esa-sceva/satcom-chunk-collection huggingface dataset:
# 6G to Take the Digital Divide by Storm: Key Technologies and Trends to Bridge the Gap
## 3 The Technologies for Bridging the Service-Delivery Divide
### Exploiting Higher Frequencies  
To truly bridge the digital divide, ultra-high-speed communications required to deliver 6G services need to be enabled. A very promising solution in this direction is to exploit new portions of the spectrum [14]. While the millimeter-wave band has been already considered in 5G mobile networks, now the interest is moving towards TeraHertz and even optical bands, motivated by the very recent advances in electronics and photonics that enabled the manufacturing of portable equipment operating at such frequencies. However, signal propagation becomes critical due to severe path loss, high molecular absorption, and the requirement for very precise antenna pointing. Coverage extension may be achieved by means of Intelligent Reflecting Surfaces (IRS) [15] that allow us to realize a virtual line-of-sight (LoS) link by smartly reconfiguring the wireless propagation environment [16]. Mainly, IRSs exploit massive low-cost passive reflecting elements integrated on a planar surface that independently reflect the incident signal by controlling its amplitude and/or phase and thus collaboratively achieve fine-grained three-dimensional (3D) passive beamforming for directional signal enhancement. The service-delivery divide could benefit from the use of higher frequencies in application scenarios that span from indoor coverage to Earth-to-ground communications.
## 5 Results and Discussion
### Spatial Interaction Between HDT Behaviors and Clustered Freight Zones
#### 5.4.7 Car Carrier (CC)  
Figure 15 shows a systematic analysis of the trip patterns of car carriers. As specialized carriers for trunk transportation of commercial vehicles, car carriers exhibit a striking spatial pattern of automotive industry cluster agglomeration and supply chain node linkage, serving as a profound reflection of Shanghai's global automotive industry layout and localized organizational logic.  
The Sankey diagram shows that Cluster 1 (37.81%) and Cluster 2 (50.37%) account for over 88% of CC trips, with 79% of flows occurring within Clusters 1 and 2, between nodes like the SAIC Jiading Commercial Vehicle Storage Yard and Waigaoqiao Ro-Ro Terminal. This serves as direct evidence of these zones' roles as core automotive manufacturing bases and international shipping hubs. Spatial heatmaps reveal that CC trip ends concentrate densely around vehicle factory-associated parking lots, Ro-Ro terminals, and railway freight stations, rather than directly at manufacturing plants. This aligns with the workflow for commercial vehicles: newly produced vehicles are driven short distances to nearby specialized parking lots after rolling off the production line of plants, and car carriers assume long-haul transport to minimize damage and mileage accumulation during transit. Trips within Cluster 4 (5.58%) and Cluster 6 (5.03%) likely relate to transfers between  
Figure 15: Systematic analysis of trip patterns of car carriers. The upper two subplots depict heatmaps of trip ends and trips, respectively. The Sankey diagram in the lower-left corner illustrates the proportions of CC trips across clustered functional zones, while the ID entropy heatmap in the lower-right corner visualizes entropy values in grid cells with high trip volumes.  
large-scale parking lots and smaller facilities, quality inspection stations, or end-user sales networks.  
The concentration metrics (\(HHI_{E}\) = 0.407, which is the third highest; \(HHI_{R}\) = 0.1, which is the second highest) highlight high node agglomeration and structured route patterns. The intense spatial focus on the specialized infrastructure of automotive industry underscores the strong reliance of CC on automotive manufacturing and logistics nodes. And trip patterns dominated by fixed node pairs (e.g., parking lot-terminal or railway station and large parking lot-small parking lot) reflect the standardized operational models of automotive supply chain dedicated transport. The ID entropy analysis in high-trip grids reveals a stark dichotomy: the low ID entropy in OEM-exclusive parking lots stems from the end-to-end control of corporate logistics arms (e.g., Anji Logistics of SAIC), whereas the high ID entropy in public terminals and third-party parking areas indicates broad participation by market-driven capacity providers.
