sadat2307/MSciNLI huggingface dataset:
Domain: Networks
Sentence 1: To structure our tests, we charted all possible combina­tions of features derived from the .elds of the 802.11 MAC header, and marked non-standard or unusual combinations (the full chart can be found in our Technical Report [2]).
Sentence 2: we singled out combinations of frame types, subtypes and Frame Control .ags either prohibited by the standard, not explicitly prohibited but of unclear utility for the standard protocols, or simply unlikely to occur in prac­tice.
Relationship: entailment
Domain: Networks
Sentence 1: In fact, this problem is equivalent to the original problem in the sense that (1) any solution of the above problem can be converted to that of the original problem without utility degradation, and (2) both problem­s share the same objective function.
Sentence 2: after solving the above problem, we immediately obtain a rate allocation for the o­riginal problem by setting the sensing rate of v to sv = fcv, and the amount of .ow on link uv to fuv.
Relationship: entailment
Domain: Networks
Sentence 1: Recent revelations by Edward Snowden have con.rmed that ASes pose a realistic threat: the NSA s Marina program stores vast amounts of metadata for up to a year [10], while the GCHQ s Tempora program buffers data for three days and metadata for 30 days [24].
Sentence 2: tor has been speci.cally tar­geted by such adversaries in collusion with ASes [4, 6, 7].
Relationship: entailment
Domain: Networks
Sentence 1: Dependency Pro.ling: Macroscope s procedure of joining infor­mation creates rich data that enables the creation of pro.les for applications and their dependencies.
Sentence 2: macroscope can label static dependencies by their usage frequency and traf.c vol­ume (incoming/outgoing) but also .nd (temporal) relationships be­tween static dependencies such as the causal ordering introduced in Section 2.
Relationship: entailment
Domain: Networks
Sentence 1: On short timescales, we advocate extremely extensible routing designs.
Sentence 2: routing systems should support arbitrary (and possibly external) route computations, a .exible policy model, innovations in the kinds of services offered along paths (such as QoS, middlebox services, monitoring), multipath routing, and accommodate various peering technologies (i.e., not require a universal protocol is used to connect domains).
Relationship: entailment
Domain: Networks
Sentence 1: This work, in a way, represents the other side of the coin: the con­cept of anti-patterns [2] for visualization evaluation, where an anti-pattern is essentially a common and straightforward response to a recurring problem that is usually ine.cient, sometimes incorrect, and even at times highly counterpro­ductive.
Sentence 2: an anti-pattern is a good idea that turns out to not work.
Relationship: entailment
Domain: Networks
Sentence 1: 6.5 Load-sensitive differentiation Given the strong evidence of traf.c differentiation performed by some large ISPs using packet content and routing information, we now investigate whether there exists other factors that may affect traf.c differentiation.
Sentence 2: if ISPs intend to use differen­tiation to conserve limited resource in their networks, we should be able to observe a strong correlation between network load and traf.c differentiation.
Relationship: entailment
Domain: Networks
Sentence 1: 2 2 1.5 1.5 z (m) z (m)  ity of an RF-based virtual touch screen, which allows a user to input her commands to a desired computing device (e.g., an An­droid phone) by writing in the air.
Sentence 2: we feed each recon­structed trajectory as a set of instructions to an Android phone and the instructions emulate a sequence of touch screen events.
Relationship: entailment
Domain: Networks
Sentence 1: For example, taking into account that the increase of processor power is dominated by Moore s law, that is 2 times a 18 months, and the rate of traffic increase is about 2 or 3 times a year in these years , we can plot the graph that means the increase rate of processor power will not exceed the one of packets.
Sentence 2: the ratio of the growth about processor and traffic will be more than several ten times.
Relationship: entailment
Domain: Networks
Sentence 1: In DTNs, the messages are disseminated according to the store-carry-and-forward principle.
Sentence 2: the messages are stored and carried by the mobile devices and Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for pro.t or commercial advantage and that copies bear this notice and the full citation on the .rst page.
Relationship: entailment
Domain: Networks
Sentence 1: So, in general, we are not in a position to de.nitively establish which clients could have been a.ected by the server-side problem.
Sentence 2: in view of this di.culty, we only look at how widespread the impact of server-side failure episodes is over the entire month-long period.3 for each server, S,we con­sider all the failures ascribed to it (i.e., to server-side failure episodes at S).
