sadat2307/MSciNLI huggingface dataset:
Domain: Networks
Sentence 1: Although this may lead to suboptimal routes when the initiator and the corre­spondent are not neighbors but are close to each other, our model has the advantage of signi.cantly reducing the com­plexity of routing from the nodes point of view, since they only have to maintain a single route (the route to the base station) instead of one route per potential correspondent.
Sentence 2: if the correspondent B is not a neighbor, then the initiator A simply sends the packet towards the base station and does not need to run route discovery to .nd a route to B.
Relationship: entailment
Domain: Networks
Sentence 1: We categorize the frame losses into three types, based on the number of retries and the loss rate in the last retry.
Sentence 2: these results (and similar ones at other locations) reveal a distinct pattern of collision losses: nBad retries = 1 AND < 10% (1) nF rames the last aggregate frame experienced at least one retry, yet in the last retry, it was received with very mild subframe loss.
Relationship: entailment
Domain: Networks
Sentence 1: Random Way-point Mobility Model: Using sim­ulation traces we have found that if a node i follows the Random Way-point model in a bounded region Si, such as, a square, then the probability of node s arrival in a region in the next t seconds, beginning at a random instance is exponentially distributed.
Sentence 2: hi(a(r, s), w) = 1 - exp(-w//s,r) (3) The mean inter-arrival time, /s,r, depends on the size (r) and the location (s) of the region a.
Relationship: entailment
Domain: Networks
Sentence 1: However,now that Mt is quite large,many of the randomly forwarded edges are likely to fall in Mt: i.e.,progress slows down.
Sentence 2: suppose we have almost reached our goal of covering U(1-) ·(1-d) nodes; at this point,we have U(1-)-Mt U(1-)d.Since the Ut closely track their means,we only get something like Ut+1/U =(1 -)d ·(1 -exp(-rß ·(Ut/U)).
Relationship: entailment
Domain: Networks
Sentence 1: Though different from each other in fast retransmission algorithm, congestion control of these TCP versions is commonly based on additive increase multiplicative decrease (AIMD) [7].
Sentence 2: tCP increases the congestion window by roughly one segment per Round-Trip Time (RTT) when there is no packet loss detected and TCP reduces its window size by half for every window containing a packet loss.
Relationship: entailment
Domain: Networks
Sentence 1: For example, if the queue utilization at H1 is 75%, at H2 is 25% and at H3 is 50%, then the probability that all three guest-users move to H2 is 0.375, according to the third approach.
Sentence 2: chances are that G1 will move to H2 , while G3 will remain attached to H3 .
Relationship: entailment
Domain: Networks
Sentence 1: Let qu(n, t) be the popularity of the n th object observed by a cache node u around a particular time t. As a gener-Here, {f1} denotes the set of f1 caches; Nu(n, t + .t) denotes the number of references to object n coming at node u during interval (t, t + .t); and Cu represents the set of caches which have u as their upstream node.
Sentence 2: the aggregate miss streams of nodes in Cu form the input stream of u.
Relationship: entailment
Domain: Networks
Sentence 1: They argue that a coordinate-wise constant-factor approximation can­not be found for this problem,and presented a pre.x-sum 2-approximation algorithm to the allocation of fairest frac­tional solution.
Sentence 2: for every integer k> 0,the sum of the .rst k coordinates of the calculated allocation vector sorted in increasing order is at most twice the sum of the .rst k coordinates of the allocation vector of the fairest fractional assignment.
Relationship: entailment
Domain: Networks
Sentence 1: These include, among others, that tier-1 ISPs do not peer at IXPs (they do), IXPs are not used for transit (they are), the number of peer-peer links in the Internet is small (it is at least an order of magnitude larger than what has been assumed), the number of customer-provider links in the Internet is much larger than the number of peer-peer links (there are easily twice as many peer-peer links than customer-provider links), and IXP peerings are mostly used for back-up (they are not).
Sentence 2: we examine in detail the peering fabric and traf.c matrix of our IXP and show the existence of a very diverse ecosystem in terms of the member ASes business types, peering strategies, traf.c exchanges, and geographic coverage that mimics the Internet s AS ecosystem as a whole.
Relationship: entailment
Domain: Networks
Sentence 1: [8] focuses on the frequencies at which di.erent numbers of packets per .ow occur.
Sentence 2: the paper: (i) shows how to smooth the estimated .ow size distribution in order to deal with short .ows that disappear; (ii) uses maximum likelihood estimation to derive the full distribution of packet and byte .ow lengths; (iii) exploits protocol level details to render the estimators more accurate.
