esa-sceva/satcom-chunk-collection huggingface dataset:
# Conservative Interference Injection to Minimize Wi-Fi Sensing Privacy Risks and Bandwidth Loss
## 2 State of the Art
### Large-Scale Applications  
Macroscopic changes in the channel, such as the introduction of new objects, cause large-scale changes to the multipath propagation characteristics of an environment. This is reflected in CSI, which can have large changes in the amplitude level or fluctuations over a captured time series [1; 13]. To correlate changes in CSI with the environment, the CSI is first filtered to remove noise. Common filtering approaches include Hampel filters to remove outliers [1] and lowpass filters to smooth the CSI [14]. Following this, features can be calculated to correlate with changes in the physical environment. In applications such as object detection [11; 15], the CSI is analysed on a frame-by-frame basis since the sensing subject is not time-varying. Hence, statistical features calculated on the set of CSI amplitudes have been used in conjunction with Support Vector Machines (SVMs) to predict the presence of different people or objects. Beyond this, large-scale changes in a physical environment such as occupancy levels have also been detected using absolute CSI amplitude levels. In addition to this, some large-scale sensing applications have used more complex temporal features calculated over the sampled time series of CSI. This includes feature calculation techniques such as Principle Component Analysis (PCA) [10] and Wavelet Transforms [1; 10], which allow temporal variations to be analysed and subsequently fed into supervised machine learning (ML) algorithms to predict behaviours such as falling, walking and waving [16].
# Molecular Imaging with \({}^{68}\)Ga Radio-Nanomaterials: Shedding Light on Nanoparticles
## 4 Biomedical Applications of \({}^{68}\)Ga-Labeled Nanoparticles
### Nanoparticles for Optical Imaging
#### 4.3.1 Quantum Dots  
QDs possess unique size-dependent photo luminescent properties due to the quantum confinement effect [124; 125; 126]. They produce bright emissions spanning the visible and infrared spectrum when excited by high-energy photons. QDs are composed by hundreds to thousands of atoms from group II and VI elements (e.g., CdSe and CdTe) or group III and V elements (e.g., InP and InAs). Their size and shape (dots, rods or tetrapods) [127], together with their internal structure (core-shell, gradient alloy or homogeneous alloy) can be precisely controlled [128]. Among the studied types of QDs the core-shell configuration of CdSe/ZnS has been shown to yield the best performance, having been used for several in vitro and in vivo studies [129; 130; 131].  
However, due to the known toxic effect that heavy metals contained in this type of NPs have in vivo, research has been focused on their biocompatible quantum-sized carbon analogs, carbon dots (CDs) [80; 132]. CDs are formed by small carbon molecules that are surface-passivated by organic molecules or biomolecules becoming strongly fluorescent in the visible and near-infrared (NIR) spectral regions. Several research groups have carried out studies using CDs to produce OI in vivo [133; 134].  
[PERSON] et al. [135] radiolabeled CdTe quantum dots using heteroleptic chelated-carboxylatogallium(III) stable complexes, -SCH2(CO)CO- was used as chelate ligand to complex QDs to \({}^{68}\)Ga. Nanoradiotracer radiochemical purity was maintained at 99% during the first 5 h. They were used in vivo for fibro sarcoma coincidence imaging in tumor-bearing rats. Coincidence imaging during the 40 first minutes after nanoradiotracer injection revealed significant tumor and liver accumulation. Ex vivo gammacounter quantification 30 min, 1 h and 2 h post injection confirmed probe accumulation was five times higher in tumor than in liver, blood, kidneys and lungs.  
Figure 7: **(a)** PET image 30 min post \({}^{68}\)Ga radiolabeled liposome injection (without glucose in the formulation); and **(b)** PET image 30 min post \({}^{68}\)Ga radiolabeled glucose liposome injection. The arrows show tumors. IP, injection point; K, kidneys; L, liver; Spl, spleen; Bl, bladder; M, magnet; T\({}_{\text{right}}\) and T\({}_{\text{left}}\), tumors. Adapted from [PERSON] et al. [119].
