sadat2307/MSciNLI huggingface dataset:
Domain: Networks
Sentence 1: 2, we can observe that, Xis larger than Yn, where Yn is de.ned as n the duration between the start of the RTS frame and the end of the DATA frame.
Sentence 2: the difference between X n and Yn, denoted as T o, can be calculated by 1 T o = Xn-Yn = TSI F S +TDI F S +Tsync + (LPH +LAC K ) R (21) 4Here we ignore the propagation delay.
Relationship: entailment
Domain: Networks
Sentence 1: While this approach is likely to be ade­quate for DTNs employing only loop-free routes, it is not su.ciently .exible for supporting short-term loops in rout­ing paths that do not disturb an otherwise-loop-free routing protocol.
Sentence 2: although an operational loop-free DTN routing solution could be locally adjusted in the vicin­ity of a congested custodian to e.ect message migration, this approach would require extreme attention to detail in how routing would be locally adjusted in order to prevent global routing algorithm instability.
Relationship: entailment
Domain: Networks
Sentence 1: We employed RS(n, k) non-binary linear codes for infor­mation dispersion over multiple paths which leads to robust­ness against at most n - k packet drops or t = L n-k J modi­ 2 .cations.
Sentence 2: k symbols are coded to n symbols and at least k unmodi.ed symbols are required for successful reconstruction of the original data.
Relationship: entailment
Domain: Networks
Sentence 1: According to this mechanism, the decision on the threshold between PTP (multiple DCHs) and PTM (FACH) bearers is operator dependent, although it is proposed that it should be based on the number of served MBMS users.
Sentence 2: a switch from PTP to PTM resources should occur, when the number of users in a cell exceeds a predefined threshold.
Relationship: entailment
Domain: Networks
Sentence 1: 5.1.2 Short Flows We will use the commonly used metric for short .ows: the .owcompletion time, de.ned as the time from when the .rst packet is sent until the last packet reaches the des­tination.
Sentence 2: we will measure the average .ow  Number of long-lived flows Figure 12: Bu.er requirements for tra.c mix with di.erent .ow lengths, measured from a ns2 simula­tion.
Relationship: entailment
Domain: Networks
Sentence 1: Second, CRMA without virtual .ows signi.cantly out-performs CSMA/CA, Wi-Fi, and Random Access.
Sentence 2: for a small number of .ows(2-4 .ows), CRMA out-performs CSMA/CA by 33%-50%,Wi-Fiby86%-93%, andRandomAccessby64%-68%.
Relationship: entailment
Domain: Networks
Sentence 1: for network operators today is to e.ciently provide hetero­geneous and .exible connectivity services to o.er guaran­teed quality levels to their customers at competitive prices.
Sentence 2: hybrid packet and circuit transport networks, based on optical transmission and switching technologies, represent a promising and e.ective solution to this issue, since they merge the advantages of packet-based service (e.g., high .exibility, better resource utilization) with those of cir­cuit switching solutions (e.g., tra.c isolation, guaranteed bandwidth, limited latency).
Relationship: entailment
Domain: Networks
Sentence 1: While the focus of these studies was different from ours-in particular, [YMKT99] explicitly discarded non­ steady samples-some of our results bear directly upon this previous work.
Sentence 2: in this section we ver­ify the finding of correlations in the loss process, but also find that much of the correlation comes only from back­ to-back loss episodes, and not from nearby losses.
Relationship: entailment
Domain: Networks
Sentence 1: 4.5 Sparse aggregation with HST We also remark that the 2-HST computed in Section 4 can be used to get an aggregation tree when spontaneously emerging resources spread out in the network are detected by their local sensors and no resource is aware of how many and where the other resources are.
Sentence 2: it solves the same problem as in the sparse aggregation framework [11].
Relationship: entailment
Domain: Networks
Sentence 1: However, it turns out that, at least for shortest hop routing, one can just as easily generate a keep/relay/drop table for a source-destination pair, given the topology.
Sentence 2: a destination-rooted sink tree may be used to determine whether or 6 For a given network size, density and mobility, the fraction of capacity used for (overhead) control messages decreases as data rates increase.
Relationship: entailment
Domain: Networks
Sentence 1: Dolev-Yao Model of Cryptography  We employ a standard Dolev-Yao black-box model to capture the functionality of cryptographic primitives and operations used in communi­cation protocols.
Sentence 2: we use the deductive system from [7], consisting of construction, elimination, and test­ing rules, to reason about messages in the three-layer model.
