esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Related Work  
### Existing Routing Protocols  
Since wireless sensor networks and traditional wireless networks have different characteristics, a number of routing protocols have been proposed to address the challenges of sensor networks. Flat routing protocols are designed for networks with homogenous nodes, _i.e._, all the network nodes have the same processing and data transmission capabilities, while their packet forwarding role is also similar. If a single path is used in transmission of data from sensor nodes to sink nodes use of such a protocol in WSN results in faster depletion of energy on nodes that are in the route path. Thus such type of strategy can easily cripple a segment of a network and possibly make it inaccessible. Discovering a path in a network is a resource intensive operation and to reduce overhead for path discovery on-demand routing protocols (e.g., AODV [1], TORA [2], DSR [3]) have been developed. These routing protocols are reactive routing protocols but they do not possess the capability to discover multiple paths, hence all of them suffer from the problem of non-uniform network usage. In [6,7] authors have proposed load sharing and network congestion reduction techniques which can be applied to any routing algorithm for WSNs domains. Directed Diffusion [8], Sensor Protocols for Information via Negotiation (SPIN) [9], Rumor Routing [10], Minimum Cost Forwarding Algorithm (MCFA) [11], and Energy-Aware Routing (EAR) [12] are some of the routing protocols that possess the capability of low topology maintenance cost and use multipath discovery to improve performance and reliability. Another protocol, AOMDV [13], which is a multipath routing protocol and an extension of AODV solves the issue of single path routing, but it doesn't take energy of the network into account. Another protocol energy efficient and collision aware (EECA) [14] is a multipath routing protocol which takes energy of nodes into account but this protocols assumes that the location and direction of nodes is well known in the network, also it tries to avoid collision by choosing distant route paths which we think is not a better approach as collision can be avoided at MAC level by RTS/CTS features [15].  
All the protocols mentioned above try to solve the single path and energy constraints issues but according to our knowledge none of the protocols try to reduce number of hops and end to end latency using hotline based approach in wireless sensor networks, which provides higher throughout and reliable communication that is a key factor for mission critical WSN applications.
# Caching Transient Contents in Vehicular Named Data Networking: A Performance Analysis
## 4 V-NDN: Design Concepts and Caching Strategies
### Cooperative vs. Autonomous Caching  
In wired networks, cooperative schemes usually lead to better performance than autonomous ones, in terms of low cache redundancy and reduced retrieval latency, at the expense of a potentially high signalling overhead [23]. Things however change in vehicular environments where, due to node mobility and unstable connectivity, it is difficult to exchange consistent information about the status of the network and the CSs of vehicles and take decisions at line speed. Traditional approaches for ad hoc networks (outside the NDN context) such as the one in [24], which takes decisions based on information density estimated during an inference phase, seem not affordable in NDN, since they would introduce a slowdown in the forwarding fabric [16; 25].  
To cope with the dynamicity of vehicular topologies, some studies considered mobility-aware caching strategies that can be applied in the presence of a full or partial RSU infrastructure. In [26], a proactive caching policy is proposed that takes into account the content popularity and the vehicle mobility prediction. The latter information is used to prefetch the contents at the RSUs the vehicles will be connected to during their journey, thus their requests will be satisfied with lower latency. However, the strategy requires the collection of prior mobility data over which applying offline a mobility prediction algorithm. In [27], a scheme called Cooperative Caching based on Mobility Prediction (CCMP) is designed where urban areas are divided into hot regions based on users' mobility patterns and a prediction model is applied to compute the probabilities that vehicles re-visit the hot regions. Nodes with higher chances of staying in hot regions and for longer times are chosen to cache contents. In [28], mobility prediction is used to create clusters of vehicles. The most suitable vehicles (e.g., the ones with the best channel quality) are selected as cluster heads and act as caches: they receive contents from the closest RSUs and cache them to serve requests from other vehicles. A shortcoming of such approach is the lack of fairness, since only few nodes handle caching operations.  
