esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Preliminaries
### Network Assumption  
In this paper, a UWSN is composed of \(n\) homogeneous sensors, \(\{s_{1},\ldots,s_{n}\}\), and a sink with mobility. Here, only the sink is assumed to have mobility. The unattended sensors are assumed to be static. These sensors are uniformly scattered over the sensing region to execute the pre-determined functionalities such as data gathering. The underlying network is assumed to be connected such that two arbitrary sensors can communicate with each other through either direct communication or multi-hop path. Time is divided into _collection intervals_, each of which will be further divided into \(v\) rounds, and can be synchronized by existing techniques [13; 14; 15]. In UWSNs, no constant connection between sensors and the sink exists. Instead, the itinerant sink periodically visits the UWSN to collect data at the end of each collection interval. Sensors are scheduled such that, in each round, each sensor obtains exactly one sensed datum. For ease of presentation, we assume that each sensor in each round gets exactly one sensed datum. Note that our proposed method actually can be applied on the case that each sensor can derive multiple sensed data or nothing in a round. After the contact to the data sink, sensors are securely re-initialized. We temporarily assume that the message transmission is reliable, which means that the message can be always be sent to the destination sensor. We will relax this assumption in later sections. In UWSNs, the geographic position of each sensor is known by the sink. This is due to the characteristic of UWSNs that the mobile sink harvests the sensed data by directly visiting the sensors. To do so, the sink must know the position of each sensor in advance. Two possible ways make the sink know the position of each sensor. First, sensors are deterministically deployed one by one by the network owner so that the position information will be passed to the sink by the owner. Second, sensors are randomly deployed, but can be aware of their geographic positions by using techniques such as [16; 17; 18; 19] or specific hardwares such as Global Positioning System (GPS).
# Occlusion-Robust Multi-Target Tracking and Segmentation Framework with Mask Enhancement
## 2 Related Works  
### Trajectory Association Algorithms  
In recent years, researchers have proposed various solutions to optimize trajectory association in MOT tasks, as shown in Table 1. Online trajectory association methods process current frames using present and past data without future information. Existing online methods [2; 3; 4; 5; 6; 7; 8] process video frames sequentially to generate tracking results but often cause trajectory loss or identity drift due to occlusion or detection errors.  
Filter-based tracking [9; 10] based on target motion and appearance generates predictive data for trajectory association. These algorithms demonstrate fast computational speeds but suffer from background noise and motion blur.  
The Multiple-Hypothesis Tracking (MHT) algorithm [5; 14] constructs multi-layer hypothesis trees for each target. However, the computational complexity grows exponentially with hypothesis expansion.  
[PERSON] et al. [15] integrated deep neural network-derived appearance features into MHT frameworks. [PERSON] et al. [16] further optimized the hypothesis, updating it through LSTM networks. Progress was also made in deep-learning based methods. [PERSON] et al. [17] employed spatial-temporal attention to address partial occlusion. [PERSON] et al. [18] proposed LSTM networks for modeling temporal dependencies. [PERSON] et al. [8] utilized deep residual networks to compute inter-frame target similarity. [PERSON] et al. [19] developed an anchor-free tracking approach based on CenterNet [20].