Relationship: entailment
Domain: Networks
Sentence 1: that n is su.ciently large): An (e, d)-triangulation of certain simple 2k-doubling n­point metrics must have order of the form d-O(k) log 1 e .
Sentence 2: for (0, d)-triangulation we obtain a lower bound of d-O(k) log n, which matches the upper bound d-O(k) log n shown by Slivkins [18].
Relationship: entailment
Domain: Networks
Sentence 1: A comprehensive simulation study using ns­2 [8] shows that TCP-AP achieves up to 84% more goodput than TCP NewReno, provides excellent fairness in almost all scenarios, and is highly responsive to changing network traffic conditions.
Sentence 2: tCP-AP provides fair sharing of the available bandwidth, even when competing flows are not within each other s transmission range, but within each other s interference range, since the proposed algorithm relies on the end-to-end measurement of the interference experienced by a TCP connection.
Relationship: entailment
Domain: Networks
Sentence 1: Furthermore, there are only 65,536 (216) possi­bilities for the lower 16 bits of Xi , and only one of them is such that when used with Xi,[0···15] (available from the packet) the next two numbers generated by Eqn 1 have the same top 16 bits as Xi+1,[0···15] and Xi+2,[0···15], which are also observed in the received packet.
Sentence 2: there is only one 16-bit number Y that satis.es the following two equations simultaneously: Xi+1,[0···15] =(Xi,[0···15] ||Y * a mod m)[0···15] Xi+2,[0···15] =((Xi,[0···15]||Y *a mod m)*a mod m)[0···15] For each of the 216 possible values of Y , verifying the .rst equality takes one addition and one multiplication.
Relationship: entailment
Domain: Networks
Sentence 1: In addition, to shed further light into the composition of one-way tra.c and to facil­itate correlating data from multiple IBR tra.c monitors, we release non-sensitive details about the evolution of one­way and two-way tra.c between 2004 and 2011 based on the studied datasets.
Sentence 2: for each 10-minute time interval, we make available statistics about the synthesis of one-way tra.c, the popularity of di.erent signs, the targeted services, and the geographical distribution of sources.
Relationship: entailment
Domain: Networks
Sentence 1: For link-by-link ARQ, the relay stores the XOR packets it successfully decodes, and if the downlink phase fails, the re­lay is responsible for retransmitting the XOR packet, not the end nodes.
Sentence 2: the uplink and downlink ARQs are decoupled so that the end nodes are responsible for re­transmission of uplink packets, and the relay is responsible for retransmissions of downlink packets.
Relationship: entailment
Domain: Networks
Sentence 1: This is in contrast to the usual stabilizing pressure that comes from packet losses, which implies that the queue occupancy is high.
Sentence 2: this enhancement to the equation-based approach may make WEBRC sessions less of a hindrance 5Note that the problem of large IGMP leave latencies that motivates the quiescent periods on the wave channels also prevents multicast leave operations from being a robust way to reduce reception rate.
Relationship: entailment
Domain: Networks
Sentence 1: Speci.cally, the Used .elds play the role of the true source address (the one that can be used to return packets), and the source IPNL address plays the role of an identi.er only.
Sentence 2: the source IPNL address tells the host who the packet came from, and the Used .elds provide a new overloaded address that should be used subsequently.
Relationship: entailment
Domain: Networks
Sentence 1: Both papers [6, 5] present approximation al­gorithms for the minimum congestion con.uent .ow problem and the maximum throughput con.uent .ow problem.
Sentence 2: [5] shows a tight T(logn)-approximation for the minimum node­congestion con.uent .ow problem and a constant factor approx­imation for the maximum throughput con.uent .ow problem in general graphs, for the special case in which the capacity of all nodes is the same.
Relationship: entailment
Domain: Networks
Sentence 1: Constant bit rate cross tra.c (CBR1): CBR1 cross tra.c consists of .xed-sized and uniformly distributed pack­ets.
Sentence 2: cBR1 tra.c is a stationary tra.c with constant average data rate over long time scale (millisec­onds), and interpacket gaps are uniform (i.e., variance is zero).
Relationship: entailment
Domain: Networks
Sentence 1: lnn-1(1 - x) with alternating signs.
Sentence 2: returning to Equation (7), we can now write: 8 f p(x|0) = pXi (x) i=1 =1 - ln(1 - x)+ 1 ln2(1 - x) 2 - 1 ln3(1 - x)+ ... 6 To evaluate this sum, consider it a function of x.f (x)= p(x|0).
