sadat2307/MSciNLI huggingface dataset:
Domain: Networks
Sentence 1: location of the user device either using geometric properties (for the idealized case) or using a message map created beforehand which contains information about the set of messages that can be received at each location.
Sentence 2: we see that the main idea here is to leverage the capability of transmitting at different power levels so that every location in the system can be described by a unique set of messages transmitted by various APs.
Relationship: reasoning
Domain: Networks
Sentence 1: Since the AHMAC operates in PCF-enabled mode under high-tra.c conditions, the MPP and MAPs can have more opportunities to exchange frames with each other.
Sentence 2: figure 8: Total network throughput of an IEEE 802.11s mesh network with a single-interface envi­ronment Figure 9: Average throughput per user in an IEEE 802.11s mesh network with a single-interface envi­ronment the total network throughput of the AHMAC in­creases until N = 110, and an AHAMC can accommodate 57% more users compared to a legacy DCF.
Relationship: reasoning
Domain: Networks
Sentence 1: s  {r}, q1t 2 n xr.8 xr.8 xt2 1 ßC k k 1 n = C (qrtr 2 - q1t1 2)(1 - ß) + qrtr 2ßC which also holds for F s(xs).
Sentence 2: r.s Let lim hr(xs) = lim (f r(xs) - f r(xs)) = 0 xr.8 xr.8 q1t1 2ßC n n .
Relationship: reasoning
Domain: Networks
Sentence 1: Subse-quently, the probability that packet rr appears in v at time t is at most 2p/z and the probability that it doesn't appear in v at all is at least 1 2p z According to Lemma 5.7, the number of possible packets like ~r is at most 3/2 mz.
Sentence 2: none of them will be in node v and time t with probability at least as needed.
Relationship: reasoning
Domain: Networks
Sentence 1: Here, we gave an example in which E [Z] decreases with increasing s 2 ., but the channel variability has no e.ect on E [Z].
Sentence 2: we may not be able to claim that the variability of the erasure Depending on whether this weighted sum of covariances is negative or positive, i.e., whether s 2 V is less than or greater than Ns 2 .
Relationship: reasoning
Domain: Networks
Sentence 1: In threshold and pri­ority sampling, w(X) is an unbiased estimator of the de- P sired value i.I wi, so the unknown mean is exactly this desired value.
Sentence 2: more precisely, we will construct functions lower, upper : X×[0, 1] .R such that for any X .X, Pr[lower(X, e) > E [w(X)]] = e (5) Pr[upper(X, e) < E [w(X)]] = e (6) on a1 - 2e signi.cance level, we can use [lower(X, e), upper(X, e)] as a con.dence interval for E [w(X)].
Relationship: reasoning
Domain: Networks
Sentence 1: Considering that a tree is created for each combination and that all trees must be traversed, this implies that This algorithm is applied to every node until it contains a number of rules less or equal to a threshold value that was set to 16 in our ×17= .... experimentations.
Sentence 2: according to Equation 3, a large number of memory accesses must be performed for each  decided to make this parameter variable unlike the similar destination fields, then EffiCuts seldom splits a third dimension parameter in EffiCuts.
Relationship: reasoning
Domain: Networks
Sentence 1: Since the .rst intra-3G handoff usually takes a longer time (about 8-10 sec­onds), the second packet (at 10th second) is delivered via 3G since it is still in DCH/FACH.
Sentence 2: in this case, the packet interval is very close to Tidle (also 10s); the 3G duration is sensitive to whether the packet arrives before Tidle times out.
Relationship: reasoning
Domain: Networks
Sentence 1: When switching to the other mode (say, from SS to DS), the lowest rate in the new mode which loss-free goodput is closest to the best-goodput rate in the old mode receives highest priority.
Sentence 2: in the example of Figure 4, when probing upward from the SS mode to the DS mode, 108DS, which loss-free goodput is the closest to the best goodput estimate in SS (i.e., 108SS that has goodput esti­mate of 96.5Mbps), has the highest probe priority among all candidate rates within DS.
Relationship: reasoning
Domain: Networks
Sentence 1: As in our implementation, the router R4 retrans­mits an end-to-end StopIt request up to three times before it sends a request to its StopIt server, and the victim needs to send one StopIt request and resubmit three .lter replacements to stop one attacking source.
Sentence 2: it can stop at most 28/4=7K attackers per second.
Relationship: reasoning
Domain: Networks
Sentence 1: They may oversee and supervise the organization and distribution of tool functionality, where more abstract and higher level reasoning is critical, but systematic and large volume­information-based decisions and reasoning are probably best handled through automation.