# Calculation Method of Rotational Constraint Stiffness for a New Tower-Pier Connected System
## 2 The Plane Rotation Model of UHS
### Parametric Analysis of RCS
#### 3.2.2 Influence of Central Angle on RCS  
When the curvature radius \(R_{0}=1.5\) m, friction coefficient \(\mu=0.15\), and vertical force \(F=10\) MN, the rotational performances of UHS with central angles of \(13^{\circ}\), \(16.5^{\circ}\), \(20^{\circ}\), \(23.5^{\circ}\), and \(27^{\circ}\) were studied. The UHS responses are shown in Figures 9 and 10.  
Figure 9 shows that the critical torque increases with the increase in the central angle. According to the above method, RCS can be obtained as shown in Figure 10. It can be concluded that when the vertical force, radius of curvature, and friction coefficient have certain values, the RCS increases with the increase in the central angle by a linear trend.  
As \(R_{2}=R_{0}\times\theta\times\frac{\pi}{180^{\circ}}\), RCS has a positive proportional relationship with \(R_{2}\). The related curve of RCS and the radius of the contact surface is fitted using the least square method in order to obtain the following expression equation:  
\[K_{M}=2021.6R_{2}=\beta\times R_{2} \tag{9}\]  
where the unit of RCS \(K_{M}\) is kN\(\cdot\)m/rad, and the unit of radius of arc-contact surface is mm. \(\beta\) is a coefficient with dimensions measured in kN/rad.
## 1 Introduction
This manuscript summarizes the privacy leakage problem in the satellite-terrestrial cooperative task-offloading architecture and addresses the problem by sending extra dummy computing tasks. Offloading cost in the privacy-preserving scenario is modeled, and the original DRTO algorithm is used to speed up the solution of the offloading decision. We conduct further experiments to evaluate the variability in the task size metric and task jitter metric before and after privacy protection. Extended simulation results prove the superiority of our privacy protection scheme and DRTO algorithm. The remaining parts of this paper are organized as follows. In Section 2, we summarize the possible privacy problems during offloading and introduce some other related works. Then we describe the system model with the privacy consideration, and formulate the offloading cost minimization problem in Section 3. The details of the DRTO algorithm are introduced in Section 4. In Section 5, the simulation results of both the privacy protection effectiveness and algorithm performance are presented. Finally, the paper is concluded in Section 6.
# Fluorescence Correlation Spectroscopy Measurement Based on Fiber Optics for Biological Materials
## 2 Materials and Methods  
### Fiber-Optic Based Fluorescence Correlation Spectroscopy (FB-FCS)  
Figure 1 shows the experimental setup of the FB-FCS system. The excitation light source was a laser diode (LP488-SF20, Thorlabs, Newton, USA) with a wavelength of 488 nm. The excitation laser, whose spectrum was narrowed by a laser line filter (FL488-1, Thorlabs, Newton, USA), was coupled to port A of a 99:1 fiber coupler (FC488-99B-FC, Thorlabs, Newton, USA). 99% of the laser was output from port B and absorbed by a light trap (LT) (FTFC1, Thorlabs, Newton, USA). The remaining 1% of the laser was output from port C and collimated by a lens (L\({}_{1}\)) with a focal length of 40 mm. The laser was focused on fluorescent samples on coverslips using a water immersion objective lens (OL) (UPlanSApo 60\(\times\)/1.20 W, Olympus, Tokyo, Japan). The diameter of the excitation laser spot (the diameter of the Airy disk) is 496 nm in an ideal setup. The emitted fluorescence of the Airy disk pattern with a diameter of 6.62 \(\upmu\)m was imaged on the end surface of port C. The mode field diameter of the optical fiber was 3.5 \(\upmu\)m at 515 nm. The end surface of port C played the role of a pinhole, and its diameter was estimated to be approximately 0.53 Airy units. The position of the focal spot of the objective lens and fiber core of the fiber coupler at port C were always optically conjugated. Thus, precise pinhole adjustment is not necessary for FB-FCS. Adjustment of the correction ring of the objective lens depending on the thickness of the coverslip makes the sensitivity of the system higher. The fluorescence imaged on port C was coupled to the fiber coupler, and 99% was output from port D. Finally, the fluorescence was detected by a single photon counting module using a photomultiplier tube (PMT) (H7421-40, HPK, Hamamatsu, Japan) via a lens L\({}_{2}\) and emission filter (EF) (FF01-525/39-25, IDEX Health & Science, New York, USA).  
