esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Data
### Human/System Summary Comparison  
We present here an example of a single data set from the TAC 2011 data. The set contains 10 documents; summaries were generated by 4 humans and 43 systems. There was 1 baseline. This data set was chosen specifically because it strongly demonstrates the problems of correlation between ROUGE and human evaluation. Certainly, not all data sets exhibit such wide discrepancies, but in general there is a consistent lack of correlation between the two.  
Table 1 shows the pyramid score, overall responsiveness score, linguistic score, and ROUGE-2 score for the human summary with the highest pyramid score and the five system summaries with the highest ROUGE scores. Note that the human's ROUGE score ranges from almost half to just 40% of the system ROUGE scores, while the system pyramid scores range from barely 14% to just 26% of the human pyramid score. Clearly, in this case, ROUGE-2, which has been shown in the past to have the highest correlation with human evaluation, is lacking.  
Figure 5 shows the human generated summary and the summary for system 26 which had the best pyramid score of the five systems with the highest ROUGE-2 scores. Note that the overall responsiveness and linguistic scores are 5, the highest possible, for the human summary while system 26 has a score of 3, the best of the five systems but clearly below the human scores. Both overall responsiveness and linguistic quality include cohesiveness, coherence, readability, and redundancy, along with more obvious grammatical issues such as run-on sentences, sentence fragments, bad punctuation and/or capitalization, lack of noun or pronoun referents, _etc_. While the system summary reads grammatically correctly, it suffers from poor ordering of the sentences that impacts the summary flow, a lack of specification for the name \"Wasser\", and the final sentence fragment.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Summary** & **Pyramid** & **Overall** & **Linguistic** & **ROUGE-2** \\
**ID** & **Score** & **Responsiveness** & **Quality** & **ROUGE-2** \\ \hline human C & 0.905 & 5 & 5 & 0.0556 \\ system 3 & 0.123 & 3 & 2 & 0.1370 \\ system 26 & 0.238 & 3 & 3 & 0.1369 \\ system 35 & 0.153 & 2 & 2 & 0.1191 \\ system 13 & 0.182 & 3 & 2 & 0.1135 \\ system 10 & 0.209 & 2 & 3 & 0.1059 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Contrast between human and system scores for data set D1113C.
# Self-Organized Fast Routing Protocol for Radial Underwater Networks
## 2 Related Work
### Location Free Protocols  
The Network Layer Protocol for UANs (NLPU) [16] is a self-configured, proactive protocol based on the concept of full-duplex channel utilization. The gateway is the master node, manages the topology of the network and establishes the routes by means of a topology discovery probe. The probe assigns channel codes to each node it travels through along the network. Conflict remains possible if the nodes select the same channel. This issue is resolved by the common parent node. Once the probe completely tours the network, a completion notice is sent back to the gateway. The protocol heavily relies on full-duplex communication.  
The Location Free Link State Routing (LFLSR) [17] protocol addresses the problem of communication voids. Every node selects the next hop based on three metrics, namely hop count, path quality, and depth. A beacon message is broadcast from the sink node and forwarded along the network. This message is used to update the information of the paths. This protocol also requires a pressure measurement device, because the beacon message is forwarded to the neighbor closest to the surface for paths with similar quality.
## 3 Key Variables in the Analytical Approximation Model  
For a given true anomaly \(\
u\), the distance between the satellite and the Earth's center is  
\[r=\frac{a\left(1-e^{2}\right)}{1+e\cos\
u}=\frac{p}{1+e\cos\
u} \tag{11}\]  
where \(p=a\left(1-e^{2}\right)\) in Equation (11) is the semi-latus rectum of the elliptic orbit. The position, velocity, and acceleration vectors of the satellite in the orbital plane (OP) are \[\mathbf{P}_{sat,OP} = r\begin{bmatrix}\cos\
u\\ \sin\
u\\ 0\end{bmatrix} \tag{12}\] \[\mathbf{V}_{sat,OP} = \sqrt{\frac{\mu}{p}}\begin{bmatrix}-\sin\
u\\ e+\cos\
u\\ 0\end{bmatrix}\] (13) \[\mathbf{A}_{sat,OP} = -\frac{\mu\left(1+e\cos\
u\right)^{2}}{p^{2}}\begin{bmatrix}\cos \
u\\ \sin\
u\\ 0\end{bmatrix} \tag{14}\]  
where \(\mu\) is the standard gravitational parameter of the Earth.  