Sentence 2: the KP requires an organizing framework that can evaluate performance and distribution constraints, functional decomposition, and policy constraints in order to organize the composition of functional subcomponents, and then monitor and possibly revise such a structure under behavioral expectations.
Relationship: reasoning
Domain: Networks
Sentence 1: Since every row in G represents an end-to-end path, we have GuN = GwN .
Sentence 2: any identi.able link sequence in the network can be repre­sented by a vector x such that x = z for some z;for such GT a link sequence, TNTNTN TN xu = z Gu= z Gw= xw if the link sequence includes an incoming link for node N, it must also include an outgoing link.
Relationship: reasoning
Domain: Networks
Sentence 1: The wireless channel linearly combines the signal samples corresponding to the two streams.
Sentence 2: the receiver receives thefollowinglinear combinations onits two antennas: y1(t)= h11s1(t)+ h21s2(t) (5) y2(t)= h12s1(t)+ h22s2(t), (6) where hij is a complex number whose magnitude and angle refer to the attenuation anddelay along thepathfrom the ith antenna on the sender to the jth antenna on the receiver, as shown in Fig.
Relationship: reasoning
Domain: Networks
Sentence 1: Decoupled Design As described above, utility maximization congestion control is based on feedback characterized by the price function pn(t).The price is a function of tn(t), which can be described as the fraction of time contention neighborhood n is busy due to transmissions.
Sentence 2: congestion control solutions can be classi.ed according to the way the congestion price is realized.
Relationship: reasoning
Domain: Networks
Sentence 1: However, our model di.ers from the models in [4, 1] in two ways: Adversary model : The adversary in [4] and [1] is as­sumed to have the same resources and communica­tion capabilities as an ordinary node in the network.
Sentence 2: that adversary model deviates from the so­called Dolev-Yao model in [6].
Relationship: reasoning
Domain: Networks
Sentence 1: The average response cost for a request for to object j is at most ~(p- n + n. p. 1) = p + 1.
Sentence 2: it follows that the transient access cost of this placement strategy is AcoessCost2 = ~ ~j~ix,n](p + i) = 0(i) from the above it is clear that the transient access cost of Pr(t) to could be arbitrarily far from the optimal.
Relationship: reasoning
Domain: Networks
Sentence 1: (./nT)||H||2 LL L and xL stands for xs or xr when source or relay transmits, respectively.
Sentence 2: () P (L) 22R o = FL - 1(8) where FL (·)is the cumulative distribution function (CDF) of z for the link L,which is given by [22] k0 -znT/.L nT-11 znT FL (z)=1 - e k0!.L k=0k0,...,knT kn =k ..k-k0 (I) -nT z.
Relationship: reasoning
Domain: Networks
Sentence 1: Yet, the absolute performance is quite different, as without attack, small scale net­work performance is signi.cantly better than large scale network performance.
Sentence 2: our goal is not to advance the aforementioned arms race by developing attacks, victim counter strategies, counter attacks, etc., but rather to explore the impact of a class of attacks that are dif.cult and time consuming to detect due to their compliance to all protocol rules.
Relationship: reasoning
Domain: Networks
Sentence 1: How are performances evaluated (detection, time location estimation, Pkt identi.cation, anomaly classi.cation) ?
Sentence 2: this list of issues should be read as a plea for joint common benchmarking ef­forts, based on scienti.c methodologies, that must be favored, encouraged and rewardedbythe research com­munity, in the spirit of what has been undertaken in the Japanese WIDE project, www.wide.ad.jp/, or in the European Tra.c Monitoring and Analysis COST-Action, http://www.tma-portal.eu/cost-tma-action/.
Relationship: reasoning
Domain: Networks
Sentence 1: By repre­senting the LAN as a virtual node in the topology graph, the LAN interfaces are modeled as point-to-point links between Figure 3: A network with routers on a LAN Figure 4: A representation of the LAN the routers and the virtual LAN node, and the topology be­comes a graph with asymmetric links.
Sentence 2: fIFR described in the previous section can be applied without any hitches.
Relationship: reasoning
Domain: Networks
Sentence 1: The average smoothed RTT remains the same with or without delayed ACKs, suggesting that the underlying network conditionsdid not change.
Sentence 2: thein­creased numberof smoothedRTT spikesrepresent morefre­quent, unnecessary congestion timeout events, anotherpiece of evidence that indicates that the congestion window is be­ing over-driven when delay ACKs are turned o.. 5.3 WorkloadandTestbed It remains to be explained why the choice of workload a.ectsthe results so much, or whetherthe sub-optimalper­formance of disabling delayed ACKs is caused by a di.erent  Figure 9: Smoothed RTT with delayed ACKs  experimental environment.
