esa-sceva/satcom-chunk-collection huggingface dataset:
# A Membrane Modified with Nitrogen-Doped TiO2/Graphene Oxide for Improved Photocatalytic Performance
## 3 Results and Discussion
### The Flux Change Analysis of the Modified Membrane  
The pure water membrane flux was obtained by the experimental self-made flux device. As shown in Figure 7, the pure water flux of the PVDF membrane was 530 Lm\({}^{-2}\)h\({}^{-1}\) and the pure water flux of the NTG/PVDF membrane reached 1672 Lm\({}^{-2}\)h\({}^{-1}\). Compared to the PVDF membrane, the pure water flux of the NTG/PVDF membrane increased by 3.15 times because the doping of nitrogen and GO increased the hydrophilicity of the TiO\({}_{2}\). The composite microfiltration membranes were prepared by the surface coating method. The photocatalysts were attached to the surface of the membranes in a dispersed state and no nanoparticle adhesion was observed inside the pores of the membranes because the specific surface area in the pore size was large and the surface tension of the membrane was also relatively large. Therefore, the nanoparticles could not enter the pores and few nanoparticles adhered in the pores of the membrane.  
The MB solution flux of the modified microfiltration membrane was tested under dark, ultraviolet and sunlight conditions. The relative flux changed, as shown in Figure 8a. In the dark, a sharp drop in  
Figure 6: The infrared spectroscopy analysis of the (**a**) PVDF membrane and the (**b**) NTG/PVDF membrane.  
Figure 7: The pure water flux of the membrane.  
pure PVDF membrane was observed during MB filtration. As shown in Figure 8b, the MB solution flux of all the membranes under ultraviolet light was significantly improved when compared to the darkness condition since the photodegradation of the MB inhibited membrane fouled during the filtration process. In particular, the NTG/PVDF membrane flux had a maximal increase, which was consistent with its excellent photocatalytic activity and hydrophilicity. The MB solution flux of all membranes in sunlight was shown in Figure 8c. Due to the slightly poor photocatalytic effect in sunlight, the TiO\({}_{2}\)/PVDF membrane and the NT/PVDF membrane showed only a slight flux increase compared to their performance in the dark. The relative flux of the NTG/PVDF membrane and TG/PVDF also decreased slowly. The penetration effect of the NTG/PVDF membrane on the MB was slightly better because the photocatalytic activity of the NTG/PVDF membrane was stronger than that of the TG/PVDF membrane. In addition, pure PVDF membranes showed similar flux declines in the dark, as well as in ultraviolet light and sunlight, due to membrane fouling, indicating that pure PVDF membranes have little photocatalytic activity under ultraviolet light and sunlight.
# Collecting Sensed Data with Opportunistic Networks: The Case of Contact Information Overhead
## 1 Introduction  
Opportunistic networks (OppNets) are based on the store-carry-forward (SCF) communication paradigm, in which data-bundles (or messages) are stored in device memory, physically carried from one point to another as the device moves about, and forwarded through available wireless communication interfaces (e.g., Bluetooth or Wi-Fi) when devices encounter each other (i.e., are within radio transmission range). This way, a message generated at a source can be delivered to its destination through multiple hops. OppNets therefore provide economical, infrastructure-less and mobile communication that involves delay-tolerant information. OppNets have been envisioned for advertisements in the form of digital word-of-mouth services, recommendation and opportunistic trading [1; 2; 3], as well as communication in the aftermath of large-scale disasters [4; 5; 6]. The following section presents another envisioned application of OppNets towards realizing smart cities.
## 2 Cross-Domain Collaborative Task Allocation Model
### Mathematical Model of Target Allocation
#### 2.3.4 Mathematical Model Construction of Target Allocation  
Objective assignment is a multi-objective combinatorial optimization problem. Because the above three objective functions have different dimensions and orders of magnitude, in order to unify the data scale and increase the speed of model convergence, the data correlation must be eliminated by data normalization methods. Firstly, the min-max normalization method was used to process the objective function value of each objective assignment scheme. Then, the analytic hierarchy process (AHP) was used to weight each objective function value according to the importance of the elements and expert experience, and the linear weighted sum method was used to combine the above three preprocessed objective function values to obtain the fitness of each objective allocation scheme.  