When the mobility prediction is not available, implementing cooperative caching schemes may be disadvantageous. For instance, the work in [22] shows that a notable caching scheme with implicit coordination, Leave Copy Down (LCD) [29], has results comparable to a Never Cache policy, in which vehicles do not cache packets. In the rest of the paper, we focus on a general scenario where vehicles mobility patterns are a priori unknown and communications are mainly based on short-lived V2V interactions. Autonomous caching schemes better suit this type of situation: they are lightweight solutions that do not require additional knowledge of the network topology and do not incur in further signalling.
# Authentication and Billing for Dynamic Wireless EV Charging in an Internet of Electric Vehicles
## 3 Related Works
### Billing Models in DWC Systems  
In addition to authentication schemes, fair and privacy-preserving billing models are also reported in the literature, some of which utilize segment-by-segment billing [47; 48] while others adopt a single bill approach [9; 49].  
In [47; 48], the authors assume that a tamper-resistant module is installed on each EV to perform secure calculations required for authentication and billing. Segment-by-segment billing is performed with a bidirectional audit between the CSP and the EV, while assumingthat a constant amount of energy is supplied per segment. A key disadvantage of segment-by-segment billing is that it enables free-riders to charge before and after authenticated EVs by utilizing the associated handover delay from one segment to another. This particular problem is addressed by the authors in [50]. The proposed privacy-preserving billing approach in [50] uses token authentication and tamper-proof meters, and requests each authenticated EV to securely share its power levels before and after the charging lane with \(N\) segments. These levels are then compared to the actual energy supplied by the charging pads, and the difference in readings helps identify free-riders while preventing over-billing of authorized EVs.  
Another lightweight and secure billing approach using hash functions and symmetric key cryptography is proposed in [49]. The process starts with a pre-registration phase using symmetric keys, followed by hashing with the different charging segments. At the end of the charging process, the EV communicates its received energy level with all the associated entities and is billed accordingly. A single bill approach is also adopted by the authors in [9], to achieve the required computational efficiency given effective privacy-preserving authentication protocols to ensure data security.
# Static Bearing Capacity Investigation of a Novel Prefabricated Light-Steel Beam-Column Connection
## 3 Experimental Results
### Effect of the Truss-Beam Configuration on the Proposed Beam-Column Connection  
The effect of the truss-beam configuration, including a \"V\"- and \"\(\wedge\)\"-shaped brace arrangement, on the static bearing capacity of the proposed light-steel beam-column connection is investigated in this section. This is because for specimens with different truss-beam configurations, the position of the loading point is different. To make a direct comparison and investigate the effect of the truss-beam configuration, loading-displacement curves were converted to the moment-rotation curves. Figure 15a-d shows a comparison of moment-rotation curves of specimens with \"V\"- and \"\(\wedge\)\"-shaped brace arrangements of the truss beams. The light-steel beam-column specimen has a higher load-bearing capacity and a lower global deflection of the truss beam when the brace members are arranged in a \"V\" shape, such as in specimen JU60-1.  
Figure 14: Effect of the column sleeve tube on the proposed beam–column connection. **(a)** JU60\(-\)1 and JU60\(-\)2, **(b)** JU60\(-\)3 and JU60\(-\)4, **(c)** JU100\(-\)1 and JU100\(-\)2, **(d)** JU100\(-\)3 and JU100\(-\)4.
## 6 Conclusions
### Research Limitations  
Although the outstanding performance of the proposed SSRDC system leaves state-of-the-art techniques behind, it certainly has some limitations also. The first point of concern is the correct recognition of complex locomotion activities that are a combination of two or more individual locomotion activities, and as our system deals with remote data, there can be some cases when data are lost during the wireless communication and the SSRDC system might not recognize the activity.
# An Efficient SS-MAC Protocol for IEEE 802.15.4-Based WSNs of Cluster Tree Topology
## 3 The Proposed SS-MAC Protocol
### Setup Phase (SP)
#### 3.2.2 Control Period (CP)  
Each CH calculates its channel according to the identification number (ID) allocated in RCP, and informs all member nodes through CS_AOC. From CP, communication between CH and its member nodes is performed on this channel. This multi-channel mechanism avoids mutual interference when multiple CHs and their member nodes communicate simultaneously.  