Relationship: reasoning
Domain: Networks
Sentence 1: m 1 Note that, by symmetry, E[Xj ]= and hence by [] 2 .3lg n 1 independence, E Xj = 3 .
Sentence 2: the median depth j=1 n1 of vi is at most k if and only if .k j=1Xj = i .it follows from Markov s inequality that vi has median 1 depth at most 3 lg n with probability at least 1 - 2 .
Relationship: reasoning
Domain: Networks
Sentence 1: But, consider the network in which ISP i at city v buys transit from ISP j at city r. In this case, ISP i pays $15050+T2 and ISP j receives T2, where T2 is the transit payment in this case.
Sentence 2: any positive pay­ment T2 between T1 and T1 - 7470 will make both ISPs agree to change to the latter network and hence the former one is never a Nash equilibrium.
Relationship: reasoning
Domain: Networks
Sentence 1: In the 1 VS case, the VS is placed nearer to the physical sink, while in the 2 VS case the second VS is placed farther from the physical sink.
Sentence 2: while the single VS provides the normal advantage in the face of congestion (relatively rare at the lowest rates we tested), the position of the second VS relative to the physical sink allows it to pro­vide the additional bene.t of servicing motes disadvantaged by unstable paths to the physical sink at low rates.
Relationship: reasoning
Domain: Networks
Sentence 1: We would like the following three criteria to be satis.ed: i) the weight for the pixel should be greater if the query has higher priority than other queries, ii) the weight for the pixel should be greater if the query s deadline is shorter than other queries since higher weight will result in the data being trans­mitted .rst, and iii) the weight for the pixel should be zero if the pixel is not in the region of interest of query i.
Sentence 2: the weight wi(u,v)is de.ned as: 1 wi(u,v)=piIi(u,v)(1)di  4.2 Progressive Compression Engine Data compression is an integral component of rich sen­sor networks where the data rates can be considerably higher than available bandwidth.
Relationship: reasoning
Domain: Networks
Sentence 1: 4 ln n o ' 2(b/2)i 4lnn Then Pr(Xu = 1) = 1 - e - = where the o' 2(b/2)i inequality follows from the standard 1-x = e -x.
Sentence 2: the 4n lnn expected number of newly informed nodes is less than o' 2(b/2)i .
Relationship: reasoning
Domain: Networks
Sentence 1: Smartphones have become highly prevalentin the developed world not only as a communications device butalso a measurement study platform [12], especially Android-basedsmartphones [13].
Sentence 2: we choose to capitalize on the Android­based platform and popularity of free, widely-available applica­tions, offering users a functional WiFiscanner in exchange for theirchoice to contribute data toour database (covered by an IRBtopro­tect our participant s data).
Relationship: reasoning
Domain: Networks
Sentence 1: 1.1 Centralized and Distributed Protocols Wireless transmissions by hosts within proximity of each other can interfere.
Sentence 2: several medium access con- trol (MAC) protocols for wireless networks have been pro- 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 profit or commercial advantage and that copies bear this notice and the full citation on the first page.
Relationship: reasoning
Domain: Networks
Sentence 1: We have mentioned earlier in Sec­tion 4 that the DA fairness index is calculated within some time interval T (note, similar intervals are used for calculat­ing Jain s index).
Sentence 2: typically tstart would be the time of the .rst occurrence of packet departure event and ac­cordingly tend is the time of the last occurrence of packet departure event irrespectively of the host k within the nor­malized window, w, drawn from ordered sequence S. In case if the o values are derived using the approach presented in the previous paragraph, tstart and tend would be the times of the .rst and the last packet captured at the access point correspondingly.
Relationship: reasoning
Domain: Networks
Sentence 1: NM : ni = B (11) ni .S The interesting aspects of this problem are: 1) FFC constraints can be transformed to it; and 2) while the original formulation has a large number of constraints, all of them are satis.ed as long as one constraint involving the largest M variables is satis.ed.
Sentence 2: if nj is an expression for the j-th largest variable in N, all constraints above hold if: M nj = B (12) j=1 if we can .nd ef.cient (linear) expressions for the largest M variables in N, we can replace all original constraints with one con­straint.
Relationship: reasoning
Domain: Networks
Sentence 1: Throughout the rest of the paper we denote by NNRT (NRT) the number of non-real-time users (real-time users) and by SNRT (SRT) the corresponding set.