Relationship: entailment
Domain: Networks
Sentence 1: One immediate implication of this .nding is that one can not simply cluster all the spam from a large number of spamming sources that contain similar URLs and the delivery of which is .nished within a short period of time as a single spam campaign, as burstiness is not a de.nitive characteristic for many spam campaigns.
Sentence 2: while burstiness plus distributed­ness may be a suf.cient condition for identifying a spam campaign, it is not a necessary condition.
Relationship: entailment
Domain: Networks
Sentence 1: In the .eld of co­operative localization however, most of the algorithmic in­vestigations carried out so far still consider unrealistic and synthetic TOA-based ranging errors under pedestrian mo­bility [1], [8], [10], hence biasing somehow the performance assessment in practical operating conditions.
Sentence 2: as far as we know, there does not exist any ranging-oriented parametric model that can really account for dynamic UWB intra-WBAN links.
Relationship: entailment
Domain: Networks
Sentence 1: Note that in this cause, powering down the medium or large router for some time cannot alleviate these failures because up- stream routers or the advertisers will recreate the problem.
Sentence 2: a downstream ISP cannot really eliminate the cascades by lo- cal actions such as power cycling equipment or installing filters (although installing filters can prevent the ca~scade from spread- ing downstream).
Relationship: entailment
Domain: Networks
Sentence 1: Thus, the value for a transmission range r0 calculated with Equation (9) repre­sents a lower bound for the radio transmission range that is needed to achieve a connected network.
Sentence 2: the same state­ment can be made for networks with a certain minimum node degree, i.e., P (G is k connected) = P (dmin = k) (18) the implication (G is k connected) .
Relationship: entailment
Domain: Networks
Sentence 1: In addition, one may also think that a city where Wi-Fi AP density is adequately high does not need any such system devices can connect to the remote server through the AP and download packets directly from them.
Sentence 2: the density needs to be such that the time taken to reach the next Wi-Fi AP must be less than the time over which the partially downloaded video can be played from the bu.er.
Relationship: entailment
Domain: Networks
Sentence 1: 5(a) the message will as well be forwarded along the edge sv1 before it detects that the next traversed edge is intersecting with st in point p. At this edge, the message continues with exploration of the face which intersects with the remaining line segment pt.
Sentence 2: in this example the message will continue with exploration of the outer face F1 by sending the message towards node v3 and keeping the right hand rule.
Relationship: entailment
Domain: Networks
Sentence 1: It can be considered as a special case of Dif­ferential Capacity Hando., where no AP-score weighting is performed(d = 0).
Sentence 2: the smoothed link-quality esti­mator qi is usedjointly withhysteresis suchthathando.is invoked if qj(k)>qi(k)+ß.
Relationship: entailment
Domain: Networks
Sentence 1: Through prototype­based evaluation, we show that the RPB scheme is highly e.cient and practical for current generation of sensor nodes.
Sentence 2: to support a sensor network with up to 216 nodes, establishing a pairwise key of 80 bits between any two 8-bit, 7.37-MHz MICA2 motes only requires about 0.13 second of CPU time, 0.33 KB RAM space, and 15 KB ROM space per node.
Relationship: entailment
Domain: Networks
Sentence 1: average number of packets 8 10 6 10 4 10 2 10 0 10 -2 10 -4 10 flow ID Figure 2: Average Packets Per Flow Network Aggregation Flows Total FP Abilene Abilene Abilene Geant Geant ingress routers OD .ows input links ingress routers input links 11 132 187 23 77 955 749 937 421 491 557 382 398 110 172 Table 3: Classi.ed anomalies Finally, to give us further con.dence in our labeling of anomalies as true or false positives, we manually inspected the heavy-hitters for each of the four IP header features we studied.
Sentence 2: we inspected the top-n source IP heavy­hitters, destination IP heavy-hitters, etc.
Relationship: entailment
Domain: Networks
Sentence 1: Degree -The degree centrality dv of node v is de.ned as the ratio between the node degree, d(v), and the maximum node degree in the network: d(v) dv = · (1) max.v.V d(v) 2.
Sentence 2: betweenness -The betweenness ßv of node v is the fraction of shortest paths connecting all pairs of nodes that pass through v.let s(j,k) represent the number of shortest paths between nodes j and k, and s(j,k)(v)the number of those paths that traverse node v.The betweenness of v is thus de.ned as: X s(j,k)(v) ßv = · (2) j s(j,k) =v=k.V 3.