The mathematical model and the constraint conditions of the constructed objective allocation are shown in Equation (6), respectively:\[\min\bigg{\{}\sum\limits_{i=1}^{3}\rho_{i}\overline{P}_{i}\bigg{\}}=\min \Bigg{\{}\rho_{1}\times(-\sum\limits_{j=1}^{c}v_{j}\times E_{j}\times(1-\prod \limits_{i=1}^{d}(1-p_{ij})))+\rho_{2}(\sum\limits_{i=1}^{d}\sum\limits_{j=1}^{ c}\frac{D_{ij}}{\overline{v}})+\rho_{3}(\sqrt{\sum\limits_{n=1}^{a}\left[\frac{S(n)}{r_{n}} \right]^{2}})\Bigg{\}}\] (6) s.t. \[\left\{\begin{array}{l}\text{1}\sum\limits_{i=1}^{d}\sum\limits_{j=1}^{ c}x(i,j)\leq d\\ \text{2}\sum\limits_{j=1}^{c}x(i,j)=1,i=1,2,\cdots,d\\ \text{3}\sum\limits_{i=1}^{d}x(i,j)\leq m,j=1,2,\cdots,c\\ \text{4}D_{ij}\leq D_{M}\end{array}\right.\]  
where 1 indicates that the total number of load agents in each allocation scheme is limited to no more than \(d\); 2 indicates that each load agent can only be assigned to one target in an allocation; 3 indicates that each target agent is limited to be hit by, at most, \(m\) load agents at the same time to prevent a waste of resources. In the actual calculation process, combined with the attack ability of our load agent, \(m\) was taken as 15; 4 indicates that the distance between the load agent \(M_{i}\) and the target agent \(T_{j}\) is less than the theoretical maximum range of the load agent.
## 4 Bogoliubov-de Gennes Equations and Their Semiclassical Approximation  
The fluctuation operator can be written as:  
\[\hat{\eta}(\mathbf{r},t)=\sum_{j}\Big{[}u_{j}(\mathbf{r})e^{-iE_{j}t/\hbar} \hat{a}_{j}+v_{j}(\mathbf{r})e^{iE_{j}t/\hbar}\hat{a}_{j}^{+}\Big{]}\, \tag{12}\]  
where \(\hat{a}_{j}\) and \(\hat{a}_{j}^{+}\) are bosonic operators and the real functions \(u_{j}(\mathbf{r})\) and \(v_{j}(\mathbf{r})\) are the wavefunctions of the quasi-particle and quasi-hole excitations of energy \(E_{j}\)[23]. As a consequence, one finds:  
\[\tilde{n}(\mathbf{r})=\sum_{j}\Big{[}\Big{(}u_{j}(\mathbf{r})^{2}+v_{j}( \mathbf{r})^{2}\Big{)}\langle\hat{a}_{j}^{+}\hat{a}_{j}\rangle+v_{j}(\mathbf{ r})^{2}\Big{]}\, \tag{13}\]  
and:  
\[\langle\hat{a}_{j}^{+}\hat{a}_{j}\rangle=\frac{1}{e^{E_{j}/k_{B}T}-1} \tag{14}\]is the Bose factor at temperature \(T\) with \(k_{B}\) the Boltzmann constant. We stress that at zero temperature, one gets:  
\[\hat{n}(\mathbf{r})=\sum_{j}v_{j}(\mathbf{r})^{2}\, \tag{15}\]  
and also:  
\[\hat{m}(\mathbf{r})=\sum_{j}u_{j}(\mathbf{r})\ v_{j}(\mathbf{r}). \tag{16}\]  
By inserting Equation (12) into Equation (11), we obtain the Bogoliubov-de Gennes equations:  
\[\hat{L}u_{j}(\mathbf{r})+g\psi_{0}(\mathbf{r})^{2}v_{j}(\mathbf{r}) = E_{j}u_{j}(\mathbf{r})\, \tag{17}\] \[\hat{L}v_{j}(\mathbf{r})+g\psi_{0}^{*}(\mathbf{r})^{2}u_{j}( \mathbf{r}) = -E_{j}v_{j}(\mathbf{r}). \tag{18}\]  
where:  
\[\hat{L}=-\frac{\hbar^{2}}{2m}\
abla^{2}+U(\mathbf{r})-\mu+2g|\psi_{0}( \mathbf{r})|^{2}. \tag{19}\]  