## 3 Linear Streaking Measurements at Free-Electron Lasers
### Experimental implementation
THz fields generated in the DAST crystal have steeper half-cycle rise-times of ~180 fs, which in principle can lead to higher resolution measurements. At LCLS measurements on 1.1 keV soft X-rays were made using THz pulses generated in DAST. High field strengths approaching 1 MV/cm were observed in the interaction region. However, these measurements suffered from timing jitter due to the reduced dynamic range. The timing jitter between XFEL and THz was measured at ~200 fs rms. Therefore, an additional method for sorting the arrival time of the X-ray pulses with respect to the THz pulses was required to isolate shots that arrived on the single-valued THz streaking ramp [107]. Three streaking spectrograms collected as the relative delay between DAST-THz pulse and X-ray pulse was smoothly varied are shown in Figure 21. The panel on the left shows the single-shot measurements in the order as they were collected and the panel in the center shows the single-shot spectra re-ordered according to the additional timing information from the spectral encoding measurements. In the sorted spectrogram, the shots arriving on the single-valued streaking ramp are clearly identified, allowing for calibrated transformation from streaked kinetic energy to time. The panel on the right shows the measured spectrogram after rotating the DAST crystal by 180 degrees to reverse the sign of the streaking field. In principle, as in attosecond spectroscopy and discussed  
Figure 20: Arrival time and jitter. Panel (a), false color plot of 50 consecutive single-shot measurements recorded close to time zero. The center of mass of the individual photoelectron spectra (black dots) is used to determine the arrival time of each pulse. Panel (b) shows the distribution of arrival times collected over ~450 consecutive shots with a Gaussian fit. The corresponding width of the fit is ~87 fs rms, which is a measure of the short-term timing jitter between the pump–probe laser that generates the THz, and the FEL (reproduced with permission from [103], Copyright Nature Publishing Group, 2012). earlier, this provides access to the average spectral phase, or chirp, of the X-ray pulse in addition to the temporal profile. The streaking power \(s\) (Equation (7)) in the DAST measurements was \(\sim\)1.7 fs/eV. With 1% relative energy resolution in the measured photoelectron spectrum, these fields have the potential to be used to achieve \(\sim\)5 fs resolution in the X-ray profile measurements. For higher time resolution, to measure pulses compressed to several femtoseconds in duration and shorter to the sub-femtosecond, attosecond regime the wavelength of the streaking fields needs to be decreased further. In attosecond physics, \(\sim\)10 attosecond resolution is routinely achieved using near infrared streaking fields. However, these fields provide dynamic range of only \(\sim\)1.5 fs and are only suitable for attosecond pulse characterization.
## 2 Methodology
### System Architecture  
A proof-of-concept prototype TCI based on the magnetic tracking method was developed. Figure 1 gives the system architecture of the proposed TCI system. Rather than placing the whole sensing system inside the narrow mouth, a combined \(T\)-type PM is employed as a tongue motion marker (two iron cylinders are utilized for illustration in Figure 1, and in a human study, a specialized polydimethylsiloxane (PDMS) mold, where the \(T\)-type PM is placed, will be required and attached to the tongue surface with tissue adhesives for safety). A sensor array of eight three-axis anisotropic magnetoresistive (AMR) sensors is fixed on a holder produced with 3D printing technology and worn under the chin in the same way as a mask. The magnetic fields derived from the \(T\)-type PM are measured by the sensor array. The built-in 12-bit ADC of the AMR sensor (Honeywell, Plymouth, MN, USA) sends out digital signals induced by the \(T\)-type PM, and transfers them to a personal computer (PC) (Dell, Round Rock, TX, USA) through an I\({}^{2}\)C-USB adapter (Viewtool, Shenzhen, China). A custom-made GUI based on Labview (NI, Austin, TX, USA) and MATLAB (MathWorks, Natick, MA, USA) is loaded onto a PC, which presents and processes the real-time measurements of sensors, figuring out the trajectory of the marked tongue position.  
Figure 1: _Cont._
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 6 Analysis of Security Functionalities of ECSSP-SC
### Analysis of Security Attributes of ECSSP-SC
#### 6.1.7 Non-Repudiation  
The components \(c,\ T,\) and \(w\) of the message sent by \(USR\) depend upon the key \(K\), which further depends on the private \(v\) and \(PWD\). Similarly, the component \(A_{S}\) in the message sent by the \(USR\) also depends on the key \(K^{\prime}\), which in turn, depends on the secret \(K_{RS}\). Therefore, if \(T^{\prime}=T,\ USR\) cannot repudiate that it transmitted \(\{c,T,w,\ T_{U}\}\) and similarly, if \(A_{S}=A_{S}{}^{\prime}\), the SVR cannot repudiate that it has transmitted the message \(\{A_{S}.T_{S}\}\), since the identity \(I_{S}\) of the server is kept secret. Therefore, ECSSP-SC successfully satisfies the non-repudiation attribute as well.