In CP, each member node sends a data slot request (DS_REQ) message to its CH in its control slot in the order specified by the CS_AOC message, as illustrated in Figure 4. A DS_REQ message is used to report the number of slots required to send data stored in the buffer. The protocol specifies that only one data frame is transmitted per data slot, so the number of slots is related to the payload size of the data frame. Assuming that the payload of each data frame is \(P_{load}^{SN}\) bytes, node \(i\) has \(M\) bytes of data to be transmitted, the number of slots required by node \(i\) is \(N_{slot}(i)=Int[M/P_{load}^{SN}]^{+}\), where \(Int[X]^{+}\) represents the smallest integer greater than or equal to \(X\).  
Figure 4: SS-MAC data collection process.  
Figure 3: Structure of CS_AOC message.
###### Abstract  
The indisputable wide use of the Eddy Dissipation Concept (EDC) implies that the resulting mean reaction rate is reasonably well modeled. To model turbulent combustions, an amount of EDC constants that differ from the original values was proposed. However, most of them were used without following the nature of the model or considering the effects of the modification. Starting with the energy cascade and the EDC models, the exact original primary and secondary constants are deduced in detail in this work. The mean reaction rate is then formulated from the primary constants or the secondary constants. Based on the physical meaning of fine structures, the limits of the EDC constants are presented and can be used to direct the EDC constant modifications. The effects of the secondary constant on the mean reaction rate are presented and the limiting turbulence Reynolds number used for the validity of EDC is discussed. To show the effects of the constants of the EDC model on the mean reaction rate, 20 combinations of the primary constants are used to simulate a laboratory-scale turbulent jet flame, i.e., Sandia Flame D. After a thorough and careful comparison with experiments, case 8, with a secondary constant of 6 and primary constants of 0.1357 and 0.11, can aptly reproduce this flame, except for in the over-predicted mean OH mass fraction.  
energy cascade model; Eddy Dissipation Concept (EDC); Sandia Flame D; turbulence Reynolds number; mean reaction rate; Reynolds Stress Model (RSM)
# Situation Assessment of Air Traffic Based on Complex Network Theory and Ensemble Learning
## 4 Experimental Setup and Details
### Ensemble Learning Model  
The training dataset needed to be standardized. Then, it was input into four base classifiers.  
(a) K-NN model  
We adopt 10-fold cross validation. After model parameters were optimized by the grid search method, the average classification accuracy score was 0.8257. The number of positive and negative samples in the five adjacent samples around each sample were output, as shown in Table 5. The _KNeighborsClassifier()_ in sklearn was called to build this model. And _kneighbors()_ was used to calculate adjacent to each sample.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline _Data_ & _NS_ & _NB_ & _EB_ & _ND_ & _NCC_ & _NE_ & _NLD_ & _NI_ & _NPR_ & _CLASS_ \\ \hline
1 January 2019 & 2.0394 & 0.0882 & 0.0678 & 1.3277 & 0.1493 & 0.4829 & 0.0382 & 0.5234 & 0.0484 & 1.0000 \\
4 January 2019 & 2.0945 & 0.0702 & 0.0478 & 1.2428 & 0.1593 & 0.5942 & 0.0399 & 0.4694 & 0.0572 & 1.0000 \\
17 January 2019 & 1.9049 & 0.0682 & 0.0434 & 1.4024 & 0.1481 & 0.3832 & 0.0418 & 0.4213 & 0.0609 & 1.0000 \\
30 January 2019 & 2.3844 & 0.0723 & 0.0522 & 1.3521 & 0.1595 & 0.3287 & 0.0413 & 0.4382 & 0.0689 & 2.0000 \\
2 February 2019 & 2.1837 & 0.0912 & 0.0528 & 1.2047 & 0.1466 & 0.5321 & 0.0523 & 0.3694 & 0.0523 & 1.0000 \\ \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) \\
9 December 2019 & 1.9489 & 0.1024 & 0.0346 & 1.4185 & 0.1503 & 0.5343 & 0.0619 & 0.4398 & 0.0442 & 1.0000 \\