Sentence 2: due to the variety of the supported services QoS prerequisites, each mobile user is associated with a proper utility function Ui which represents his degree of satisfaction in relation to the expected tradeoff between his utility-based actual uplink QoS-driven throughput performance and the corresponding energy consumption per time slot t. it can be expressed as: ( *, PP ) T R F f ( ), PP ) TR ,( Y , * iii -i ii iii -i UR(, PP,) ;; (3) ii -i Pi Pi where R* "RF () f Y denotes user s i actual uplink transmission i iii rate (i.e.
Relationship: reasoning
Domain: Networks
Sentence 1: Let Nreal be the estimated Nreal by p´A, then Nreal = p´A × VA = VA/Vvor.
Sentence 2: psel can be ´ estimated as follows in the uniform node distribution: Psel = Nreq/N´ real =(p × VA)/(VA/Vvor)= Vvor × p That is, when the density of upper level is speci.ed to be p, the probability of node selections is approximately calcu­lated by the area size of each node s Voronoi cell Vvor and the requested density p. For example, Fig.
Relationship: reasoning
Domain: Networks
Sentence 1: An important take-away is that even in LTE cell networks that have 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 00:00 01:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 00:00 01:00 Time Time (c) Concourse(d) Middle/Upper Level Sections Figure 3: eNodeB Volumes higher aggregate capacity for an individual cell site than the pre­vious generation 3G networks, a small number of high bandwidth .ows can consume a substantial portion of that capacity over short time scales.
Sentence 2: the impact of such .ows on the other users needs to be carefully considered.
Relationship: reasoning
Domain: Networks
Sentence 1: By applying the well-known EM (Expectation/Maximization) algorithm [21], it can be shown that this iterative procedure is increasing in like- lihood.
Sentence 2: the overall computational complexity of the re-estimation algorithm is essentially proportional to T. the parameters for the HSMM model caa be estimated ef- ficiently within the framework of dynamic mobility model tracking illustrated in Figure 1.
Relationship: reasoning
Domain: Networks
Sentence 1: The purpose of the simulations is mainly to compare the size of the reports and the effectiveness of the bit-sequences in reducing the false invalidation rate, denoted FIR, which is defined as number of items - j&#38;ely - invalidated FIR = number - of - items - invalidated To the best of our knowledge, there has been no study on the distributions of potential reconnection patterns.
Sentence 2: we have chosen to use the two patterns, a uniform distribution and a non-uniform distribution with a peak in the middle of the window as described in the Examples 1 and 2 of Section 4.1 for our simulations.
Relationship: reasoning
Domain: Networks
Sentence 1: As a by-product of this thresholding, we also notice that essen­tially none of the large number of IPv6 peerings that have been established at our IXPs carry any signi.cant traf.c.
Sentence 2: while connectivity for IPv6 is there, traf.c still ranges in the order of less than 1% at each of our IXPs.
Relationship: reasoning
Domain: Networks
Sentence 1: InordertoevaluatetheperformanceofRALevin-Campello algorithm[10]inTIGRframework, thepower-ratefunctions (BERperformance)for supportedmodulation schemes,given in Table 1, are determined in order to calibrate the required SNR for di.erent scenarios.
Sentence 2: the BER perfor­  f  2 MHz mance of TIGR link for the case of ideal receiver, non­ideal receiver and RF transmission is derived and shown in Fig.
Relationship: reasoning
Domain: Networks
Sentence 1: N Proof: The node is equally likely to be at any of the N grid points.
Sentence 2: p1 = P(there is one node within adistance2Kof the transmitting node) = (number of grid points within distance 2K)/(total number of grid points) = 2K 1+ 1+4K(2K+1) r=1 4r N = N .
Relationship: reasoning
Domain: Networks
Sentence 1: It is well-known that popular publishers tend to make popular objects, and they can also explosively increase the popu­larity of unpopular objects (from not-so-popular publishers) merely by publishing them under their names [6], [7].
Sentence 2: we are motivated to propose more ef.cient caching policy that can con­sider the above practice of popular objects and popular publishers.
Relationship: reasoning
Domain: Networks
Sentence 1: This scheme, however, has two drawbacks: (a) high overhead due to periodic .ooding of keys, and (b) a period of vulnerability (during which packets may be delivered to multiple nodes) that grows with the time interval between consecutive .oods of (IPaddress, key) pairs.
Sentence 2: an at­tempt to reduce the .ooding overhead increases the period of vulnerability.