The solution of these equation can be done numerically by choosing the external potential \(U(\mathbf{r})\). However, an analytical solution can be obtained within the semiclassical approximation, where \(-i\boldsymbol{\
abla}\rightarrow\mathbf{k}\) and \(\sum_{j}\rightarrow\int d^{3}\mathbf{k}/(2\pi)^{3}\)[23]. It follows that the Bogoliubov differential equations become algebraic equations:  
\[L_{\mathbf{k}}(\mathbf{r})\ u_{\mathbf{k}}(\mathbf{r})+g\psi_{0} (\mathbf{r})^{2}v_{\mathbf{k}}(\mathbf{r}) = E_{\mathbf{k}}(\mathbf{r})\ u_{\mathbf{k}}(\mathbf{r})\, \tag{20}\] \[L_{\mathbf{k}}(\mathbf{r})\ v_{\mathbf{k}}(\mathbf{r})+g\psi_{0} ^{*}(\mathbf{r})^{2}u_{\mathbf{k}}(\mathbf{r}) = -E_{\mathbf{k}}(\mathbf{r})\ v_{\mathbf{k}}(\mathbf{r})\, \tag{21}\]  
where:  
\[L_{\mathbf{k}}(\mathbf{r})=\frac{\hbar^{2}k^{2}}{2m}+U(\mathbf{r})-\mu+2g| \psi_{0}(\mathbf{r})|^{2}\, \tag{22}\]  
and that the zero-temperature non-condensed density reads:  
\[\hat{n}(\mathbf{r})=\int\frac{d^{3}\mathbf{k}}{(2\pi)^{3}}v_{\mathbf{k}}( \mathbf{r})^{2}. \tag{23}\]  
This quantity is also called local quantum depletion of the Bose-Einstein condensate. In addition, the local anomalous density is given by:  
\[\hat{m}(\mathbf{r})=\int\frac{d^{3}\mathbf{k}}{(2\pi)^{3}}u_{\mathbf{k}}( \mathbf{r})\ v_{\mathbf{k}}(\mathbf{r}). \tag{24}\]
## 4 UbiPriSEQ: The Proposed Framework
### Device Module (DevM)
#### 4.3.2 DevM: PrivacyMetric  
The PrivacyMetric \(Pri_{d}(s_{t},a_{t})\) for the state \(s_{t}\) and action \(a_{t}\) at time \(t\) is defined in Equation (1). The variable \(g_{t}\) is the number of tasks generated, \(o_{t}\) is the number of tasks to be transmitted, \(w_{l}\) (**PrivacyWeight**) is the weight that determines the relative importance of the location privacy compared to the usage-pattern privacy, and \(h_{t}\) indicates the channel state. The channel state, \(h_{t}\), is modeled as a Markov process with two states \(\{0,1\}\). The variable \(w_{l}\) takes the value \(w,\ \forall w\in\mathbb{R}^{+}\) (the required level of usage pattern privacy), when the channel state is \"bad\", otherwise it is zero for \"good\" state (see Reference [44]). Simply put, under 'good' channel state, PrivacyMetric is the absolute difference of all the computational tasks produced by a device and the tasks offloaded to fog or cloud.  
\[Pri_{d}(s_{t},a_{t})\triangleq|g_{t}-o_{t}|+w_{l} \tag{1}\] \[w_{l}=\begin{cases}0,\text{if}\ h_{t}==0\\ w,\text{if}\ h_{t}==1,\ \forall w\in\mathbb{R}^{+}\\ \end{cases}\]  
DevM maintains **usage pattern privacy** and breaks any patterns in PrivacyMetric value over time by adding PrivacyWeight and intentionally delaying offloading to avoid attackers detecting a usage pattern. DevM is able to hide usage pattern and hence attackers are unable to detect the device's location which could have been compromised due to the device's connection with a known or rogue node or base station (using distance or trilateration). DevMPrivacyMetric estimates the **usage pattern privacy** and **location privacy**.