The transformation matrix \(A_{O2E}\) is used to convert the vector in the orbital plane to the ECI system:  
\[\mathbf{A}_{O2E}=\begin{bmatrix}\cos\omega&-\sin\omega&0\\ \sin\omega&\cos\omega&0\\ 0&0&1\end{bmatrix}\begin{bmatrix}1&0&0\\ 0&\cos i&-\sin i\\ 0&\sin i&\cos i\end{bmatrix}\begin{bmatrix}\cos\Omega&-\sin\Omega&0\\ \sin\Omega&\cos\Omega&0\\ 0&0&1\end{bmatrix} \tag{15}\]  
The parameter \(\Omega\) in \(\mathbf{A}_{O2E}\) results in rotation with the \(z\)-axis in the ECI system. Considering the geometry and the Earth's ellipsoid reference, in the following discussion, we set \(\Omega=0\) for convenience, and this will not result in an error value in the QPE approximation.  
The onboard orbit determination system measurements are given in ECI terms, which can be regarded as the true state vector plus an error vector. With the assumption that the measurement errors of the satellite's position and velocity follow a normal distribution with an expected value of 0, the error vectors can be described as  
\[\Delta\mathbf{P}=\begin{bmatrix}\Delta p_{x}\\ \Delta p_{y}\\ \Delta p_{z}\end{bmatrix}\sim\begin{bmatrix}N\left(0,\sigma_{p,x}^{2}\right) \\ N\left(0,\sigma_{p,y}^{2}\right)\\ N\left(0,\sigma_{p,z}^{2}\right)\end{bmatrix} \tag{16}\]  
\[\Delta\mathbf{V}=\begin{bmatrix}\Delta\
u_{x}\\ \Delta\
u_{y}\\ \Delta\
u_{z}\end{bmatrix}\sim\begin{bmatrix}N\left(0,\sigma_{p,x}^{2}\right) \\ N\left(0,\sigma_{p,y}^{2}\right)\\ N\left(0,\sigma_{p,z}^{2}\right)\end{bmatrix} \tag{17}\]  
Then, the state vector measurements of the satellite are  
\[\mathbf{P}_{sat,ECI,e}=\mathbf{A}_{O2E}\mathbf{P}_{sat,OP}+\Delta\mathbf{P} \tag{18}\]  
\[\mathbf{V}_{sat,ECI,e}=\mathbf{A}_{O2E}\mathbf{V}_{sat,OP}+\Delta\mathbf{V} \tag{19}\]  
From Equations (2), (3), (18) and (19), the position vectors of the antenna aiming point are  
\[\mathbf{P}_{tar,ECI,e}=\mathbf{P}_{sat,ECI,e}+\rho\cdot\hat{u}_{a,ECI} \tag{20}\]With the help of Equations (3), (19) and (20), the difference in the relative velocity from the satellite to the antenna aiming point can be given as  
\[\Delta\mathbf{V}_{st}=\left(\mathbf{V}_{sat,ECI,e}-\mathbf{V}_{tar,ECI,e}\right) -\left(\mathbf{V}_{sat,ECI}-\mathbf{V}_{tar,ECI}\right)=\begin{bmatrix}\Delta v _{x}+\omega_{e}\Delta p_{y}\\ \Delta v_{y}-\omega_{e}\Delta p_{x}\\ \Delta v_{z}\end{bmatrix} \tag{21}\]
## 3 Numerical Evaluations and Experimental Analyses  
### Parameter Settings  
This study used the MATLAB R2021b software platform to implement the proposed algorithm, which was validated using simulated experimental validation. The length \(L\), width \(W\), and height \(H\) of the monitoring area are 500 m; the sensing radius \(R_{s}\) of the node is 100 m; the radius \(R_{c}\) of node communication is 200 m; the maximum number \(K\) of iterations is 100; the number \(n\) of particles swarm is 50; the number \(N\) of nodes is 25-50; the maximum inertia weight \(w_{\max}\) is 0.9, the minimum inertia weight \(w_{\min}\) is 0.4; acceleration factors \(c_{\min}=0.25\), \(c_{\max}=2.75\), \(c_{\min}=1.25\), \(c_{\max}=2.5\), \(c_{\min}=0.25\), and \(c_{\max}=2.75\); the maximum step