16 December 2019 & 2.0874 & 0.0507 & 0.0571 & 1.5105 & 0.1494 & 0.5034 & 0.0497 & 0.3957 & 0.0531 & 1.0000 \\
29 December 2019 & 2.2049 & 0.0973 & 0.0519 & 1.4454 & 0.1446 & 0.4875 & 0.065 & 0.4529 & 0.0789 & 2.0000 \\ \hline \end{tabular}
\end{table}
Table 4: The air traffic network situation dataset in East China.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Waypoints** & **WYK\_VOR** & **DOGAR** & **LADIX** & **CG** & **KALBA** & \(\cdots\) & **PIMOL** & **LAMEN** \\ \hline WYK\_VOR & 0 & 0.502812 & 0 & 0 & 0 & \(\cdots\) & 0 & 0 \\ DOGAR & 0.502812 & 0 & 0.536808 & 0 & 0 & \(\cdots\) & 0 & 0 \\ LADIX & 0 & 0.536808 & 0 & 0.592898 & 0 & \(\cdots\) & 0 & 0 \\ CG & 0 & 0 & 0.592898 & 0 & 0.710775 & \(\cdots\) & 0 & 0 \\ KALBA & 0 & 0 & 0 & 0.710775 & 0 & \(\cdots\) & 0 & 0 \\ \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) \\ PIMOL & 0 & 0 & 0 & 0 & 0 & \(\cdots\) & 0 & 0 \\ LAMEN & 0 & 0 & 0 & 0 & 0 & \(\cdots\) & 0 & 0 \\ \hline \end{tabular}
\end{table}
Table 3: The weighted network adjacency matrix of specified date.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **Positive** & **Negative** \\ \hline
1 & 4 & 1 \\
2 & 3 & 2 \\
3 & 4 & 1 \\
4 & 0 & 5 \\
5 & 4 & 1 \\
189 & 3 & 2 \\
190 & 3 & 2 \\
191 & 5 & 0 \\ \hline \end{tabular}
\end{table}
Table 5: The number of positive and negative samples.
# Feasibility Study on Grouting Compactness Detection in Sleeves Using Piezoelectric Transducers
## 4 Test Results and Discussion
### Grouting Compactness Detection Based on WPE Method  
For WPE, both the wavelet basis and decomposition layer may affect the detection result [32]. The results obtained by using different wavelet bases for the same signal are often quite different. In addition, the number of decomposition layers is too small and the denoising effect is not ideal, while too large decomposition layers will lead to the increase of computation and serious information loss. In this paper, since wavelet basis sym8 has the characteristics of compact support set, good continuity, and symmetry, sym8 is used to identify grouting compactness by trials. In terms of the decomposition layer, when the decomposition layer was set as five, the fifth layer signal had serious information loss. Therefore, in sym8, the decomposition layer was set as four.
# Intermodular Mandibular Distraction Osteogenesis--A Preliminary Report
## 3 Results  
Like any distraction treatment, IMDO is a dynamic procedure that requires regular radiological and clinical monitoring (Figures 3-5).  
Figure 4: Lateral skull view (**a**) before and (**b**) after treatment with IMDO.  
Figure 3: Radiological follow-up. Panoramic X-rays: (**a**) Before IMDO. Note the small diastema between the first and second molars of the mandible. (**b**) Control after distractor placement. Note the symmetrical axis tilt of the distractors. (**c**) End of active distraction treatment and removal of activation rods for more patient comfort. (**d**) Bone consolidation 10 weeks after end of distractor activation. (**e**) After removal of the distractors.  
Figure 5: Clinical follow-up. Photographic documentation of two patients: (**a**) 12-year old patient with class II/2 occlusion before and after end of combined orthodontic and IMDO treatment. (**b**) 12-year old patient with class II/1 occlusion and traumatic deep bite before and after end of combined orthodontic and IMDO treatment.  
Detailed case data descriptions are provided in Table 1. In total, 20 patients (11 male and 9 female) with a mean age of 14.8 (SD = 0.9) years were included. In all cases, the combined orthodontic-surgical treatment was successfully completed. Patients had little facial swelling postoperatively and pain medication was needed for only one week in most patients. An average gain in horizontal mandibular length of 9.6 mm (SD = 3.7 mm) could be achieved. In one patient, premature bone maturation was seen in the distraction gap, forcing re-osteotomy.  