# A Matrix-Based Proactive Data Relay Algorithm for Large Distributed Sensor Networks
## 3 Problem Description
### Energy Consumption on Communication  
In a deployed sensor network, the sensor nodes are usually battery powered [29], and they have to operate on an extremely frugal energy budget. Since communication is the major source of energy consumption in sensor networks, to prolong the lifetime of the sensor network requires a careful consideration of the energy cost in each transmission.  
For each sensor \(v_{i}\), the energy cost on communication is mainly composed of two parts: the energy of broadcasting data \(d_{j}\): \(Eb(v_{i},d_{j})\), and the energy of receiving data \(d_{j}\): \(Er(v_{i},d_{j})\). They can be computed as follows [24]:  
\[Eb(v_{i},d_{j}) =(E_{elec}(v_{i})+E_{amp}(v_{i}))\times d_{j}.length \tag{7}\] \[Er(v_{i},d_{j}) =E_{elec}(v_{i})\times d_{j}.length\]where \(E_{elc}(v_{i})\) is the energy consumed by \(v_{i}\)'s transmit electronics or receive electronics for digital coding, modulation and filtering of the signal, \(E_{amp}(v_{i})\) is the energy consumed by its TX amplifier, and \(d_{j}length\) (bits) is the length of data pieces. Suppose that the sensors are homogenous, and the size of data is identical. The energy cost of broadcasting and receiving a piece of data can be substituted by two constants \(Eb\) and \(Er\) respectively. The energy consumption on transmitting a piece of data is the sum of energy on broadcasting and all neighbors' receiving:  
\[Energy(v_{i},d_{j})=Eb+Er\times\sum_{v_{j}\in N(v_{i})}Pr(v_{i},v_{j}) \tag{8}\]
# Using Blockchain to Protect 3D Printing from Unauthorized Model Tampering
## 2 Design and Analysis
### \"Fingerprints\" of an STL File  
The data stored on the blockchain will be saved by nodes, and multiple nodes will have multiple backups. In this way, the blockchain ensures the reliability and security of data. However, 3D models are usually larger than tens of megabytes. If multiple backups are stored in the blockchain, storage resources will be consumed fast. To solve this storage problem, we use hash function to obtain the \"fingerprints\" of 3D models and put these \"fingerprints\" on a blockchain. The term hash function refers to a function that compresses an input bit string of arbitrary length into an output bit string of fixed and finite length [29]. These functions are mainly used to speed up the process of finding stored data. Various files of different sizes, such as videos, email text data, and pictures, can be converted into fixed lengths. The hash function also has unidirectional and anti-collision properties [30]. According to these characteristics, the hash is usually used to ensure data integrity, that is, to verify whether the data are illegally tampered with. Therefore, even if the original data are in an unsafe environment, the integrity of those data can be detected based on their hashes [31]. The hash function can be written as y = Hash(data), where y is always a fixed-length output. In our case, a hash function is to generate \"digital fingerprints\" for STL files [32]. If one byte of data is modified in an STL file, completely different hashes will be generated [33]. The 3D model hashing and storage process is shown in Figure 3. In a typical process, the model designer uploads his new design to the system (for convenience, we created a 3D design of a cup named Model-CUP in Figure 3), and the system performs hash calculation and generates the \"digital fingerprints\" [34]. Later, one operator downloads the Model-CUP from the internet and plans to print it. A 3D model transferred via the internet might not be secured, so the downloaded model was called Model-CUP'. The model file is transmitted over the internet, but its hashes are safely stored on the blockchain. Before printing, an authorized printer which is a node in the blockchain needs to conduct hashes verification and only print these files whose hashes are on the model whitelist. A whitelist is a group of smart contacts on the blockchain [35]. This list is maintained by authorized
# An Energy-Efficient and Secure Routing Protocol for Intrusion Avoidance in IoT-Based WSN
## 3 Proposed Intrusion Avoidance Protocol  
This section summarizes the proposed energy-efficient and secure routing (ESR) protocol for IoT based WSN. The basic structure of a cluster based network is illustrated in Figure 1. The proposed ESR protocol divides the overall its functionality into two main components that are discussed in the next sections. In the first component, ESR protocol organizes optimum hierarchical topology construction and the thresholding based secret sharing scheme (SSS) [45] for secure data routing. Based on multiple criteria along with QoS constraints, the optimized cluster heads are determined in association to the distributed clusters in energy efficient and balanced manner. Furthermore, the proposed clustering scheme improves network lifetime with low overhead and power consumption ratio between the sensor nodes. In the second component, the secure and trustworthy routing path is constructed between cluster heads and BS to avoid any intrusions caused by malicious nodes. To achieve reliable data forwarding, the BS generates a secret key, which is shared among selected cluster heads. In data forwarding from cluster heads, the data packets are encrypted using the SSS mechanism. On the other hand, BS reconstructs the inbound data packets from cluster heads using the proposed secret sharing scheme. Furthermore, the relative impact of each factor in an optimized selection process of the cluster head is evaluated based on a sensitivity analysis, which is a mathematical model that provides an understanding of the affiliation among input and output values in development. Simulation results of ESR outperform other relevant schemes with respect to packet delivery ratio, network lifetime, end-to-end delay, communication cost, number of route discoveries and network overhead. Figure 2 illustrates the block diagram of the ESR protocol.  