length of sensor node movement under the action of grid point is \(max\_step=2.5/2\); the maximum step length of sensor node movement under the action of sensor node is \(max\_sensor=3.5/2\); node initial energy is 100 J; and the transmitting power, receiving power, and idle power of the node are 2 w, 0.75 w, and 0.001 w, respectively. To verify the effectiveness of the algorithm, the proposed algorithm was compared with the BES [20], 3D-IVFA [22], and DABVF [24] algorithms under the same parameter conditions. The simulation performance of the proposed algorithm was compared with those of BES-DBR, 3D-IVFA-DBR, and DABVF-DBR on the NS-3 software platform. The results verified that network energy loss can be effectively reduced.
# Possible Blockchain Solutions According to a Smart City Digitalization Strategy
## 2 Blockchain in Smart City Solutions
### Security of Data Sent by Oracles  
As one of the ultimate goals, a mobile application (Android and iOS) was identified to unite all individual services and processes for city companies, institutions, and city users. To ensure the security of transfer, data from oracles should be encrypted. The blockchain network infrastructure can provide all that is necessary for data security [24; 25]:  
* Integrity, authenticity, and non-repudiation of legally relevant information (WELMEC Software Guide 7.2, Measuring Instruments Directive 2014/32/EU, 2020).
* The blockchain stores and attests public keys from IoT devices.
* The solution does not depend on a trusted third party.  
Additionally, the oracles should send data using secure IoT communication protocols to ensure anti-tampering protection [26; 27; 28]. To ensure the true values of the measured data it is important to prevent tampering, which can happen at the software level, hardware level [29; 30], and tampering sensor input level [31].
# Connectivity Analysis in VANETs with Dynamic Ranges
## 4 Discussion
### General Observations  
VANETs on highways exhibit different behaviors depending on the mobility pattern (synchronized or exponential) of vehicles and the path-loss model for signal propagation. In VANETs the interplay between vehicle density, SNR, and path-loss significantly affects network connectivity. For instance with high vehicle density, connectivity can be improved by providing more relay nodes (in the case of multi-hops) but also increases interference, leading to lower SNR due to packet collisions and co-channel interference. Conversely, low vehicle density reduces interference but increases the risk of network fragmentation and higher path loss, weakening SNR over longer distances. The path-loss factor further exacerbates connectivity issues, especially in sparse networks, as signal attenuation limits communication range. To mitigate these concerns, interference-aware MAC protocols, adaptive transmission power, multi-hop relaying, and hybrid communication strategies (e.g., DSRC and 5G) are essential. A balance may be struck between maintaining strong connectivity in dense environments while overcoming path-loss in sparse regions to ensure reliable communication in VANETs.