## 3 Construction of Our Work
### Security Analysis
Since the likelihood of the adversary guessing \(st_{c+1}\) is \(\frac{1}{2^{\lambda}}+negl(\lambda)\), and the probability after \(p=poly(\lambda)\) polynomial queries by the adversary is \(\frac{p}{2^{\lambda}}+p\cdot negl(\lambda)\), it follows that \(G_{3}\) and \(G_{2}\) are indistinguishable. \[\Pr(G_{2}=1)-\Pr(G_{3}=1)\leq\frac{p}{2^{\lambda}}+p\cdot negl(\lambda). \tag{3}\]  
**Game \(G_{4}\)**: In \(G_{4}\), the value of \(st\) is generated instantaneously during the search. Algorithm 5 shows the algorithmic changes on the \(DO\). \(G_{4}\) utilizes **Updates** to record the update query that occurred after the last search, and extracts the number of updates and \(\mathbf{DB}(w)\). \(G_{4}\) randomly selects the query result of a random oracle without the information about \(st\). Subsequently, \(st\) is generated and used to update the random oracle during the execution of the search operation. From the adversary's perspective, since \(G_{4}\) and \(G_{3}\) output two random strings during the update phase and both output \((k_{w},st_{c})\) during the search, \(G_{4}\) and \(G_{3}\) are completely indistinguishable. **Simulator**: In the ideal game \(I_{DEAL}\), the simulator \(\mathcal{S}\) constructs a view by utilizing the leakage function. Algorithm 6 details \(\mathcal{S}\), which maintains two mappings and an update counter. The values of each mapping are also randomly generated during the update. Unlike \(G_{4}\), \(\mathcal{S}\) uses \(\underline{w}\leftarrow\min\mathbf{sp}(w)\) to represent the first time to search keywords \(w\) and uses the update history \(\mathbf{uh}[w]=((i,bs_{i})|q_{i}=(i,bs_{i},w))\) as input to parse the number of keyword updates and update requests, rather than computing based on **Updates**. Afterwards, \(\mathcal{S}\) randomly generates \(st_{i}\) and updates the random oracle with the information above. The simulator and \(G_{4}\) are entirely equivalent. \[\Pr[I_{DEAL}(\lambda)=1]=\Pr[G_{4}=1] \tag{4}\]```
**Setup\((\lambda)\)**  
_Data Owner_ :
```
1:L,E\(\leftarrow\)empty map
2:\(t\gets 0\)

```
```
```
1:M,W,bs,T\()\)
2:Mat Owner_ :  
```
3:Add \((t,bs)\) to Updates\([w]\)
4:L\([t]\xleftarrow{\xi}\{0,1\}^{\lambda}\)
5:E\([t]\xleftarrow{\xi}\{0,1\}^{l+\lambda}\)
6:\(t\gets t+1\)
7:Send \((\textbf{L},\textbf{E})\) to server
```  
```
1:M,W,bs,T\()\)
2:Data Owner_ :
3:if Key\([w]\)\(=\bot\)then
4:\(\textbf{Key}[w]\xleftarrow{\xi}\{0,1\}^{\lambda}\)
5:endif
6:\(k_{w}\leftarrow\textbf{Key}[w]\)
7:if\(\textbf{W}[w]\)\(=\bot\)then
8:\(\textbf{W}[w]\xleftarrow{\xi}\{0,1\}^{\lambda}\)
9:endif
10:\(((v_{0},bs_{0})\cdots,(v_{c},bs_{c}))\)\(\leftarrow\) Updates\([w]\)
11:\(st_{0}\leftarrow\textbf{W}[w]\)
12:\(c\leftarrow|\textbf{Updates}[w]|\)
13:for\(i\)\(=\)\(1\)to\(c\)do
14:\(st_{i}\xleftarrow{\xi}\{0,1\}^{\lambda}\)
15:\(\textbf{H}_{1}[k_{w}||st_{i}]\)\(\leftarrow\) L\([v_{i}]\)
16:\(\textbf{H}_{2}[k_{w}||st_{i}]\)\(\leftarrow\) E\([v_{i}]\oplus(st_{i-1}||bs_{i})\)
17:endfor
18:Send \((k_{w},st_{c})\) to server
```  
**Algorithm 5**_Game_\(G_{4}\)
## 6 Discussion  
Future quantum technologies have been highlighted as important considerations in the automotive domain [40], with QKD highlighted as an important aspect in maintaining the future security of connected vehicles. Practical examples of using quantum technologies in vehicular communications tend to focus on theoretical ideas and simulations [16; 17; 18; 19]; however, in AirQKD, we can see how existing communications systems work with emerging quantum technologies.  
The work involved in the AirQKD project was extensive and complex; it involved multiple partners and technologies. The V2I application of FSO-QKD security constituted just a fraction of the whole project, yet, in itself, it still required significant effort for its design and implementation. Therefore, we cannot delve into the details of every element but focus on providing enough details to ensure an understanding of the project, similar to how other testbeds report results [41]. Furthermore, a substantial amount of IP has been developed, which limits the amount of information that can be provided on many aspects of the technology.
# Comparative Analysis of the Methods for Fiber Bragg Structures Spectrum Modeling
## 1 Introduction
In this paper, firstly, a homogeneous non-apodized FBG was modeled using both methods, and the resulting reflectance spectra were compared to the one obtained via a specialized commercial software. Secondly, modeling results of a \(\pi\)-phase-shifted FBG are presented. For both FBG models, the influence of the partition interval of the LS method on the simulated spectrum was studied. It was established that the asymmetry of the refractive index profile partition near the phase shift relative to its center causes the asymmetry of the spectral response of the simulated grating, resulting in the increased simulation error. Therefore, in the case of phase-shifted FBG, in order to increase the simulation accuracy, it is not enough to decrease the partition interval of the LS method, but it is also required to fulfill certain conditions of the refractive index definition in the model, which are discussed in the manuscript.