Relationship: entailment
Domain: Networks
Sentence 1: A F3 ~- - F5 B F Figure 1: Fundamental conflicts between fairness and maximal utilization A FI B Fi ~ F2 E D WF5 vF4 Original Topology Flow Graph Figure 2: Generating a flow graph The above example illustrates the fundamental conflict between achieving flow fairness and maximizing overall sys- tem throughput.
Sentence 2: some flows may need to be starved to maximize channel utilization and conversely, en- forcing any notion of "fairness" across flows may result in sub-optimal channel utilization.
Relationship: entailment
Domain: Networks
Sentence 1: 6.1.1 Uplink CSI Estimation Due to the absence of uplink/downlink channel reciprocity in cellular networks [19], we can only adopt closed-loop CSI esti-mation (see Section 2.2) in BeamAdapt.
Sentence 2: the client conca-tenates a short field made up of several training symbols to the data field in each uplink frame.
Relationship: entailment
Domain: Networks
Sentence 1: The (1 - e)-diameter of a temporal network is de.ned in [2]; it is the number of hops that is needed to achieve a fraction at least (1 - e) of the success ratio obtained with .ooding, for a given maximum delay.
Sentence 2: this is the maximum number of hops k, necessary to construct all paths needed to be almost competitive with .ooding, with a .xed maximum delay.
Relationship: entailment
Domain: Networks
Sentence 1: Based on the DAG, a tree structure can be constructed by assign­ing each node an incoming link with the best link quality among all incoming links, as shown in Fig.3(b).
Sentence 2: as we show later, it can be easily proved that this tree structure is the energy-optimal one for .ooding among all treestructuresgeneratedfromtheDAG.if a .ooding packet is forwarded based on this tree, (i.e., a node only receives a .ooding packet from its parent), the expected total number of transmissionsis minimized.
Relationship: entailment
Domain: Networks
Sentence 1: When a 1-hop neighbor of node v receives this packet and sees its ID in this packet, it knows that it is a forwarding node and continue the same broadcasting procedure as node v. Another feature of DCB is that a node tries to transmit a packet to all its selected forwarding nodes reliably.
Sentence 2: a node v waits for a certain period to overhear the rebroadcasting from all its selected forwarding nodes.
Relationship: entailment
Domain: Networks
Sentence 1: Previous work [12] has characterized that a frequencyestimation error of 10%leads to = 2dB reduction in SNR, which is almost negligible for the SNR regime we consider.
Sentence 2: 7.2 Prototype system evaluation Our second set of experiments evaluates the Enfold implementa­tion on the Sora platform at 6 Mbps data rates.5 we 59Mbps shares the same BPSK modulation scheme per-subcarrier as 6 Mbps while the coding rate changes to 3/4.
Relationship: entailment
Domain: Networks
Sentence 1: We formulate a stochastic model for the structure and inter­action of the primary and secondary user networks using techniques from spatial statistics [18] in an e.ort to strike a balance between mathematical tractability and realism of the model.
Sentence 2: we argue based on extensive spec­trum use measurements and several empirical data sets on wireless network structure that a combination of Gaussian random .elds [19] and Gibbs point processes [20] with care­ful selection of parameters yield good models.
Relationship: entailment
Domain: Networks
Sentence 1: In the two scenarios the pres­ence of the bottleneck will likely appear on di.erent param- From top to bottom: total packet count, RTT, FRTX, eters: loss and retransmission events in the former, larger delays and RTT in the latter.
Sentence 2: the physical nature of the bottleneck plays a role in de.ning its impact on the tra.c and ultimately its signature .
Relationship: entailment
Domain: Networks
Sentence 1: Besides, we found that the eigen­loads, de.ned using the set of leading components, present a similar behavior across the di.erent tra.c load series.
Sentence 2: we showed that they can be categorized in two classes: (i) the deterministic, slow-varying eigenloads carry­ing the major information content and (ii) the noisy eigen­loads, which are related with the irregular variations of the tra.c.
Relationship: entailment
Domain: Networks
Sentence 1: We see that for clustering and betweenness on both GP and GW , the average AUC over 14 days is nearly identical to what we saw for the .rst day.
Sentence 2: on GP, the average is essentially the same for both clustering (i.e., 0.75 for Day 1 versus 0.744 for 14 days) and betweenness (i.e., 0.724 for Day 1 versus 0.718 for 14 days); and on GW , the average clustering (i.e., 0.787 versus 0.7625) and betweenness (i.e., 0.581 versus 0.562) are also comparable.