# Reviews on Corrugated Diaphrags in Miniature Fiber-Optic Pressure Sensors
## 2 With initial stress  
When the initial stress exists in the circular CD, the first term of the Equations (2) and (5) is the elastic restoring force of the diaphragm caused by the bending stress, while the second term is the elastic recovery caused by the mid-face stretching, and the ratio of FD to the CD in this term is \(2.83/b_{p}\). The mid-face stretch can also be regarded as the behavior of initial stress. Thus, the ratio of the elastic restoring force between the FD with initial stress and the CD with initial stress should also be \(2.83/b_{p}\), and the deflection equation of the CD with initial stress is [44]  
\[P=a_{p}E\frac{h_{d}^{4}}{R_{d}^{4}}\frac{\omega_{0}}{h_{d}}+4\frac{h_{d}^{2}}{ R_{d}^{2}}\frac{\omega_{0}}{h_{d}}q\frac{b_{p}}{2.83}+b_{p}\frac{E}{(1-v^{2})} \frac{h_{d}^{4}}{R_{d}^{4}}\frac{\omega_{0}^{3}}{h_{d}^{3}}, \tag{10}\]  
For small deflection, the cubic term of center defection \(\omega_{0}\) can be neglected, and mechanical sensitivity of the CD with initial stress can be obtained as [44]  
\[S_{m(CD)}=\frac{d\omega_{0}}{dP}=\frac{R_{d}^{2}}{4h_{d}\!\!\left[\sigma\frac{ b_{p}}{283}+\frac{a_{p}}{4}E\frac{h_{d}^{2}}{R_{d}^{2}}\right]}, \tag{11}\]  
As shown in Equations (6) (7) and (11), the mechanical sensitivity of the CD depends on the corrugation profile factor \(q\). Compared with the number of corrugations \(N\), as shown in Equation (9), the corrugation depth \(H\) has a much more important effect on \(q\), or the mechanical sensitivity of CD.  
In the case of small deflection, compared with Equation (4), the factor \(b_{p}/2.83\) in Equation (11) indicates that the effective initial stress will be reduced by \(2.83/b_{p}\) times. It is easily proved that \(2.83/b_{p}\) will be bigger than 1 when the corrugation depth is larger than 0 [42]. This means that the influence of initial stress on the mechanical sensitivity of the diaphragm will be reduced and improve the mechanical sensitivity of the diaphragm due to the introduction of corrugation. At the same time, the corrugated structure can be accurately fabricated by MEMS technology, which greatly reduces the dependence on the manufacturing technology compared to the deposition process.
# A Smart Collaborative Routing Protocol for Reliable Data Diffusion in IoT Scenarios
###### Abstract  
It is knotty for current routing protocols to meet the needs of reliable data diffusion during the Internet of Things (IoT) deployments. Due to the random placement, limited resources and unattended features of existing sensor nodes, the wireless transmissions are easily exposed to unauthorized users, which becomes a vulnerable area for various malicious attacks, such as wormhole and Sybil attacks. However, the scheme based on geographic location is a suitable candidate to defend against them. This paper is inspired to propose a smart collaborative routing protocol, Geographic energy aware routing and Inspecting Node (GIN), for guaranteeing the reliability of data exchanging. The proposed protocol integrates the directed diffusion routing, Greedy Perimeter Stateless Routing (GPSR), and the inspecting node mechanism. We first discuss current wireless routing protocols from three diverse perspectives (improving transmission rate, shortening transmission range and reducing transmission consumption). Then, the details of GIN, including the model establishment and implementation processes, are presented by means of the theoretical analysis. Through leveraging the game theory, the inspecting node is elected to monitor the network behaviors. Thirdly, we evaluate the network performances, in terms of transmission delay, packet loss ratio, and throughput, between GIN and three traditional schemes (i.e., Flooding, GPSR, and GEAR). The simulation results illustrate that the proposed protocol is able to outperform the others.  