Figure 1: Structure of the cluster based network.
# Safety Assessment of Gas Pipelines Crossing River through Hydrodynamic Analysis
## 2 Materials and Methods
### Composition of Input Data
#### 2.4.2 Boundary Conditions for Long-Term Riverbed Change Analysis  
The topographical data constructed for measuring bed changes were prepared by reflecting the survey data from the River Basic Plan Report [43] and the survey results from the field investigation at the location where the gas pipeline is buried. When applying the HEC-RAS model for one-dimensional analysis, appropriate boundary conditions must be input to analyze the water level and flow velocity of the river. Generally, in the analysis of a steady non-uniform flow in rivers, the flow rate is input as the upstream boundary condition, and the water depth is input as the downstream boundary condition. Long-term bed changes were simulated under normal flow conditions over a long period, so boundary conditions that can be used to derive the water level and flow velocity during normal flow periods were reviewed. The upstream boundary condition of the model, the flow rate, was calculated based on the flow duration analysis for drought flow, low flow, ordinary flow, and abundant flow, and was applied by analyzing the monthly runoff data for the past 30 years from the River Basic Plan Report for the Hyunpung Stream [43]. In the River Basic Plan Report, when the observed flow data of the flood are valid, the flow with a specific return period is considered as the dominant discharge, which is calculated as 6.0 m\({}^{3}\)/s, as shown in Figure 4. The downstream boundary condition of HEC-RAS can be assigned as one of the critical depth, normal depth, or stage-discharge relationships. In this study, the normal depth was adopted as the downstream boundary condition. The roughness coefficient in rivers represents the degree of channel resistance, and it varies due to various complex factors such as changes in water depth, bed shape, bed changes, sediment load, type of vegetation, artificial bed excavation, and flow diversion between channels. To obtain a highly accurate roughness coefficient, it is necessary to observe and verify it continuously. The roughness coefficient was determined by reviewing the methods for estimating the roughness coefficient based on the presence of water level data and the  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Discharge**} & **Upstream of the** & **Downstream of the** & **Hyunpung Water** & \multirow{2}{*}{**Ackers and White** **(ton/day)**} \\  & **Simulated Area** & **Simulated Area** & **Level Gauge** & \\  & **(m\({}^{3}\)/s)** & **(m\({}^{3}\)/s)** & **(m\({}^{3}\)/s)** & **(ton/day)** \\ \hline Abundant flow (Q95) & 0.20 & 0.41 & 175.77 & 8.21 \\ Ordinary flow (Q185) & 0.095 & 0.19 & 81.24 & 1.97 \\ Low flow (Q275) & 0.065 & 0.13 & 56.73 & 1.24 \\ Drought flow (Q355) & 0.045 & 0.09 & 38.72 & 0.201 \\ \hline \end{tabular}
\end{table}
Table 2: Flow duration analysis and sediment load results of the study area.  
previously established values for the main river and tributaries. and adopting the value of 0.030 deemed appropriate for the study area in the River Basic Plan Report [43].
## 3 Results and Discussion
### Damping Properties  
Figure 9 shows cement specimen loss tangents at 0.1-20 Hz. The loss tangent increased for all specimens as the frequency increased. Loss tangents were higher for specimens with added flax fiber as compared to C0, and increasing the fiber content resulted in a greater improvement in the loss tangent, indicating that the addition of flax fiber improved cement damping properties. F0.5 and F1 improved the loss tangent by 29.17% and 45.83%, respectively, relative to C0 at 20 Hz, and MF0.5 and MF1 improved the loss tangent by 25% and 37.5%, respectively, relative to C0 at the same frequency. The loss tangent of alkali-modified flax fiber reinforced cement pastes was lower as compared to that of raw flax fiber reinforced cement pastes. In other words, alkali treatment decreased the damping properties of flax fiber in the cement composites.  
Figure 8: Flexural strength of cement specimens for different curing ages.  