Relationship: entailment
Domain: Networks
Sentence 1: The ideal, espoused by CIDR, is that each allocation to an ISP has exactly one entry in the routing table, and no assignments ap­pear in the routing table.
Sentence 2: each customer advertises its as­signment as a route in BGP (or perhaps using static routing or shar­ing an IGP with the ISP, although these practices are probably less prevalent now), but the ISP aggregates these routes, and advertises to its peers or its upstream provider the address pre.x representing its allocated space.
Relationship: entailment
Domain: Networks
Sentence 1: Formally, we denote the network status at time t as G(t)=(V, E(t)), where V is a complete set of N nodes within the network, and E(t) is a set of directed edges at time t. An edge e(i, j) belongs to E(t) if and only if (1) node ni is a neighboring node of nj, and (2) nj is active and hence able to receive data at time t. Essentially, G(t) represents the potential traf.c .ow within the network at time t. Obviously the connectivity of G(t) varies with time.
Sentence 2: g(t) is a time-dependent network.
Relationship: entailment
Domain: Networks
Sentence 1: 4.2 SMS Solution Tradeoffs SMS is a low-cost, low-bandwidth method of transmitting data that is supported by most mobile phones.
Sentence 2: we found a SMS-based solution most appropriate because many users already own mobile phones, and the cost of an individual SMS is very low (somewhere between US$.03 and US$.05 in Tanzania at the time of this work).
Relationship: entailment
Domain: Networks
Sentence 1: The amount of bytes allocated over each route is: (4) byteroute=indexroute*(Byte_MAX-Byte_min)+byte_min where Byte_MAX and Byte_min are parameters that define the maximum and minimum number of data bytes that could be allocated on each concurrent path.
Sentence 2: byte_min can be used to control the insorgence of concurrent route-path overheads when few data are to be sent, while Byte_Max can be used as upper bound factor for the buffering on the receiver side.
Relationship: entailment
Domain: Networks
Sentence 1: The user should be able to rotate its request pattern and even add noise to the sequence of requests that result in retrieving a .le.
Sentence 2: instead of requesting all the necessary chunks for an speci.c .le in a sequence of t1,t2,...,ti periods, the user should be free to distribute and rotate its requests over a much larger time span such as t1,t2,...,ti,...,ti+p.
Relationship: entailment
Domain: Networks
Sentence 1: Rather than initializing Alice s locations uniformly across the entire .oor (as in Walk I in Figure 1), Zee uses these RSS measurements and the database to obtain a con.ned proba­bility distribution, as depicted by the gray region corresponding to Walk II in Figure 1.
Sentence 2: the WiFi database obtained from Bob s walk helps Zee to narrow down the possibilities for Al­ice s initial location.
Relationship: entailment
Domain: Networks
Sentence 1: The algorithm has a basic assumption that there is a longer prefix if there is a match in the current level of the access and there is not a longer length prefix if there is no match in the current level of the access.
Sentence 2: starting from accessing the medium level (level 4 in Figure 3) by hashing, the search continues to the level of a longer length (level 5) for a match, and otherwise, the search continues to the level of a shorter length (level 3) for no match, and so on.
Relationship: entailment
Domain: Networks
Sentence 1: While SyncScan potentially reduces the latency of a handoff to a few milliseconds, it requires regular suspension of communication for roughly twice the amount of time it takes a wireless interface to change chan­nel, which, depending on hardware, could exceed 10 ms. An important advantage of MultiScan is that it requires changes only at the wireless client.
Sentence 2: we have implemented all the necessary functionality in a (Linux) client as a kernel module that controls the handoff process and (re)association decisions.
Relationship: entailment
Domain: Networks
Sentence 1: We next analyze the fairness performance of the discrete sched­ule by comparing the dominant services a .ow receives under both schedules.
Sentence 2: let Di F(0, t) be the dominant services .ow i receives in (0, t) under the .uid schedule, and Di D(0, t) the dom­inant services .ow i receives in (0, t) under the corresponding dis­crete schedule.
Relationship: entailment
Domain: Networks
Sentence 1: In order to make Dij (t)and Eij (t)con­tinuous as in [8], in the following analysis, we assume that a bit is immediately released from the buffer after it has been transmitted.
Sentence 2: suppose that sn (s0 =0)and rn (r0 =0)are the time that the n th packet is removed from Qij and Bij respectively.
Relationship: entailment
Domain: Networks
Sentence 1: Our work on real-time crowsourcing-based estimation of RF properties is inspired by the work [7] in building the EZ localization system.
Sentence 2: the EZ algorithm as­sumes a log-distance path loss model (LDPM) between in­door APs and mobile devices, and uses multiple simultane­ous RSSI measurements from the various mobile devices to estimate both the parameters of the log-distance model and the locations of the APs.