The TTL signal from the PMT was transformed into an autocorrelation function (ACF) using a hardware correlator (10 CX220 YU484I6G, APOLLO, Yokohama, Japan). The time resolution of the correlator is variable in the range of 10-1,280 ns because it is constructed using a field-programmable gate array (FPGA) chip. The time resolution of the ACF was set to 640 ns in this study.
# The Application of Nano Titanium Dioxide for Hydrogen Production and Storage Enhancement
## 2 Titanium Dioxide
### Hydrogen Production Exploiting TiO\({}_{2}\) under Visible and Solar Light
#### 2.1.4 Coupling TiO\({}_{2}\) with MoS\({}_{2}\) and CdS  
[PERSON] et al. [52] developed a photocatalyst based on a porous TiO\({}_{2}\) monolith with MoS\({}_{2}\) nanosheets and CdS nanoparticles grown on the porous TiO\({}_{2}\). The MoS\({}_{2}\)-CdS-TiO\({}_{2}\) photocatalyst was designed to exploit both CdS and MoS\({}_{2}\) to extend the absorption to visible light and to improve the hydrogen production efficiency. The MoS\({}_{2}\)-CdS-TiO\({}_{2}\) photocatalyst with 3% of MoS\({}_{2}\) and 10% of CdS (molar ratio to TiO\({}_{2}\)) showed a H\({}_{2}\) generation rate of 4146 umol h\({}^{-1}\) g\({}^{-1}\) using Na\({}_{2}\)S and Na\({}_{2}\)SO\({}_{3}\) as sacrificial reagents under visible light. In addition, CdS-TiO\({}_{2}\) and MoS\({}_{2}\)-TiO\({}_{2}\) photocatalysts were also tested. A H\({}_{2}\) generation rate of 875 umol h\({}^{-1}\) g\({}^{-1}\) and 1360 umol h\({}^{-1}\) g\({}^{-1}\) was obtained with CdS-TiO\({}_{2}\) and MoS\({}_{2}\)-TiO\({}_{2}\), respectively. [PERSON] et al. [53] prepared MoS\({}_{2}\)/CdS-TiO\({}_{2}\) nanofibers via an electrospinning-mediated photo-deposition method. The MoS\({}_{2}\)/CdS-TiO\({}_{2}\) photocatalyst, with a loading of 1 wt% of MoS\({}_{2}\), showed a H\({}_{2}\) evolution rate of 280.0 umol h\({}^{-1}\) in a lactic acid aqueous solution under visible light.  
The main works that describes TiO\({}_{2}\)-based photocatalysts modified with non-noble metals, metallic oxides, CdS, and MoS\({}_{2}\) for H\({}_{2}\) production under visible and solar light were summarized in Table 1.
# Using Product Network Analysis to Optimize Product-to-Shelf Assignment Problems
## 4 Implementation and Experimental Results
### Sensitive Analysis
#### 4.3.2 Analysis of Product Relationship Networks  
To understand the characteristics of TBN and CBN, we use five popular indexes: Density, isolated product, diameter, average shortest distance, and clustering coefficient [31]. Density is the number of links divided by the number of total potential links. Diameter is the maximum distance between any pair of nodes in the network. Average shortest distance is the average of all-pairs shortest distance. A clustering coefficient is a measure of the degree to which nodes in a network tend to cluster together  
Figure 8: Number of active links in CBN.  