Relationship: entailment
Domain: Networks
Sentence 1: In order to simulate the mobil­ity of the nodes, we work with the random waypoint mo­bility model and select an appropriate set of parameters for the mobility patterns of the hosts based on the semi­ speed (m/s) Figure 2: Stability for uniform speeds pause time (sec) Figure 3: Stability for mixed speeds nal work of [21] that study the behavior of the model and analyzes the problem of speed decay.
Sentence 2: [21] shows that the model fails to provide a steady state in that the average nodal speed consistently decreases over time, providing unreliable and misleading results.
Relationship: entailment
Domain: Networks
Sentence 1: 3.1.2 Game Process In each slot t, player l chooses strategy sl,t according to the probability distribution pl,t(k),k 2 Sl.
Sentence 2: the distribution pl,t(k) is calculated in each slot as follows: pl,t(k)= 1 ,t =1, (1) |Sl| 81 1 (1 -5) Ql,t-1(j, k)+ 5 6,k = j > pl,t(k)=  1 (2) >1 -P pl,t(h),k = j : h=6j,h2Sl  in the .rst slot (t =1), the probability of choosing any strategy k is uniformly distributed as in (1)4.
Relationship: entailment
Domain: Networks
Sentence 1: The intuition behind our approach is as follows: since the total amount of available spectrum is limited, adapting to spatio-temporal variations in traf.c demand can be achieved by allocating wider channels to links with higher demand by tak­ing spectrum away from links with less demand.
Sentence 2: greater capacity is assigned to heavily utilized links, thereby bene­.ting the .ows passing through them.
Relationship: entailment
Domain: Networks
Sentence 1: This paper clarifies the difference in MIMO propagation characteristics between dual and single polarized antennas using 18 fixed point field measurement data and continuous measurement data.
Sentence 2: we show the relation between the MIMO capacity factor in terms of the ratio of the second eigenvalue of the channel matrix to the first and cross-polarization discrimination (XPD) for dual and single polarization.
Relationship: entailment
Domain: Networks
Sentence 1: Here, DWiFi(t) is the number of packets that is completely trans­mitted through a WiFi network during time slot t,and DRenege(t) is the number of packets that renege during time slot t. Note that DWiFi(t) is given by 0, if Q(t)+ A(t)=0 or S(t)=0, DWiFi(t)= sup{k :k i=1 Li(t) = S(t)}, otherwise, where Li(t) (i =1, 2, ...,Q(t)+ A(t)) denotes the size of the ith packet in the WiFi queue at time t. Also, note that DRenege(t) = Q(t)+ A(t) - DWiFi(t).
Sentence 2: reneging (if any) occurs among Q(t)+A(t)-DWiFi(t) number of packets in the WiFi queue.
Relationship: entailment
Domain: Networks
Sentence 1: To describe the dynamics of user subscription, we construct and analyze a dynamic model which speci.es how users form their beliefs and make decisions based on their beliefs.
Sentence 2: we consider a discrete-time model with time pe­riods indexed t =1, 2,.... At each period t, user k holds a belief or expectation on the QoS of NSP S2, denoted by g k(.2t ) where .t 2 is the fraction of subscribers at period t, and makes a subscription decision in a myopic way to maximize its expected utility in the current period.6 we specify that every user expects that the QoS in the cur­rent period is equal to that in the previous period.
Relationship: entailment
Domain: Networks
Sentence 1: Copyright &#38;#169; 2009 ACM 978-1-60558-569­7/09/06...$5.00  1.INTRODUCTION Research and standardization activity has been accomplished at international level in order to define modern, interoperable communications and networking standards for emergency response and public safety.
Sentence 2: in Europe, the ETSI Recommendation TS 102 181 recognizes the importance of the "Location Services" in the EMTEL framework (Emergency Telecommunications ), which must "provide real-time information regarding the position of personnel or vehicles to a command point" .
Relationship: entailment
Domain: Networks
Sentence 1: As shown in the example, the proposed area partition gives tight lower and upper bounds for each subarea.
Sentence 2: for a sub­area Au with DiB (p) = Du[Mu] where p .Au,we have U[hu i (Au i (Au) - 1] ,p .Au , (13) )] = UiB (p) = U[hu where hu i (Au) denotes the index of the outer circle (centered at , hu node i), and hu i (Au) = Mu.
Relationship: entailment
Domain: Networks
Sentence 1: Without loss of generality, we assume that, the task C originates at its source s E V at slot 0.