Relationship: entailment
Domain: Networks
Sentence 1: = f2 In order to formalize this minimal security requirement, we introduce the following security objective function 8 ' < if .i,j : Ti,j ·Ei,j · `QQ´ FABEM 1, m ' m ' (conf ,T)= ·£=1 Ei,k+£ + £=1 Ek+£,j =0 : 0, otherwise where we derive matrix E ' with size n×n from E,so that E ' =1, if Ei,j = 8,otherwise E ' =0.
Sentence 2: i,j i,j Ei,j ' =1, if vi cannot send a message directly to vj ,other­wise E ' =0.
Relationship: entailment
Domain: Networks
Sentence 1: Although several of the vulnerabilities described in Sec­tion 3 can be mitigated using the above stopgap procedures, there are many design characteristics of CALEA systems that make them inherently susceptible to manipulation.
Sentence 2: combining data from multiple IAPs into a sin­gle CDC, sending SMS and IP payloads via the CDC, and requiring a separate CCC for each call leg are intrinsic in CALEA designs based on the J-standard, leading to vul­nerabilities that cannot easily be addressed by modifying the con.gurations of wiretap systems.
Relationship: entailment
Domain: Networks
Sentence 1: FIB COMPLETION 4.1 Completion Approach Our .rst TCAM FIB merging approach, called FIB com­pletion, addresses the pre.x masking issue described in the previous section in a direct way: whenever a pre.x from the UPS does not appear in a given individual FIB, we simply associateitwithavalidNHinthisFIB.ThevalidNHisthe one associated with the longest ancestor pre.x in the same FIB.
Sentence 2: each null NH entry described in Section 3 is replaced by the .rst valid NH entry encountered when going up the corresponding trie branch towards the root.
Relationship: entailment
Domain: Networks
Sentence 1: Third, traf.c-aware optimization saves energy more ef.ciently than pure optimization for discovery performance.
Sentence 2: finally, in both mobility scenarios, single-radio PSM with traf.c­aware optimization achieves similar performance to dual-radio GPSM without the optimization., if the additional traf.c informa­tion available, the one-radio architecture could be tuned in a manner that would overcome the disadvantage of not having an extra radio.
Relationship: entailment
Domain: Networks
Sentence 1: Therefore, while a considerable part of transmission opportunities is used by the Common Channel assignment to send control packets, these opportunities are used by the MILP strategies to send data packets.
Sentence 2: the MMFlow/Con.ict-free case performs much better than the Common Channel assign­ment especially under high data tra.c, because it computes a schedule for radio links so that collisions and interference are avoided.
Relationship: entailment
Domain: Networks
Sentence 1: Our answer to this question is di.erent depending on whether one considers the directed or bidirectional version of the problem: For any oblivious power assignment p, there exists an instance with n directed communication requests need­ing O(n) colors when using p, but only one color when using a di.erent power assignment.
Sentence 2: oblivious power assignments cannot yield approximation ratios better than n for the directed interference scheduling problem, which corresponds to the worst possible per­formance guarantee.
Relationship: entailment
Domain: Networks
Sentence 1: For (iv), we note that having an in.nite number of overlays with in.nitesimal demands is equiv­alent to having all the in.nitesimal demands on a single overlay, Average latency (us) each of which tries to minimize its own latency.
Sentence 2: (iv) 20000 is equivalent to having a single overlay using overlay source rout­ 15000 ing.
Relationship: entailment
Domain: Networks
Sentence 1: Rather than digging into the details of such an interaction, we present in this section an overview of some rich mul­timedia applications that can be built on top of Janus.
Sentence 2: we will describe how di.erent plugins, sometimes with more than a single instance per user, can be used in an e.ective way as bricks to realize a much more complex ap­plication than the one each individual plugin was designed for.
Relationship: entailment
Domain: Networks
Sentence 1: Indeed, as the slope of the ROC curve .attens, the increase in true positive rate is proportionally smaller than the corresponding increase in false positives.
Sentence 2: a higher signi.cance level, although it always increases the true pos­itive rate of the test, is not always productive as it eventually does more bad than good through increased false positive rates (i.e.
Relationship: entailment
Domain: Networks
Sentence 1: The lower bound and the upper bound of r is derived as follows: rlow = min{i|si < 10si+1 } rhigh = min{i|si < 2si+1 } After obtaining the range of r, we run our algorithm on eachpossibility of r from rlow to rhigh.