Figure 7: Number of active links in transaction-based networks (TBN).  
Table 5 shows the values of the five indexes when minimum support count is 5, 10, 15, 20, 25, and 30 for TBN. Table 6 shows the values of the five indexes when minimum support count is 30, 40, 50, 60, 70, and 80 for CBN.  
According to Tables 5 and 6, when the minimum support count is high, the density of the two product networks will be low since there are few active links in the product network. The network density of the two networks is similar when the minimum support count is 15 in TBN and 60 in CBN. However, the number of isolated nodes in CBN is much higher than in TBN since CBN aggregates customer transactions. Similarly, the average shortest distance and diameter in CBN is shorter than in TBN.  
It is clear that the product associations derived from TBN are based on individual transactions. That is, TBN considers each transaction to be equally important so that repeated purchases will be accumulated/counted when conducting association analysis. This makes TBN suitable for stores with fewer customers with higher loyalty. On the other hand, the product associations derived from CBN are based on individual customers. The analysis result derived from CBN will be less affected by customers who repeatedly purchase the same products. Therefore, CBN is suitable for stores with large numbers of one-time customers.
## 3 Results
### Sensorial Evaluation of Vegan Muffins  
To assess the acceptability of the muffins, a sensory analysis was conducted. Figure 5 presents a summary of the results from the evaluation of sensory characteristics, providing the average values for all attributes assessed. It was observed that despite differences in microstructure and texture, there were no significant variations (\(p>0.05\)) in the sensorial evaluation results for all attributes. However, it is worth noting that the muffins made with spelt flour and added microalgae exhibited a darker color and an intense \"grassy\" aroma. These findings demonstrate that microalgae can be successfully utilized in the production of vegan muffins. Similarly, [PERSON] et al. [7] demonstrated that muffins containing 2 and 6% Chlorella, as well as Spirulina, were acceptable to the panelists. Furthermore, [PERSON] et al. [10] confirmed the satisfactory sensory quality of Spirulina bars, which were well-received by school-aged children despite variations in texture.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Muffin Type** & **Hardness** & **Elasticity** & **Cohesiveness** & **Chewiness** \\  & **(N)** & **(-)** & **(-)** & **(-)** \\ \hline CW & 62.55 \(\pm\) 6.76 \({}^{\text{c}}\) & 0.85 \(\pm\) 0.03 & 0.49 \(\pm\) 0.04 & 25.85 \(\pm\) 3.63 \({}^{\text{a}}\) \\ CWa0.5 & 64.65 \(\pm\) 8.28 \({}^{\text{d}}\) & 0.81 \(\pm\) 0.04 & 0.46 \(\pm\) 0.05 & 24.14 \(\pm\) 2.71 \({}^{\text{a}}\) \\ CWa1.0 & 62.17 \(\pm\) 5.10 \({}^{\text{c}}\) & 0.82 \(\pm\) 0.02 & 0.46 \(\pm\) 0.04 & 23.41 \(\pm\) 1.83 \({}^{\text{a}}\) \\ CWa1.5 & 61.75 \(\pm\) 5.86 \({}^{\text{c}}\) & 0.83 \(\pm\) 0.02 & 0.46 \(\pm\) 0.02 & 23.52 \(\pm\) 2.08 \({}^{\text{a}}\) \\ SW & 52.75 \(\pm\) 5.10 \({}^{\text{a}}\) & 0.76 \(\pm\) 0.06 & 0.42 \(\pm\) 0.06 & 16.71 \(\pm\) 2.77 \({}^{\text{b}}\) \\ SWa0.5 & 57.41 \(\pm\) 5.32 \({}^{\text{b}}\) & 0.76 \(\pm\) 0.03 & 0.42 \(\pm\) 0.05 & 18.16 \(\pm\) 1.52 \({}^{\text{b}}\) \\ SWa1.0 & 52.80 \(\pm\) 4.33 \({}^{\text{a}}\) & 0.79 \(\pm\) 0.03 & 0.45 \(\pm\) 0.04 & 18.54 \(\pm\) 