## 5 Biomarkers of Response and/or Resistance to Immune Checkpoint Inhibitors
### Clinical Markers of Immune Checkpoint Inhibitors Activity--Immune-Related Adverse Events  
Patients receiving immunotherapy frequently experience adverse events (AEs), with severe (grade \(\geq\) 3) treatment-related AEs occurring in 20-50% of those treated with anti-PD-1/anti-PD-L1 monotherapy [9; 40; 42; 43; 49]. Toxicity is even more frequent with ICIs combinations [9; 49; 50]. Typical toxicities of ICIs are immune-related AEs (irAEs), which are similar across different drugs of this class, and affect several organs, most frequently the skin, gastrointestinal tract, liver, lung, and endocrine glands [127]. Severe irAEs occur more frequently in patients treated with anti-CTLA4 monotherapy compared to those receiving anti-PD-1/anti-PD-L1 monotherapy (25% vs. 10-20%, respectively) [41; 42; 43; 128]. When anti-PD-1/anti-PD-L1 and anti-CTLA4 antibodies are combined, immunotoxicity is cumulative, as demonstrated by the high percentage (around 50%) of patients requiring corticosteroids, an indicator of severe irAEs, in the phase II study evaluating nivolumab + ipilimumab [47]. Differently, when anti-PD-1/anti-PD-L1 antibodies are combined with anti-angiogenic agents, toxicities are addictive and mostly non-overlapping [7; 50].  
Interestingly, as found for TKI treatments [129], the development of irAEs heralds greater clinical benefit [73]. In particular, the development of treatment-related AEs graded \(\geq\) 2 was independently associated with longer OS and PFS in a derivation cohort of HCC patients treated with ICIs therapy as a part of clinical trials submitted to FDA [73]. In the same study, these results were confirmed in an independent validation multicenter cohort. Therefore, the development of irAEs probably reflects a higher drug-induced immune activation directed against the tumor. However, when also considering the conflicting results observed in other tumor types [127], the association between irAEs emergence and higher ICIs efficacy remains to be firmly established in HCC patients.
# Investigation of Ytterbium Incorporation in Lithium Niobate for Active Waveguide Devices
## 4 Summary  
The diffusion of Yb in LiNbO\({}_{3}\) can be described by [PERSON]'s law of diffusion. The experimental values determined in this work reveal an anisotropic diffusion coefficient with higher values for the diffusion along the crystallographic c-axis (z-axis). No anisotropy was found for the (maximum) solubility of Yb in LiNbO\({}_{3}\). However, the solubility is temperature-dependent. When the diffusion reservoir is not exhausted, small grains of Yb remain on top of the surface, leading to high surface roughness, which has to be strictly avoided when fabricating optical waveguides. Absorption and fluorescence measurements for Yb diffusion-doped ridge waveguides are in good agreement with results published for volume-doped bulk substrates. These results are essential for the future development of efficient Yb-based laser sources in LiNbO\({}_{3}\) ridge waveguides.  
Investigation C.E.R., D.B., and S.S.; writing--original draft preparation, C.E.R.; writing--review and editing, D.B., S.S., and D.K.; supervision, D.K.; project administration, D.K.; funding acquisition, D.K. All authors have read and agreed to the published version of the manuscript. This research was funded by Deutsche Forschungsgemeinschaft (grant DFG Ki482/17-1). The authors declare no conflict of interest.  
Figure 8: Polarization-dependent absorption (**a**) and fluorescence (**b**) spectrum of a 10 \(\upmu\)m wide and 2.2 cm long Yb:Ti:LiNbO\({}_{3}\) ridge waveguide.
## 2 Methodology
### The Mobile Robot System  
This paper describes a mobile robot system that uses a learning algorithm based on SNN. The algorithm employs dopamine-modulated STDP for robot navigation. The system also includes two ultrasonic sensors and two color detection sensors for obstacle detection and target identification. Sensory signals are encoded into sensory neurons, and motor neurons control the movement of the robot. Through training, the synaptic weights between the sensory and motor neurons are modified to enable the robot to navigate and avoid obstacles in unknown environments. Previous studies have also explored using neural networks for mobile robot navigation and obstacle avoidance. For example, [PERSON] [28] used a multi-layer neural network to map sensory inputs to motor commands for navigation.  