Keywords:geographic routing protocol; inspecting node mechanism; reliable data diffusion; game theory; IoT +
Footnote †: journal: Sensors
# Evolutionary Game for Content Cache in a mm-Wave-Based Vehicular Fog
## 3 mm-Wave-Based Vehicular Communication Model
### Multi-Lane Road Model  
Vehicle distribution over the road area is modeled typically by 2D Poisson point process (2D-PPP), which shows more degree of freedom in vehicle location, but not tractable. In this paper, we adopt a multi-lane stochastic geometry model for directional mm-Wave vehicular communications,in which each of the multi-lanes is supposed to have consistent vehicle density \(\lambda\) for probability mass function [67].  
The number of neighbor vehicles that a transmitting vehicle can reach is varying with radio range as shown in Figure 3. Average nodes in multiple lanes of each region \(d_{1},d_{2}\) and \(d_{3}\) can be calculated as below when vehicle densities of those regions are \(\lambda_{gg}\), \(\lambda_{Gg}\) and \(\lambda_{GG}\), respectively.  
* Number of neighbor vehicles in area \(d_{1}\): both vehicles can have connection regardless of beam direction. \[\mathbb{E}[N_{1}]=\lambda_{gg}\bigg{(}2d_{1}+4\sqrt{d_{1}^{2}-d^{2}}\bigg{)},\ d<d_{1},\] (12) where \(d\) is distance from an origin node which is calculated by number of lanes (i.e., \(d=iL_{w}\), \(i\)-th lane with lane width \(L_{w}\)).
* Number of neighbor vehicles in area \(d_{2}\): there are two cases of connectivity in the region, \(G_{t}g_{r}\) or \(g_{t}G_{r}\). \[\mathbb{E}[N_{2}]=2\lambda_{Gg}\bigg{(}2d_{2}+4\sqrt{d_{2}^{2}-d^{2}}\bigg{)},\ d_{1}\leq d<d_{2}.\] (13)
* Number of neighbor vehicles in area \(d_{3}\): both Tx and Rx antennae have beam directions toward each other. \[\mathbb{E}[N_{3}]=\lambda_{GG}\bigg{(}2d_{3}+4\sqrt{d_{3}^{2}-d^{2}}\bigg{)},\ d_{2}\leq d<d_{3}.\] (14) Consequently, total expected neighbor vehicles are \(\mathbb{E}[N]=\sum_{i}E(N_{d_{i}})\), \(i\in\{1,2,3\}\)
# Non-Contact and Non-Intrusive Add-on IoT Device for Wireless Remote Elevator Control
## 4 Design and Implementation of the IoT Device
### Compatibility with the Original Button Panel  
The IoT device has been designed to be compatible with manual operation of the buttons that compose the original button panel, a feature that is especially required for an add-on device in case the internal battery runs out of power. Figure 3 shows an illustrative representation of the system proposed to replicate the behavior of the original buttons of the elevator's control panel. The front side of the casing (Figure 3a) includes an additional set of buttons with a pressure sensitive area that transmits any pressure to the original buttons  
Figure 2: Push button activation with servomotors: (**a**) button (blue part) released; (**b**) button (blue part) pressed.  
underneath. Figure 3a shows a front view of one of the additional buttons, which are similar to a flexible cantilever. When pressed (Figure 3c), the area that joins the cantilever to the casing is elastically deformed, transferring the pressure to the original buttons through a column attached to the cantilever.
# Calculation of Nonlimit Active Earth Pressure against Rigid Retaining Wall Rotating about Base
## 3 Determination of Friction Angle in A Nonlimiting State  
### Qualitative Analysis of Influencing Factors of Nonlimiting State Friction Angle  
In applying the classical earth pressure theory, engineers and technicians assume that the soil displacement behind the wall reaches its ultimate state: this is then reduced by applying a safety factor. However, the wall often fails to reach the limiting condition [19; 26]. [PERSON] [27] first proposed the nonlimit state hypothesis, which holds that an intermediate state between the static and limiting states of the retaining wall exists, which is referred to as the nonlimit state. Nonlimit earth pressure is affected by factors such as retaining wall displacement mode and fill displacement, and the calculation is very complex [28; 29]. At present, it is in the exploratory stage. Relevant research is of great significance to ensure the stability of retaining walls and optimize structural design [30].  