1.33 \({}^{\text{b}}\) \\ SWa1.5 & 52.25 \(\pm\) 3.61 \({}^{\text{a}}\) & 0.77 \(\pm\) 0.05 & 0.43 \(\pm\) 0.05 & 17.12 \(\pm\) 2.66 \({}^{\text{b}}\) \\ \hline \multicolumn{5}{c}{ANOVA} \\ \hline Factor & F & \(p\) & F & \(p\) & F & \(p\) \\ \hline Flour type (X) & 100.97 & \textless{}0.001 & 3.27 & 0.072 & 2.93 & 0.088 & 82.50 & \textless{}0.001 \\ Microalgae addition (Y) & 4.28 & 0.006 & 1.48 & 0.221 & 0.61 & 0.609 & 1.02 & 0.387 \\ X \(\times\) Y & 0.74 & 0.531 & 0.55 & 0.652 & 0.99 & 0.399 & 0.81 & 0.492 \\ \hline \end{tabular}
\end{table}
Table 4: Texture parameters of vegan muffins with added microalgae (each value is presented as mean \(\pm\) SD; a, b, c, d—homogenous group, \(\alpha<0.05\)).
## Appendix B The Injection Time  
The phase space density of electrons in a reservoir can be anticipated by assuming that each degree of freedom of an electron needs a phase space area equal to \(2\pi\), which is usually derived by using the single particle wavefunction of electrons as the Bloch states and then introducing the Born-von Karman boundary conditions. The interpretation of this result for two wave packets with spatial dispersion \(\sigma_{x}\) and wave vector dispersion \(\sigma_{k}=1/\sigma_{x}\), center positions \(x_{01}\) and \(x_{02}\), and center wave packets \(k_{01}\) and \(k_{02}\) is simple. When they are far away from each other in the phase space, i.e., \(|x_{01}-x_{02}|>>\sigma_{x}\) or \(|k_{o1}-k_{o2}|>>\sigma_{k}\), the norm of the two-electron wave function is equal to the unity. However, when the wave packets are approaching each other, the probability decreases. In particular, for \(x_{o1}=x_{o2}\) and \(k_{o1}=k_{o2}\), we get \(\psi_{1}(x)=\psi_{2}(x)\) and \(\Phi(x_{1},x_{2})=\psi_{1}(x_{1})\psi_{1}(x_{2})-\psi_{1}(x_{2})\psi_{1}(x_{1})=0\). This is the time-dependent wave packet version of the Pauli exclusion principle (or exchange interaction) mentioned for time-independent Hamiltonian eigenstates.  
For example, for electrons in a 2D space (with the position \(\vec{r}=\{x,y\}\) and wave vector \(\vec{k}=\{k_{x},k_{y}\}\)), we consider a volume of the phase space equal to \(\Delta x\Delta z\Delta k_{x}\Delta k_{z}\), with the degrees of freedom \(\{x_{0},z_{0},k_{x},k_{z}\}\) satisfying \(x_{0}<x<x_{0}+\Delta x\), \(z_{0}<z<z_{0}+\Delta z\), \(k_{x0}<k_{x}<k_{x0}+\Delta k_{x}\) and \(k_{x0}<k_{z}<k_{x0}+\Delta k_{z}\). The total number of electrons in this phase space cell taking into account the properties of a fermion are,  
\[n_{p}=\frac{\Delta x\Delta z\Delta k_{x}\Delta k_{z}}{(2\pi)^{2}}, \tag{12}\]  
where \((2\pi)^{2}\) is the volume occupied by a single electron in the 2D phase space. Then, the time of injection of an electrons in the \(x\) direction from this volume of the phase space will be given by:  
\[\tau_{in}=\frac{\Delta x}{v_{x}n_{p}}=(2\pi)^{2}\frac{1}{v_{x}\Delta z\Delta k _{x}\Delta k_{z}}, \tag{13}\]  
where \(v_{x}\) is the electron velocity in the phase space volume. It can be demonstrated [39] that interpretation of \(\Delta x\) and \(\Delta k_{x}\) in terms of the wave packets mentioned above implies:  
\[\Delta x=\sigma_{x}\sqrt{2\pi}, \tag{14}\] \[\Delta k_{x}=\sigma_{k}\sqrt{2\pi}. \tag{15}\]We notice that the condition \(\sigma_{x}\cdot\sigma_{k}=1\) implies the desired condition \(\Delta x\cdot\Delta k_{x}=2\pi\). Then, the injection time in Equation (13) is just:  
\[\tau_{in}=\frac{\sigma_{x}}{v_{x}}, \tag{14}\]  
which only depends on the properties of the reservoir.