Relationship: entailment
Domain: Networks
Sentence 1: 7.3 Managing bandwidth with exceptionhandlers We now evaluate a concrete example of network exception han­dlers using our enterprise network traf.c dataset described in Sec­tion7.1.
Sentence 2: we describeahypothetical scenario where anexception handleris employedfor bandwidth savings through  Figure 9: Bandwidth savings by applying the network exception handler in Fig.
Relationship: entailment
Domain: Networks
Sentence 1: This is due to the fact, that (in the depicted case) on encountering the next edge crossing with st at a point p, the next adjacent face which intersects with the open line segment pt can always be traversed by using the left hand rule and selecting from the crossing edge the next one in counterclockwise direction.
Sentence 2: the criteria applied in GFG and GPSR are equivalent for this example.
Relationship: entailment
Domain: Networks
Sentence 1: Next, we evaluate VERITAS s e.ciency by comparing its alloca­tion algorithms to two best-known greedy algorithms that maximize the spectrum utilization and the social welfare (sum of winning bids).
Sentence 2: we compare: Greedy -1/(X + 1) [14]: The best-known greedy allo­cation algorithm that maximizes the spectrum utiliza­tion.
Relationship: entailment
Domain: Networks
Sentence 1: We modi.ed the IP-to-AS mapping based on these results to treat sibling ASes as a single network.
Sentence 2: we replaced every occur­rence of Bor Din the IP-to-AS mapping with the set {B,D}.We Internet Exchange Points Sibling ASes Unannounced Addresses AS 6431 AS 25 AS 3130 AS 6431 AS 25 AS 3130 AS 6431 AS 25 AS 3130 Match 78.2% 84.4% 85.4% 86.0% 85.9% 87.0% 90.0% 90.6% 91.0% Mismatch 6.4% 8.7% 7.1% 6.4% 7.8% 6.2% 2.7% 3.5% 2.6% Incomplete 15.4% 6.9% 7.5% 7.6% 6.3% 6.8% 7.4% 6.0% 6.6% Match/Mismatch ratio 12.20 9.70 12.06 13.42 11.00 14.08 33.51 25.95 35.41 Table 10: The results of using the three techniques to tune the IP-to-AS mapping Figure 5: Mismatches caused by unannounced IP addresses considered the traceroute and BGP AS hops a match if the BGP AS hop was the same as either of the two siblings in the traceroute AS path.
Relationship: entailment
Domain: Networks
Sentence 1: On the other hand, CR proved itself ef.cient in allowing seamless L2 and L3 handoffs for real­time traf.c, in performing load balancing and in many other tasks.
Sentence 2: the total L2+L3 handoff time is about 15.6 ms in open networks and about 21.4 ms in networks using IEEE 802.11i.
Relationship: entailment
Domain: Networks
Sentence 1: Our ultimate goal with this framework is to meet the user-centric service expectations for intra-cell resource fairness and minRate guarantee, and to maxi­mize the service provider s revenue by accommodating more sat­is.ed users into the system.
Sentence 2: we propose an opportunistic downlink scheduling algorithm, named the Weighted Alpha-Rule, to provide high aggre­gate throughput and tuneable resource fairness among users within each cell.
Relationship: entailment
Domain: Networks
Sentence 1: Finally, while we present our algorithms as using the entire graph information (i.e., as a centralized algorithm), four of our algorithms work in a way that only uses local information at each step, and can be implemented as a distributed algorithm along the lines of [14, 27].
Sentence 2: the same techniques can be applied per (smashed) snapshot to make a distributed version of the proposed algorithms.
Relationship: entailment
Domain: Networks
Sentence 1: ( N , N ) E 12 ® ki kj ° 0 otherwise ¯ Definition 2 The Maximal Link Interference Region (MLIR) of WMN s satisfies the following conditions: 1. s .
Sentence 2: l 2. l , l s, I (l , l )1 12 12 3. l s, l s and I (l , l )0 ij ij in one MLIR, only one link can transmit data at any time.
Relationship: entailment
Domain: Networks
Sentence 1: Since in a Poisson distribution the degrees will be fairly tightly grouped around the mean, then using a clique generalization that limits how small the minimal degree can be, such as the k-plex, approximately captures the set of nodes in this distribution.
Sentence 2: if we chose a large enough k, then we should be able to match the maximal k-plexes against a majority of the af.liations.
Relationship: entailment
Domain: Networks
Sentence 1: The DMMT-OA algorithm is a constant-factor approximation algorithm.