The model experiments of [PERSON] [31] and [PERSON] [32], as well as the FEM results of [PERSON] [33] and [PERSON] [34], demonstrated that as the wall rotation under RB increased, the earth pressure at different depths declined sharply and then gradually stabilized. The backfill soil entered the limit equilibrium state from top to bottom.  
The resultant force of ground pressure diminishes as the rotation angle increases in RB mode. The displacement of the retaining wall increases with the increase in the wall rotation. Notably, \(S_{c}\) was defined as the critical value of the displacement of the retaining wall reaching the active limit state, which was proposed by [PERSON] et al. [31]. [PERSON] et al. [19] suggested \(S_{c}=3\sim 5\times 10^{-4}\) H, with H as the wall height, and the value of \(S_{c}\) was only dependent on the wall height. It is assumed that \(S_{\text{d}}\) refers to the horizontal displacement of the soil behind the wall top when the wall rotates. When \(S_{\text{d}}/S_{c}<1\), the soil within the wall height is in the nonlimit state; when \(S_{\text{d}}/S_{c}=1\), the soil at the top of the wall only reaches the active limit state; and when \(S_{\text{d}}/S_{c}>1\), the active limit state begins to extend downward from the top of the wall with the increase in the angle of the rigid wall, and the earth pressure continuously decreases (these conclusions come from the research of [PERSON] [26]). Because the rotation angle is large enough, the shear strength of the soil can be  
Figure 1: Schematic of soil behind retaining wall.  
thoroughly developed. Moreover, the rotation angle continues to increase, soil pressure behind the wall remains constant, and active limit states stop propagating downward.  
For the following parameter analysis, the rotation angle \(\epsilon\) of the retaining wall is defined as the ratio of the displacement \(S_{\text{d}}\) of the soil at the wall top to the wall height \(\text{H}\left(\epsilon=S_{\text{d}}/\text{H}\right)\), and the unit is radian.
## 4 Discussion  
This study assessed the current state of taxonomic knowledge available to enumerate the diversity of dermapteran fauna in the southern Australian grain growing regions and is the first to apply molecular methods to study Australian native Dermaptera. Anisolabididae were classified based on combinations of male primary and secondary sexual characters. These reliably sorted males into morphospecies, all of which showed consistently lower genetic distance within than among putative taxa at the _cox1_ barcode locus as well as a fragment of _cob_, reflecting a clear barcode gap. The use of barcodes therefore shows promise for the study of Australian Dermaptera. Importantly, this allowed females of the morphospecies to be identified, which permitted a more complete assessment of morphology and allowed all samples to be resolved. Female genitalia, the spermatheca and the ovipositor, have been recognised as potentially important taxonomic characters [97; 98], and so future studies should incorporate these characters into their morphological assessment, as they will likely provide more clarity to the problem of female identification. Another important caveat is the lack of female specimens for \(G\). sp. 2 and \(G\). sp. 3. Given the morphological similarity within the SA (_G_. _forcipata_, \(G\). sp. 2, \(G\). sp. 3) clade, females of \(G\). _forcipata_ could be mistaken for females of these two unknown species, over-inflating estimates of the distribution and abundance of \(G\). _forcipata_. This is possible, but unlikely, as \(G\). _forcipata_ was not found at any of the sites where male \(G\). sp. 2 and \(G\). sp. 3 were collected. Moreover, juvenile morphology remains opaque, although cost-effective adult-larval matching solutions for large numbers of samples are available [99], which could take advantage of the sequence data reported herein. Given that these morphospecies were found at the sites with less-frequent sampling, it is likely that the scarcity of these morphospecies in the sample is an effect of sampling bias.