Figure 7: SEM image of F1 showing microfractures at 500 \(\times\).  
Improved damping by the addition of flax fibers could be attributed to the following:  
1. The components of flax fiber include a type of polymer possessing viscoelastic properties; the incorporation of viscoelastic material in cement could improve its damping properties.
2. A single flax fiber is normally comprised of a bundle of elementary fibers and each elementary fiber is made up of a fiber cell wall layer (S1, S2, and S3; Figures 10 and 11). The S2 layer consists of numerous cellulose microfibril that contribute to the mechanical properties of flax fibers. Vibration damping in flax fibers is attributed to the friction between elementary fibers and between adjacent fiber cell walls in fiber bundles [19]. Moreover, the flax fiber produces a relative displacement within the cementitious matrix, causing sliding friction, which dissipates energy under external loads.
3. In the S2 layer system, the cellulose, pectin, and lignocellulosic form a sandwich structure. During loading transfer processes, the complete system produces a constrained damping in the untreated flax fiber reinforced cement composite. However, alkali treatment removes the pectin and lignocellulosic from the flax fiber. This removal is the likely reason why the damping properties of alkali-modified flax fibers are lower as compared to those of raw flax fibers.
4. The lumen of the elementary flax fiber may also contribute to its damping properties [20].  
Figure 10: SEM image of a flax fiber fracture surface at about 2000 \(\times\).  
Figure 9: Loss tangent of the cement specimens for different frequencies.
## 2 Patients and Methods
### Study Population  
We included adult patients with sepsis or APN combined with ureteral calculi who were at least 18 years old from 2003 to 2017. We used the 9 th and 10 th revisions of the International Classification of Diseases, 9 th and 10 th revision, Clinical Modification (ICD-9-CM and ICD-10-CM) and CPT codes to identify the study cohort and comorbidities.  
Ureteral calculi were classified into calculus of the ureter (ICD-9: 592.1, ICD-10: N20.1) and calculus of the kidney with calculus of the ureter (ICD-9: 592.1, ICD-10: N20.2). Severe infections were classified as APN (ICD-9: 590.10, 590.80, ICD-10: N10) and sepsis (ICD-9: 038.40, 038.42, 038.9, 790.7, 995.91, 995.92, ICD-10: A41.9).  
Demographic factors and comorbidities: ischemic heart diseases (ICD-9: 410-414, ICD-10: I20-I25), hypertension (ICD-9: 401, ICD-10: I10-I13), diabetes mellitus (ICD-9: 249, 250, ICD-10: E08-E13), stroke (ICD-9: 430-438, ICD-10: I60, I61, I62, I63, I65, I66, I67, I68, and I69), [PERSON]'s disease (ICD-9: 331.0, ICD-10: G30), chronic kidney disease (ICD-9: 585.3, 585.4, 585.5, 585.6, 585.9, ICD-10: N18, N19), chronic respiratory diseases (ICD-9: 490-496, 510, ICD-10: J40-J47), overweightness and obesity (ICD-9: 278, ICD-10: E66), history of UTI (ICD-9: 590, 595, ICD-10: N10 and N30), history of urinary stone (ICD-9: 592, 594, ICD-10: N20-N22), insurance type (EPO, HMO, POS, PPO, Indemnity, other), surgical treatment type, and cost data (total treatment cost for urinary stone without sepsis or APN vs. urinary stone with sepsis or APN), were analyzed. Surgical decompression, such as retrograde double J stent insertion (CPT: 52332, 52005) and percutaneous nephrostomy (CPT: 50430, 50431, 50432, 50433, 50434, 50435, 50693, 50694, 50695), were analyzed.  
We excluded patients with the following medical comorbidities or past surgical history from this study: kidney transplant status (ICD-9: V42.0, ICD-10: Z94.0), congenital malformations of the urinary system (ICD-9: 753.0, 753.3, 753.4, 753.9, ICD-10: Q60, Q62, Q63, and Q64), neuromuscular dysfunction of the bladder (ICD-9: 596.54, ICD-10: N31.2, N31.9), pregnancy (ICD9: V22, V23, V28, 630-679, 760-779, ICD-10: O00-O9A, P00-P96, Z32, Z33, Z34, Z36, Z3A), and majorrologic surgery--nephroureterectomy (CPT: 50548), radical nephrectomy (CPT: 50545), partial nephrectomy (CPT: 50543), radical prostatectomy (CPT: 55866), and radical cystectomy (CPT: 51570, 51575, 51580, 51585, 51590, 51595, 51596, and 51999).