Sentence 2: for a multicast tree Ts obtained from DMMT-OA, its approximation ratio satisfies 2p µ= ·K (,2 ) , where (39) .= .p 33 vu 3 .min .3 =.cv wxy (( x ( s .
Relationship: entailment
Domain: Networks
Sentence 1: In our experiments, we discovered that the effects of hardware variation and some time-varying phenomena appear to be linear as well.
Sentence 2: the calibration function can be Chipset Relation ACX100 c(i)=i Prism c(i)=0.85 · i - 43.5 Atheros c(i)=2.77 · i - 409.5  256 224 192 Table 2: Linear relationships between several different 802.11 Signal intensity (new card) cards and the ACX100 card we used in training.
Relationship: entailment
Domain: Networks
Sentence 1: Note that for each TCP flow i, the Internet gateway has to provide at maximum a total of awndi free packet buffer space in the pacing queue, where awndi denotes the size of the receiver advertised window of flow i.
Sentence 2: the wired TCP source of flow i can never transmit more than awndi packets back to back before waiting for a corresponding TCP ACK to arrive.
Relationship: entailment
Domain: Networks
Sentence 1: (1 - )|C| n The intuition behind the de.nition of expander graph is that for any chosen subset of the expander, the expansion and edge boundary of the cluster represents a better connectiv­ity.
Sentence 2: for any chosen cluster from the ex­pander, number of adjacent nodes are always larger than the size of the cluster times some constant .. Good ex­pansion property implies low degree and high connectivity (i.e.
Relationship: entailment
Domain: Networks
Sentence 1: It is important to note that, depending on the applica­tion (e.g., unicast versus multicast), the gateway server may utilize both the new generated (down-transcaled) stream and the original scalable stream for its different clients.
Sentence 2: since the quality of the original scal­able stream Sin is higher than the quality of the down­transcaled stream Sout over the range [Rmin_in , Rmax_in], then it should be clear that clients with access bandwidth that falls within this range can benefit from the higher quality (original) scalable stream Sin.
Relationship: entailment
Domain: Networks
Sentence 1: For i = 0to k, it chooses a set Si of su.ciently many (as de.ned later) re­quests from Ci taking into account interference caused by the previously selected sets S0,...,Si-1.
Sentence 2: si satis­.es the SINR constraints with gain ß/2 on top of S0,...,Si-1, i.e., the interference constraints for every pair in Si are satis­.ed with gain ß/2 taking into account the interference caused by the previously inserted pairs in S0,...,Si-1 and the other pairs in Si.
Relationship: entailment
Domain: Networks
Sentence 1: There is considerable topological di.erences between 802.11a and 802.11b/g channels.
Sentence 2: links under 802.11b/g channels have much longer range than those on 802.11a channels due to the fact that 802.11b/g is at a lower frequency band and that 802.11b standard permits lower and more robust transmission rates (e.g.
Relationship: entailment
Domain: Networks
Sentence 1: In particular, in Sections 5.2 and 5.3 we compare diversity to the single-channel case by averaging the ER ob­tained from using only channel 1 or only channel 2.
Sentence 2: if the throughputs over channels 1 and 2 are ERch1 and ERch2 , respectively, then we de.ne ERone channel as ERch1 + ERch2 ERone channel = .
Relationship: entailment
Domain: Networks
Sentence 1: Choosing the right traf­.c aggregation is tricky: too much aggregation will lead to total detections total detections 70 60 50 40 30 20 10 # principal components (c) Abilene false-positive rate abilene (threshold = 90%) smooth and predictable .ow curves whereas too little aggre­ gation yields a very heavy-tailed .ow-size distribution (see .gure 2) and hence some highly variable small .ows whose spikes are not of interest to network operators.
Sentence 2: for both Abilene in .gure 4(d) and Geant in .gure 4(b), it is clear that the ingress router aggregation consistently detects fewer anomalies than OD .ows and input links.
Relationship: entailment
Domain: Networks
Sentence 1: On the other hand, because the reference point has an IP address outside of the target pre.x, any pre.x hijacking attacks targeting the pre.x will not affect the reference point.
Sentence 2: we will detect disagreement be­tween the path from a monitor to a target pre.x and the path from the same monitor to the corresponding reference point of the target pre.x.
Relationship: entailment
Domain: Networks
Sentence 1: C " ,s " A generation or analysis rule r has the form r, with C,s C and C " constraint sets and s and s " substitutions, and it expresses that (C " ,s " )can be derived from (C,s), which we denote by (C,s) fr (C " ,s " ).
Sentence 2: the constraint reduc­tion rules are applied backwards.
Relationship: entailment
Domain: Networks
Sentence 1: Thus, CB performs at least as well as the Count-Min algorithm.2 (c) Our reconstruc­tion algorithm detects errors.