# Novel Designated Ownership Transfer with Grouping Proof
## 3 Security Analysis
### Forward Secrecy  
Assume an attacker can intercept all previously transmitted messages between all communicating parties during the ownership transfer phase. However, without knowing the previous ownership by shared key \(Ky_{i}^{q^{\prime}}\), the new owner (recipient \(P^{q}\)) cannot decrypt the message transmitted between the tags and its former owner (supplier).
## 4 Adaptation
### Use Cases  
As described throughout this work, KIDE4I is intended to be easily adapted to different use cases. In industrial scenarios, most typical interactions for workers are, on the one hand, with information systems to retrieve information about maintenance tasks or access specific information such as blueprints or technical manuals. On the other hand, workers also take part in collaborative tasks with robots, in which both work together towards completing an assignment [35]. In the context of this work, KIDE4I has been adapted to four different use cases that are relevant in current industrial setups considering the typical scenarios cited above: a guide robot--the characteristics of which are analogous to logistics robots--a bin-picking robot, a Computerized Maintenance Management Software (CMMS) and an assistant for procedure execution.
# Separable Data-Hiding Scheme for Encrypted Image to Protect Privacy of User in Cloud
###### Abstract  
As cloud computing becomes popular, the security of users' data is faced with a great threat, i.e., how to protect users' privacy has become a pressing research topic. The combination of data hiding and encryption can provide dual protection for private data during cloud computing. In this paper, we propose a new separable data-hiding scheme for encrypted images based on block compressive sensing. First, the original uncompressed image is compressed and encrypted by block compressive sensing (BCS) using a measurement matrix, which is known as an encryption key. Then, some additional data can be hidden into the four least significant bits of measurement using the data-hiding key during the process of encoding. With an encrypted image that contains hidden data, the receiver can extract the hidden data or decrypt/reconstruct the protected private image, according to the key he/she possesses. This scheme has important features of flexible compression and anti-data-loss. The image reconstruction and data extraction are separate processes. Experimental results have proven the expected merits of the proposed scheme. Compared with the previous work, our proposed scheme reduces the complexity of the scheme and also achieves better performance in compression, anti-data-loss, and hiding capacity.  
separable; data hiding; compressive sensing; Cloud +
Footnote †: journal: Computer Science  
1
# Distributed Edge Computing to Assist Ultra-Low-Latency VANET Applications
## 2 Related Works
In [32], the authors focused on a vehicle-to-vehicle communication system operating at a road intersection, where the communication links can be either line-of-sight (LOS) or non-line-of-sight (NLOS). The authors presented a semi-empirical analysis of the packet delivery ratio of dedicated short-range communication (DSRC) safety messages for both LOS and NLOS scenarios using a commercial transceiver. The authors of [33] critically reviewed the existing methods of adaptive traffic signal control in a connected vehicle environment and compared the advantages and disadvantages of those methods. The proposed architecture provides load balancing between distributed controllers and uses a local view of the network to make better decisions. Both the theoretical model and the simulation results confirmed that the proposed SDVN architecture differs from the existing SDVN with a central controller in terms of latency. The proposed system has the same low latency as the existing VANET.