## 2 Materials and Methods
### Data Sources  
In this study, the Digital Elevation Model (DEM), land use/cover, soil, meteorology, and in situ measured runoff data were used to construct the SWAT hydrological model for the URB (Table 1). The DEM data were obtained from NASA with a spatial resolution of 30 m. Furthermore, to effectively characterize the surface cover changes--for instance, the degradation of grassland caused by overgrazing--the land-use/cover data were obtained via a secondary classification system for 2000, 2010, and 2020, with a spatial resolution of 1 km. The data were downloaded from the Environmental Resources and Data Center of the Chinese Academy of Sciences. Soil data were obtained from the Harmonized World Soil Database, with a spatial resolution of 1 km. Weather data were provided by the China Meteorological Data Network, with regard to daily precipitation, maximum/minimum temperatures, wind speeds, and relative humidity from 1981 to 2020 for the URB and 12 adjoining meteorological stations. The SWAT model employed multi-objective calibration, using measured runoff, field sampling, and Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover and ET data. The measured runoff data were monthly runoff  
Figure 1: Overview map of (**a**) geographic location, (**b**) elevation, (**c**) land use/cover, and (**d**) subbasin delineation of the URB.  
data, which were obtained by collating data between 1981 and 2000 from the Nunaimiao hydrological station and between 2004 and 2012 from the Ulgai reservoir inlet hydrological station. Field sampling data were converted into runoff based on the field measurements of water level and cross-section. The remote-sensing snow cover and ET data were selected from MODIS MOD10A1 and MOD16A2 products from 2001 to 2019, respectively, both having a spatial and temporal resolution of 8 days/500 m.  
Additionally, remote-sensing data, basic geographic information, and socioeconomic data were selected to evaluate the spatial and temporal variation characteristics of EQ in the URB from 2001 to 2019 (Table 1). Among these, remote-sensing data mainly included gross primary productivity (GPP), normalized difference vegetation index (NDVI), LAI, and land-cover type (LCT) data from 2001 to 2019. The MODIS MOD17A2, MOD13A3, MOD15A2, and MCD12Q1 products were selected and downloaded free of charge from the NASA Land Processes Distributed Data Archive Center, respectively. The spatiotemporal resolutions of MOD17A2, MOD13A3, and MOD15A2 products were 8 days/500 m, 30 days/1 km, and 8 days/500 m, respectively. The basic geographic-information data were vector boundary layers of the URB, and the socioeconomic data focused on population density, based on the population data network, with a spatial resolution of 1 km.
# Antibiofilm Potential of Natural Essential Oils
## 4 ABF Activity of EOs and Their Components
### EOs as Antimicrobial Agents (Mechanisms of Action)
[42] demonstrated the AM and ABF effects of the _Elletaria cardamomum_ L. EO (ECEO) containing \(\alpha\)-terpinyl acetate (34.95%) and eugenol (25.30%). The EO used in the 1% concentration inhibited the growth of _E. coli_ O157:H7 and _P. aeruginosa_ ATCC 14213. The evaluation of the ABF activity showed that the EO concentration of 0.015, 0.031, 0.062, and 0.125% caused 64.29, 65.98, 70.41, and 85.59% inhibition of BF formation by _E. coli_ and 6.13, 45.50, 49.45, and 100 inhibition of BF formation by _S._ Typhimurium, respectively. The authors pointed out that ECEO could be used as a safe natural AM agent inhibiting microbial BF and counteracting multidrug-resistant microorganisms, as well as a safe alternative to chemical agents in the food industry. In turn, [PERSON] et al. [38] showed the AM and ABF effects of an EO from _Tetractinis articulata_ (Vahl) Masters sawdust (containing 34.37% carvacrol and 11.07% cedrol). Nine strains were tested, including eight clinical isolates from catheter-related infections and one ATCC 25923. The analyzed EO showed strong AM activity against all strains, including one multidrug-resistant (MDR) _S. aureus_ strain and two methicillin-resistant _S. aureus_ (MRSA) strains, as well as BF-inhibiting activity.