Relationship: reasoning
Domain: Networks
Sentence 1: In or­der to avoid router overload and limit the amount of SNMP tra.c through the network, network operators usually se­lect a small number of metrics to be polled by the Network Management Station and base their network provisioning decisions on those speci.c metrics.
Sentence 2: it is not uncommon for a network operator to collect link utilization measurements and infer delay per­formance based on the collected information.
Relationship: reasoning
Domain: Networks
Sentence 1: Un­like livecast, where the source could modify layer rate on the .y according to network dynamics[12], the playback stream­ing requires the layer rate to be determined o.ine, or ad­justed with limited .exibility.
Sentence 2: an optimal layer rate allocation scheme is impossible without extensive mea­surement study on crucial factors, including the population average outbound/inbound bandwidth ratio (c) Average Number of Supplying Peers Figure 13: Performance Comparison of Di.erent Layer Rate Allocation Schemes (Average Request Rate=120req/hr, Bu.er Length=5min)  5.
Relationship: reasoning
Domain: Networks
Sentence 1: These two e.ects enable the separation of dynamics to better understand and analyze the harvesting source.
Sentence 2: we assume that the input power at a given time and position can be written as: Pharv (r,t)= PS (r,t) · p(r,t)(1) where Pharv is the input harvesting power at a position r and time t, PS refers to the slow dynamics in power units and p stands for the fast dynamics, which is described as a di­mensionless stochastic process.
Relationship: reasoning
Domain: Networks
Sentence 1: If so, its re­ported size, after normalization with q,is q -1 max{z, qx i} = max{zq -1 ,x i}; see Section 2.5.4.
Sentence 2: estimated usage arising from smart sampling of the measured .ow (if any) produced by sampling the packets of original .ow i is yxi =wi max{zq -1 ,x i}, (3) where the wi are mutually independent indicator random vari­ables taking the value 1 if .ow record i would be selected during smart sampling, i.e., with probability pz/q(x i).
Relationship: reasoning
Domain: Networks
Sentence 1: Note that we call the same atomic piece of information a Certi.cate when it is created and supplied to inform about the neighborhood; and we call it a Proof when it is reused and supplied to prove a link state.
Sentence 2: the Certiproof table of node B contains Certi.cates signed by various neigh­bors of B; in each of these Certi.cates, the address .eld contains the address of B.
Relationship: reasoning
Domain: Networks
Sentence 1: i=1 6.2 Expected Overhead of HHBA We know that the maximum number of retransmissions at the ith hop for a given reliability, even when using HHBA instead of HHB, is the same as Ni as computed above.
Sentence 2: after sending Ni copies of the packet, a sender at ith hop can stop further retransmissions even if it does not get an acknowledgment.
Relationship: reasoning
Domain: Networks
Sentence 1: If we increase the HELLO interval from 1 second to 60 seconds, the number of LS updates is decreased by 52% to 80%, and the number of SPF computations is decreased by 77% to 82%.
Sentence 2: we can e.ectively reduce the overhead caused by HELLO messages, LS updates and SPF computation by increasing the HELLO interval.
Relationship: reasoning
Domain: Networks
Sentence 1: NOTIFICATION-BASED ASSERTION MANAGEMENT FRAMEWORK The Identity Federation functional architecture for SIP, allows a SIP UA a very granular level of control in en­abling/using and disabling Identity Federation for their as­sociated User Information from one SIP context to another.
Sentence 2: the federating UA has freedom to request, receive and use multiple SAML Assertions from one/or more APs to one/or more RPs, to avail of Identity-Federation based ben­e.ts.
Relationship: reasoning
Domain: Networks
Sentence 1: COMPONENT DETAILS (HARDWARE AND SOFTWARE REQUIREMENTS) Table 1 Table depicting the various hardware and software requirements of Aegis Taciturn.
Sentence 2: s.NO    COMPONENTS 1 Microcontroller AT89C51 2 Vehicle Speed Sensor 3 GPS Module-ZX 41215P 4 GSM Modem 5 RS-232 6 Call Sensing Circuit 7.CONCLUSION AND FUTURE WORK Aegis Taciturn would act as a benchmark rendering effective and quick response from the cops for reporting use of mobile while driving by the drivers.
Relationship: reasoning
Domain: Networks
Sentence 1: In addition, when we color node i in position X, compared with Case (1) where we color node i - 1 in position X, now the network has one more colored node while all the other settings are exactly the same, so we have Zi=-1 v Zi = .
Sentence 2: for all a, we have Pr(Zi-1 >a) = Pr(Zi=-1 >a) v Pr(Zi = >a) = Pr(Zi >a).
Relationship: reasoning