload is said to occur when both classes use up their entire allocations (resulting in saturation) or the queue of pending requests exceeds a threshold.
Sentence 2: (i) Median,(&#38;) 1 Rbe and Perc,(U,t) > R,t; or (ii) qbe > Q or qrt 1 Q, where Q is a large threshold.
Relationship: entailment
Domain: Networks
Sentence 1: We can assume that the larger densities give a stronger advantage to our algorithm, since statistic disorders of the network are rare and so the algorithm can behave more as desired, minimizing complica­tions in the routing.
Sentence 2: we remark that in the U­shape obstacle, our algorithm starts outperforming TRUST when the density d becomes larger than 2, becomes quite close to optimality when d gets 4, while for larger densities it is near-optimal.
Relationship: entailment
Domain: Networks
Sentence 1: 2.2.1 Basic Mobility Observations While we are able to detect a user s access to the mo­bile data network and accurately estimate a given location (above) and characterize accessed applications (below), an important underlyingquestionisif we are able to accurately estimate user mobility patterns.
Sentence 2: there is on average a gap of 6 hours and 11 minutes between two con­secutive sessions from the same user.
Relationship: entailment
Domain: Networks
Sentence 1: In order to quantify the inter-state and intra-state dis­tances, we use non-linear dimensionality reduction1 to re­duce the 100 dimension vectors to just 1-2 dimensions.
Sentence 2: we apply the well-known Isomap algorithm of Tenenbaum et al [12] to the complex link signature mea­surements.
Relationship: entailment