## 3 Problem Description
When \(G\) exceeds \(G^{*}\), the normalized throughput rapidly decreases. In this paper, the higher the normalized throughput of the CAS, the higher the channel utilization rate of TTS (the more SUE successfully gains access within a unit time, the more data the satellite receives). Therefore, for systems that use the SIC algorithm, the optimization goal is  
\[P2:min(|G-G^{*}|)=min\bigg{(}\bigg{|}\frac{M_{i}P_{i}}{N_{C}}-G^{*}\bigg{|} \bigg{)}. \tag{5}\]  
Considering a potential scenario where a piece of SUE may be persistently randomized into a nontransmitting state due to the access probability, resulting in prolonged waiting times for some pieces of SUE, we further propose an alternative algorithm. Since the access load represents the number of accessing pieces of SUE per frame, we no longer regulate the number of accessing pieces of SUE through probability adjustment but instead control the access load by dynamically adjusting the frame length. The load expression is given as follows:  
\[G_{i}=\frac{m}{n}=\frac{M_{i}}{K_{i}}, \tag{6}\]  
Figure 4: Flowchart of Successive Interference Cancellation (SIC). where \(K_{i}\) denotes the adjusted number of timeslots in frame \(i\). The optimization objective of the method is as follows:  
\[P3:min(|G-G^{*}|)=min(\left|\frac{M_{i}}{K_{i}}-G^{*}\right|). \tag{7}\]  
The three approaches mentioned above are named STCA without SIC, STCA with SIC, and STCA with variable frame length, respectively.
## 2 Related Works
### Method Based on Game Theory  
The resource management challenges in large-scale UAV communication networks were explored from a game-theoretic perspective in [6], aligning perfectly with the distributed and autonomous nature of such networks. In [27], a network-side self-organized coalition formation approach was introduced to mitigate intra-tier interference primarily through neighborhood cooperation. By forming disjoint coalitions, small-cell access points (SAPs) were able to reduce interference within each coalition, thereby enhancing their achievable data rates. Similarly, ref. [28] proposed a distributed coalition formation algorithm for cooperation in ad hoc networks, utilizing joining and leaving rules to ensure that each node's choice led to a stable coalitional structure.  
While the coalition formation games proposed in these studies effectively coordinated internal interference by aggregating adjacent nodes into independent coalitions, they only managed to reduce the degree of internal interference rather than eliminating it entirely. From our perspective, given that UAV networks are tasked with transmitting a significant volume of safety-critical control information, any data loss due to internal interference could result in substantial and potentially catastrophic consequences. Therefore, the ultimate design goal for UAV swarm networks should be the establishment of a completely collision-free and highly reliable network.
# A Power Control Algorithm Based on Chicken Game Theory in Multi-Hop Networks
## 2 System Model
The fading correlation property between the control channel and the data's communication channel can be derived as follows:  
\[\rho=\frac{1}{L}\frac{\sin\left(\frac{2\sqrt{3}\pi L}{\sqrt{L^{2}-1}}\Delta f \tau_{rms}\right)}{\sin\left(\frac{2\sqrt{3}\pi}{\sqrt{L^{2}-1}}\Delta f\tau_{ rms}\right)}\exp\left(j2\sqrt{3}\pi\sqrt{\frac{L-1}{L+1}}\Delta f\tau_{ rms}\right) \tag{9}\]  
where \(\Delta f\) is the carrier frequency separation between the two independent channels and \(\tau_{rms}\) is the delay spreading of the fading channel. Once \(L=2\) and \(\Delta f.\tau_{rms}=0.165\), the frequency separation should be \(\Delta f=165\) KHz for \(\varrho=0.5\). Figure 2: Impact of \(\varrho\) on the transmit power.