Sentence 2: since we consider the case where there is only one task, we have Ws 0 = W , and Vi E V  {s}, W 0 = 0. i The completion time T , also called the makespan, is de­.ned as the time di.erence between the origin time, i.e., slot 0, and the .nish time, i.e., the time point when all nodes .nish their respective workload that belongs to C. T = min t, subject to Wi t = 0, Vi E V .
Relationship: entailment
Domain: Networks
Sentence 1: The kth moment of a packet t stream is de.ned as: = v k where vi is the value Mk ii , (e.g., RTT) of the ith packet in a stream.
Sentence 2: tt t the moments M0 = vi 0 = 1 and M1(v) = vi give ii i the number of packets and the sum of such values in all the packets in the stream, respectively.
Relationship: entailment
Domain: Networks
Sentence 1: For example, the following HTTP headers cache the spoofed response for a day, and impede the browsers from refreshing the page: Last-Modified: today Cache-Control: public Expires: tomorrow Mallory can poison the web-cache with spoofed pages that users will receive when they access the (poisoned) websites.
Sentence 2: this allows Mallory to cache spoofed login pages, i.e., a persistent phishing exploit.
Relationship: entailment
Domain: Networks
Sentence 1: Flooding Attacks by Exploiting Persistent Forwarding Loops Jianhong Xia, Lixin Gao, Teng Fei University of Massachusetts at Amherst {jxia, lgao, tfei}@ecs.umass.edu ABSTRACT In this paper, we present .ooding attacks that exploit rout­ing anomalies in the Internet.
Sentence 2: we focus on routing anomalies introduced by persistent forwarding loops.
Relationship: entailment
Domain: Networks
Sentence 1: Our results show that Nemo has satisfactory measurement .delity under a range of operating conditions.
Sentence 2: nemo yields a dynamic measurement range from 0.8 uA to 200 mA, a sampling rate of 8 KHz, and a minimum resolution of 0.013 uA, while only incurring an average mea­surement error of 1.34%.
Relationship: entailment
Domain: Networks
Sentence 1: The di.­culty of course for making any statement about the potential of SPTs is that the quality of the outcome of all proposed approximations is a function of both the aggregation tree as well as of the second stage, i.e., of the schedule construc­tion heuristic (given an aggregation tree).
Sentence 2: it has been unclear whether the SPT alone or the subsequent scheduling phase is to be blamed when ine.cient schedules are produced.
Relationship: entailment
Domain: Networks
Sentence 1: However, this does not mean that all .ow setups should be mediated by a central controller.
Sentence 2: we argue that micro.ows (a micro.ow is equivalent to a speci.c end-to­end connection) can be divided into three broad categories: security-sensitive .ows, which must be handled centrally to maintain security properties; signi.cant .ows, which should be handled centrally to maintain global QoS and congestion properties; and normal .ows, whose setup can be devolved to individual switches.
Relationship: entailment
Domain: Networks
Sentence 1: Subnet centric probing is used for stub networks that have a limited number of ingress points whereas vantage point spreading is designed for exploring path diversity of transit networks that have many peering points but not many internal sub­nets.
Sentence 2: we do not need to perform subnet centric probing per vantage point; we can use the same set of probes to accomplish both objectives, by independently choosing their source and destination addresses.
Relationship: entailment
Domain: Networks
Sentence 1: 2.1 The continuous case Picture the following continuous variant of our problem: Given a (possibly non-simply) connected region R C R2 and some point p E R we call that point beacon , we consider the isolevels of the geodesic distance function dp from p in the domain R as in Figure 2.1.
Sentence 2: for any point x E R dp (x) denotes the minimum Euclidean length of an open curve 1 C R with one endpoint being p, the other x.
Relationship: entailment
Domain: Networks
Sentence 1: between the statistical orthogonality between RONS channels and the number of RONS channels that the system can a.ord, since more RONS channels will make the channel-guessing attacker more di.cult to make a correct guess.
Sentence 2: we allow that the inter-RONS-channel correlation to be as great as t when selecting RONS chan­nels.
Relationship: entailment
Domain: Networks
Sentence 1: However, the question which immediately arises is whether the stationarypoints of the Lagrangian correspond to global minima of the function Ge(u, s) for some .xed u > 0.
Sentence 2: the question is whether the sta­tionary points of the Lagrangian (which are Karush-Kuhn points of (18)) are necessary and su.cient for the sought optimum.
Relationship: entailment
Domain: Networks
Sentence 1: Congestion Quota: If we assume legitimate users have limited traf.cdemand while attackers aim topersistently congest abottle­necklink,wecanfurtherweaken aDoS .ooding attackbyimpos­ing a congestionquota, anideaborrowedfrom re-ECN[9].