Sentence 2: on each instance ri, the quality of the result is calculated as follows: ||X - U × V|| ||V - V '' || quality(ri)= .1 +.2 dn rid quality(ri) is the quality metric of how good the estimation assuming there are ri FGs.
Relationship: entailment
Domain: Networks
Sentence 1: Inspired by the practice of visualizing a surface through meshing neighboring vortexes in computer graphics, in Modellet, we try to approximate the possibly irregular radio map by meshing neigh­boring .ngerprints.
Sentence 2: modellet adapts to the speci.c local data density and environmental conditions by fusing informa­tion from both measured .ngerprints and signal propagation mod­els, based on the key concepts of the supporting set.
Relationship: entailment
Domain: Networks
Sentence 1: This framework can be used to analyze connectivity properties of di.erent application scenarios and node deployment strategies, such as uniform and non-uniform distributions;  We show how to apply discrete percolation theory to characterize and solve connectivity issues in wireless ad hoc networks.
Sentence 2: we characterize the CTR for connectivity in a regular obstructed environment;  We derive analytical expressions for the probability of crossing links between parallel and perpendicular seg­ments sharing an intersection, and these results are used for deriving a lower bound for the probability of connectivity at intersection;  Combining results of discrete percolation theory and the probability of connectivity at intersections, we com­pute the CTR for connectivity in the overall network.
Relationship: entailment
Domain: Networks
Sentence 1: We presented preliminary results from an early version of the system which demon­strate that we are able to save energy in a localisation application.
Sentence 2: our localisation runs show an increase in battery life from 9.2 hours using GPS to 22.2 hours using a combination of ac­celerometer, GPS and 802.11, with little change in the .delity of the location data.
Relationship: entailment
Domain: Networks
Sentence 1: Our Approach: We propose a mechanism to locally infer and judiciously propagate information about obstacle pres­ence in the network, to early avoid the broader network re­gion around the obstacle, towards achieving near optimal data propagation paths.
Sentence 2: the sensors lying close to the boundaries of the obstacle are able to locally and distributively identify themselves as such, by the fact that the rescue mode used is again followed by greedy propaga­tion closer towards the destination.
Relationship: entailment
Domain: Networks
Sentence 1: Each pair of inside and outside host groups with strong interactions corre­sponds to a dense (bipartite) subgraph in the original TAG and the bipartite (hyper)graph induced by the low-rank core matrix is re­ferred to as the (core) latent hypergraph of the original TAG.
Sentence 2: the tNMF method approximately decomposes aTAG into a series of dense subgraph components and a (core) latent hy­pergraph representing inter-connection structures among the graph components.
Relationship: entailment
Domain: Networks
Sentence 1: Using the media layering concept, the presentation inter- val of a buffered frame is decided by the current presenta-tion quality, which is decided by how many media layers are transmitted.
Sentence 2: a same number of stored frames in the buffer can have different semantics, i.e_, denoting dif- ferent presentation lengths, depending on which presenta- tion quality and which media layers axe currently adopted.
Relationship: entailment
Domain: Networks
Sentence 1: Table 4: Determining traf.c classes and user behavior Client tag Server tag Traf.c class, User behavior   web user, proxy user website Browsing mail server mail server Mail  node  Gaming [abuser][blocked] server n/a  Chat chat server n/a IRC server Chat [streaming node] [streaming node] Streaming   Malware affected host affected host p2p node p2p node P2P [ftp share] ftp server Ftp The unconstrained endpoint approach can be applied in a straight­forward way to the traf.c classi.cation problem.
Sentence 2: there is no reason to constrain ourselves to strictly observing pack­ets and connection patterns.
Relationship: entailment
Domain: Networks
Sentence 1: How manyrandom nodes would a scalable overlay need to contact to .ndalow latency route?Figure1shows thatfor most host pairs, the bottom 50% of potential 1-hops(sortedby total latency) does not contain a single low latency route and even considering the bottom 97% of one-hop intermediates still miss manyof the latency improvements (only 30% of the paths would have lower than a 400 ms latency, compared to at least 45% of the paths when using the best 1-hop).
Sentence 2: 97% of the time, a randomly chosen intermediary will not signi.cantly improve latency.
Relationship: entailment
Domain: Networks
Sentence 1: A feasible sampling method for .ow records must (i) control sample size within a sampling budget; (ii) yield su.ciently accu­rate estimates; and (iii) provide samples in a su.ciently timely manner for applications.
Sentence 2: the sample design must match the statistical distribution of .ow fea­ 1The analytic value of full data over summaries decreases with age, which may in any case be curtailed by policy.
Relationship: entailment