# Protoporphyrin IX Based All-Solid-State Ion-Selective Electrodes for Choline Determination In Vitro
## 1 Introduction
Another solid-contact ISE for Ch and derivatives determination based on octa amide cavitand and carbon nanotubes dealt with the problem of inner filling solution in conventional electrodes, however, the linear range of \(10^{-5}\)-\(10^{-1}\) M and selectivity over common ions such as ammonium and sodium were not yet satisfactory [27]. The ionophore plays a central role in the selective binding with the compounds. As a macrocycle aromatic compound with tetrapyrrole, protoporphyrin IX shown in Figure 1 can bind Ch via cation-\(\pi\) bonding [28; 29]. The porphyrin core and hydrogen of the O-H group in Ch can also form the hydrogen bonds [30]. Moreover, the hydrophobic property of protoporphyrin is more suitable for lipophilic polymer membranes. It minimizes the leakage from the membrane into the aqueous solutions [31], seemingly a proper ionophore for Ch. In recent years, solid-state ISEs draw increasing attention, rather than classical liquid-contact ISEs in potentiometric methods. Between the ion-selective membrane and electron-conducting substrate, a solid conductive layer replacing the conventional internal filling solution makes the most difference. It acts as an ion-to-electron transducer to transfer  
Figure 1: Molecular structure of protoporphyrin IX. the ionic conductivity in the selective polymer membrane into the electronic signal in the electron-conducting substrate [32]. Various solid conductive polymers have been tested and applied. Poly (3_A_-ethylene dioxythiophene) doped with poly (styrene sulfonate) (PEDOT/PSS), one of the most well-known organic conductors, is commonly applied in electrodes due to low redox potential, high conductivity, good electrochemical stability and biocompatibility [33; 34]. During electropolymerization, the hydrophobic EDOT interacts with the hydrophilic PSS to form PEDOT/PSS hybrid fibers. Electrostatic forces of attraction between \(\pi\)-conjugated PEDOT chains and negatively charged PSS chains dominate the conductivity [35; 36]. As a redox pair, PEDOT/PSS transmits the ions to electrons. In this paper, an all-solid-state ISE for Ch determination was fabricated. By electropolymerization, the PEDOT/PSS layer performing as an ion-to-electron transducer was coated on the gold wire. Furthermore, the PVC selective membrane containing protoporphyrin IX as a specific ionophore for Ch determination was covered on the solid conductive layer. The fabricated Ch ISE showed a detection limit of 0.49 uM and a fast response time of less than 5 s. The characteristics such as stability, selectivity, reproducibility and repeatability were also investigated. Moreover, the performance in artificial cerebrospinal fluid was studied as well.
## 4 Analysis of the CRB for Short Observation Intervals
### CRB when \(\Delta\rho>\rho_{chip}\)
The results when computing Equation (40) match the CRB(\(h\)) values obtained using Equation (35) when \(\Delta\rho>\rho_{chip}\), which correspond to the flat part of the curves later shown in Figure 4. As a consequence, it follows that a two-tracking channel architecture for code altimetry is efficient in terms of the bound defined in Equation (35) given a sufficiently large propagation path difference. In practice, such a scenario might appear to be unlikely for ground-based receivers. Nonetheless, many of the new GNSS signals have higher bandwidths, which implies lower \(\rho_{chip}\) values (e.g., 293 m for GPS L1 C/A signal vs. 29.3 m for GPS L5) [62]. In the previous discussion, we have highlighted the problem arising when trying to separately estimate the two contributions to \(\Delta\phi\) and how the CRB for the joint estimation of \(\mathbf{\
abla}\) captures this effect. However, what if we assume some prior knowledge of one of the two terms in \(\Delta\phi\)? We examine now the impact on the CRB if we assume that one of these terms is known, which represents the best case scenario given such an assumption. We study two cases: (1) when \(\phi_{\text{T}}\) is assumed to be known, referred to as \"phase altimetry\" case; and (2) when \(h\) is known, referred to as \"reflection coefficient estimation\" case.
## 3 Results
### Frost Resistance  
Figure 11 shows a frost resistance test chamber and concrete samples after freezing/thawing cycles. For each type of concrete, 12 samples were prepared (6 reference samples and 6 samples for freezing/thawing cycles), of which averages were calculated (Table 7).  
The analysis of Table 7 shows that among the reference samples, the highest compressive strength of 52.6 MPa was obtained by M1 concrete, while the smallest was 43.4 MPa in BZ concrete without additives. Considering the compressive strength of samples after 150 freezing cycles, it was observed that the highest average compressive strength of 46.4 MPa was obtained by M1 concrete, in which cement was replaced with glass flour in the amount of 10%. The lowest compressive strength after 150 freezing cycles was recorded for M3 concrete. The average strength in this case was 34.2 MPa. The average decrease in the strength of the samples subjected to freezing in two cases for concrete M2 and M3 exceeded the permissible 20%. The lowest decrease in strength was for BZ concrete samples without additives. The average weight loss after freezing ranged from 0.381% for BZ samples to 0.880% for M3 samples. Samples were subjected to 150 freeze and thawing  
Figure 11: (**a**) A chamber for testing the frost resistance of concrete and (**b**) samples after the test.  
Figure 10: Average density of concrete.  
cycles, in which cement was replaced with glass flour in the amount of 10% and concrete without additives is frost-resistant--F150. The other two types of concrete, in which cement was replaced in the amounts of 20 and 30%, turned out to be non-frost-resistant concrete. The research shows that there is a certain limitation at which the conversion of cement to glass flour causes a decrease in compressive strength after freezing/thawing cycles. In the context of these results, it can be indicated that the optimal content of the additive in the form of glass flour is 15%.