Sentence 2: an access router only allows a host to send a limited amount of congestiontraf.c through abottlenecklink withinaperiod of time.
Relationship: entailment
Domain: Networks
Sentence 1: Alternatively, it has the length of a collided transmission Tc or, when the node is in backo., it has the length s. In [7] the saturation throughput is calculated using re­newal theory arguments.
Sentence 2: because in a single­hop network all the nodes view the same channel state, the instants of time right after the end of a transmission are re­newal points, so it is su.cient to analyze a single renewal interval (which is the variable slot) for the derivation of the saturation throughput.
Relationship: entailment
Domain: Networks
Sentence 1: In particu- lar, we show that each new shortest path after k edge failure can be represented as a concatenation of k q- 1 original short- est paths if the network is unweighted, and an interleaving of k + 1 original shortest paths and k edges in the weighted case.
Sentence 2: if a base set of LSP's are provisioned in the network that includes all-pairs shortest paths, then ev- ery path can be restored after a single edge failure with the concatenation of at most two or three basic LSP's.
Relationship: entailment
Domain: Networks
Sentence 1: simple subset dynamic sector pooling pooling programming Example 1 0.001851 0.002894 0.005480 Example 2 0.005964 0.005964 0.006177 We make the observation that the dynamic programming bound is determined assuming that the traffic vector for each call class is known.
Sentence 2: the optimal policy assumes knowledge of the traffic vector.
Relationship: entailment
Domain: Networks
Sentence 1: At a very high level, our .ndings can be summarized as follows: Component-based analysis: When comparing DHT al­gorithms we advocate analyzing the component design decisions separately rather than comparing DHTs as black-boxes or turn-key systems.
Sentence 2: this re­quires separating systems-level design decisions, which usually are independent of the routing, from routing­level design decisions.
Relationship: entailment
Domain: Networks
Sentence 1: Therefore, if the bot-edge probability of a P2P bot­ = ln m net is pem , then the dye-pumping algorithm potentially identi.es all other P2P bots from a given seed bot with high probability as long as there are more than m bots in the net­work.
Sentence 2: it gets easier for the proposed method to reveal P2P bots as the botmaster infects more hosts in the network.
Relationship: entailment
Domain: Networks
Sentence 1: If we assume, however, that our edge-detection algorithm is rep- resentative of one sort of filtering operation, we do know the time it took to execute that operation.
Sentence 2: on our client laptop we measured # -236msec (30) o~cS c If this computation represents the time needed for filtering data that arrived over t = 10 seconds, for example, Equa- tion 19 tells us that m/n < ~cs~t (31)# = 10/0.236 (32) = 40 (33) about 40 tasks per client, for an arbitrary number of clients n. Of course, the client machine should reserve some power for consuming the data after filtering, so it should not run anywhere close to 40 such tasks.
Relationship: entailment
Domain: Networks
Sentence 1: In the agent case, data filtering happens on the server.
Sentence 2: the server's time is a combination of the filtering costs plus the time spent initializing newly arrived agents: ~_.~m, co(D,F~j,Fij ) rtCi,~,t (3) TSA----E otsS s + ~sS----[- i=1 j=l If we know that the expected value of the computing complexity cij is p, then we can simplify and obtain a bound on the number of client tasks (agents), m. we assume that EO cij (D, F[j, F O) mp --(4) otsS s otSS s Now TSA < t, m# + rtC, n,t < t (5) ozs S s m <_ (asSS-rCinit) i (6) # Wireless network, Tw.
Relationship: entailment
Domain: Networks
Sentence 1: Our study, performed over several weeks in networks on University of Wisconsin-Madison campus, provides a .rst benchmark of its kind for DHCP-enabled networks.
Sentence 2: we present results of this perfor­mance study for two networks with contrasting administra­tive styles a small, tightly controlled network where each host is authenticated before being admitted into the net­work, and a larger loosely administered network where such tight central administration is di.cult for a variety of tech­nical and non-technical reasons.
Relationship: entailment
Domain: Networks
Sentence 1: ODMAC o.ers to the administrator a tool to control if the adjust­ment of the duty cycle will occur to the sensing or the relay­ing tasks of the node.
Sentence 2: one of the parameters of ODMAC, named SP rob, de.nes the probability that if there is a need for an adjustment to the duty cycle, this will favor the sensing duty cycle.
Relationship: entailment