All patients achieved a Class I occlusion, which remained stable until the data collection of this publication. Infections that would have led to premature distractor removal were not observed. However, in one patient an abscess in the region of the activation rod had to be treated by evacuation. No functional impairment of the sensitive function of the third branch of the trigeminal nerve was noted. In nine patients, fractures of the mesial root of the second molar occurred at different levels during osteotomy (Figure 6). Three second molars had to be removed, one molar with root fracture was treated endodontically. Resorptions of the fractured root fragments were seen during follow-up.  
The average time between insertion and removal of the distractors lasted 4.6 months (SD = 1.5 mths). One distractor side showed premature consolidation before the distraction was completed. During distractor removal the jaw position was corrected surgically.  
The removal of the distractors after the consolidation period was without complications.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Patient Nr.** & **Sex** & **Age (Year)** & \begin{tabular}{c} **Period Distractors** \\ **In Situ (Months)** \\ \end{tabular} & \
\begin{tabular}{c} **Distraction Left** \\ **Side (mm)** \\ \end{tabular} &
\begin{tabular}{c} **Distraction Right** \\ **Side (mm)** \\ \end{tabular} \\ \hline
1 & M & 13.9 & 4.2 & 12.4 & 9.9 \\
2 & F & 13.9 & 4.7 & 10.6 & 6.7 \\
3 & F & 14.3 & 4.9 & 16.9 & 18.5 \\
4 & F & 14.5 & 2.5 & 17.6 & 11.5
# Dynamic Information Flow Tracking: Taxonomy, Challenges, and Opportunities
## 6 Discussion and Prospect
### SoC-Level DIFT Design  
Most DIFT designs focus on protecting the target software running on the processor. Only a few methods have been proposed for SoC-level DIFT designs to protect the information flow between third party IPs (3 PIPs). The current DIFT design can provide protection for information flow between the processor and 3 PIPs because most IPs can be accessed in the form of memory mapped I/O (MMIO) registers. The security check can be enforced on the corresponding memory regions. However, the information flow between IPs is also an important part in any SoC environment. The 3 PIPs play an important role in data computation acceleration and some security-critical functions, e.g., encryption and hashing.  
The authors in [20] extended the bus to support extra tag transmission, although this DIFT design may not be deployed in real-world systems. A possible solution is to attach one DIFT-oriented IP to the bus. The IP is responsible for monitoring the information flow between IPs and provide a summary to the DIFT engine in the processor or coprocessor for further processing. In addition, an extra bus topology can be added to support the tag transmission at runtime. The extra bus topology can be used for propagating the tag about information flow between processor and IP. The tag propagation between IPs can also leverage this new bus topology. The DIFT engine in the processor and coprocessor can serve as a management unit for information flow in the whole SoC.
# Post-Quantum Key Exchange and Subscriber Identity Encryption in 5G Using ML-KEM (Kyber) 1
## 4 Security and Cryptanalysis on PQC KEM: How Kyber Was Selected  
In this section, we focus on the security of NIST PQC. To evaluate the security of candidate algorithms, NIST defined three security definitions--two for encryption (for general-use and ephemeral cases) and one for digital signatures as discussed in [24]--and categorized them into five security levels (they require resources at least equivalent to breaking AES with 128/192/256-bit keys or finding collisions in SHA-256/384 hash functions) based on how hard it would be for an attacker to break them. NIST alsohighlighted other important security features, like forward secrecy, protection against side-channel and multi-key attacks, and resistance to misuse, which remain important.  
NIST selected Kyber as the standardized KEM cipher in 2022 [25], informed by the cryptanalysis conducted by the cryptographers and security researchers around the world. Section 4.2 describes the cryptanalysis, much of which is based on side-channel vulnerabilities. Informed by them, Kyber improved and advanced its security and design from 2017 [26] to 2019 [27] to 2021 [28].  
The first and second rounds focused more on performance and hardware compatibility, while the third round informed the security strength and cryptanalysis, described in Section 4.2. Saber [29], another KEM cipher candidate from the third round, is based on a lattice-based problem like Kyber, but the math problem is based on modular learning with rounding or MLWR. However, because MLWR is less analyzed from the attacker perspective (i.e., fewer cryptanalysis studies), NIST selected SABER.