Sentence 2: it can distinguish the .owswhosesizesareincorrectly estimated,andproduce an upper and lower bound of the true value; whereas the Count-Min sketch onlyguarantees an over-estimate.
Relationship: entailment
Domain: Networks
Sentence 1: E) if and only if w belongs to the disk centered in v and of radius r. The transmission range of a node depends, in turn, on the energy power supplied to the node.
Sentence 2: the power Pv required by a node v to correctly transmit data to another station w must satisfy the inequality Pv = dist(v, w)2,where dist(v, w)is the Eu­clidean distance between v and w. In several works [1, 12, 15, 21], it is assumed that nodes can arbitrarily vary their transmission range over the large set {dist(s, t)|s, t .
Relationship: entailment
Domain: Networks
Sentence 1: Depending on the mechanism used to implement packet leashes, this requirement bene.t other parts of the protocol: TIK [14], for example, authenticates each packet in a lightweight manner, thus protecting the more expensive signature veri.cation from a denial-of-service attack.
Sentence 2: if a node A receives an au­thenticated packet containing a bogus signature from node B,then A can lower the priority with which it checks signatures sent by B.
Relationship: entailment
Domain: Networks
Sentence 1: The main idea of PA is to investigate how an in.nitesimal perturbation on some com­ponents of the LMM-Rate problem can affect the objective func­tion.
Sentence 2: considering a small increase on the right-hand­side (RHS) of (10), i.e., changing bito bi+Ei, where Ei>0, node :Æ1 ibelongs to the minimum node set S1if and only if (0)<0.
Relationship: entailment
Domain: Networks
Sentence 1: Yet, with a reordering buffer of 3 or more packets, the throughput decreases to approximately 1% of the peak value indicating a successful attack and near starvation of the .ow.
Sentence 2: if the scheduler selects the next packet to service randomly among the .rst 3 or more queued, the resulting reordering cannot be overcome by TCP.
Relationship: entailment
Domain: Networks
Sentence 1: To examine the difference, we group applications based on their types based on the domain knowledge.
Sentence 2: we study three groups: video streaming (YouTube, GoogleVideo, RTVE, Vimeo, Youku), social web (Tuenti, Facebook, Twitter, Linked-In, MySpace, LiveJournal) and news (CNN, BBC, washingtonpost, wikipedia).
Relationship: entailment
Domain: Networks
Sentence 1: In addition, P{Yk < y} = y since the random variable Yk is uniformally distributed over the range [0, 1).
Sentence 2: thus the cu-mulative distribution function (CDF) of the derived random variable Xk is: Fa(x) = P{Xk < x} = P{ b~k < X} = P{Yk < z bwk } xbwk which gives the probability density function (PDF) of ran- dom variable Xk as: fl~(x) = F~(x) = bwk " ~, bujk-1 when the priority of entity i is x, the probability that entity i wins the contention is derived from Eq.
Relationship: entailment
Domain: Networks
Sentence 1: Lastly, it can be observed that the communication architectures underlying many use cases cannot guarantee end-to-end applicative connectivity, since the dynamic flight paths of the UAVs may cause frequent fragmentations in the aerial network.
Sentence 2: continuous changes in the relative motions of each UAV creates challenging conditions to maintain connected communication links, either between UAVs or with other network nodes.
Relationship: entailment
Domain: Networks
Sentence 1: Via analysis, simulation and .eld experiments, we have demonstrated that our SISR localization method can automatically tolerate errors in rang­ing measurements by de-emphasizing bad data while accentuating more accurate readings.
Sentence 2: sISR can accurately local­ize dif.cult non-convex topologies, even in the presence of non-Gaussian errors in ranging.
Relationship: entailment
Domain: Networks
Sentence 1: In this paper we propose to use conformal mapping to compute a new em­bedding of the sensor nodes in the plane such that greedy for­warding with the virtual coordinates guarantees delivery.
Sentence 2: we extract a planar triangulation of the sensor net­work with non-triangular faces as holes, by either using the nodes location or using a landmark-based scheme without node location.
Relationship: entailment
Domain: Networks
Sentence 1: For instance, in(?x).P and in({| ?y |} kAB ).Q can both read message { G} kAB but the .rst process just reads it, denoted in({ G} kAB ), while the second one reads and decrypts it, de­noted in({| G |} kAB ).
Sentence 2: x is bound to { G} kAB while y is bound to G. This makes it possible to express protocols where part of a message is unknown to the re­cipient, as also done, e.g., in symbolic model checking and constraint solving [5, 11, 28].
Relationship: entailment