Similarly, [PERSON] et al. [29] developed a neural network-based controller using sonar sensors. [PERSON] and [PERSON] [30] used a neural network approach with a laser rangefinder sensor for obstacle detection and avoidance. Mobile robot systems commonly use various sensors, such as rangefinders, sonar, and visual sensors, to perceive the environment and avoid obstacles. Using multiple sensors allows the robot to gather more comprehensive data and make better decisions about its movement. Incorporating neural networks into mobile robot navigation enables more precise and efficient control by adapting the behavior of the robot based on its experiences.  
Our work focuses on a two-dimensional (2D) robot design in this initial phase. Figure 1 illustrates this design, showcasing a two-wheeled mobile robot with separate velocities for its right (V\({}_{\text{R}}\)) and left (V\({}_{\text{L}}\)) wheels. The robot's orientation is represented as (\(\Theta\)), and the distance between its wheels is denoted as 'b'. This visual representation effectively conveys the kinematic behavior of the robot. Within the 2D environment, static rectangular obstacles are introduced randomly, adding complexity to the robot's pathfinding task. The primary objective of the robot is to autonomously navigate to a specific destination, called the \"Target\" or \"Goal\" location. The robot commences its mission from a predetermined \"Start\" location. An essential performance metric is the navigation time, which quantifies the duration required for the robot to successfully traverse from the initial \"Start\" point to its intended \"Goal\" destination.
## 2 Related Works
### Fractional Frequency Reuse  
Many studies have shown that the utilization of cellular resources in fractional form for unicast D2D communications improves the utilization of available resources in a cellular network [21]. In the FFR method, the whole area of a cell is divided into two non-overlapping regions: A region near the eNB known as the cell inner region and a region far away from the eNB known as the cell outer region. The FFR scheme assures that the users of the adjacent cell do not interfere with each other while reusing the cellular frequency by the D2D pairs. Co-channel interference and inter-cell interference are the main challenges while reusing cellular frequency resources in D2D communications underlaying cellular networks. This type of interference cannot be mitigated if there is an absence of a dedicated spectrum band for each section of the cell. For example, allocating a dedicated spectrum to users close to the eNB and allocating a different spectrum band to users far away from the eNB.  
In the literature, various methods have been discussed to mitigate these interferences. In [22], the authors proposed an SC-FDMA-based resource allocation scheme for D2D communication. In their work, the frequency reuse factor of three was analyzed to increase the system spectrum efficiency and capacity. The disadvantage of this scheme is that the system throughput goes to a steady state with the increase of the number of D2D pairs. In [23], the authors proposed a scalable D2D communications with a frequency reuse much greater than 1. In their study, scalable admission and power control methods were analyzed. The results showed that their proposed methods improve the network spectral efficiency. However, the network model considered only an omnidirectional antenna, thus it cannot mitigate inter-cell interference. The systematic frequency reuse mechanism with a frequency reuse factor of 2 was proposed in [24]. In this study, the authors focused on the distance based resource allocation and power control phenomenon. Comparing the result of the proposed resource allocation scheme with the FFR scheme for the frequency reuse factor of 1 improves the spectrum efficiency. An FFR scheme for D2D communication underlaying a cellular on wireless multimedia sensor networks was proposed in [25]. The authors discussed the non-orthogonal use of a cellular link by the D2D users to improve system throughput. In their network, the cell coverage was divided into six zones for interference management and the results showed that their proposed scheme improved the throughput and minimized the inter-cell interference.
## 1 Introduction
Currently, research on nanomodified transformer oil in domestic and international studies primarily focuses on normal and high-temperature environments, while the insulation properties of nanomodified transformer oil under extremely cold conditions remain to be investigated. Due to adverse environmental and weather factors in the northern part of China during winter, the performance of transformer oil is greatly affected. The influence of temperature on transformer oil cannot be ignored, as both high and low temperatures can cause significant fluctuations in breakdown voltage [40; 41; 42]. Low temperatures not only affect the breakdown voltage of transformer oil but also have a significant impact on other parameters, such as electrical conductivity and relative permittivity, apart from the breakdown voltage [43; 44; 45]. The most direct effect is a decrease in breakdown voltage, which leads to transformer failures and potentially disastrous consequences for substations and the entire power system [46; 47]. Under specific conditions, while low temperatures certainly have a significant impact on the performance of transformer oil, ultimately, the key factor is the water content in the oil. Water tends to precipitate at 0 \({}^{\circ}\)C and solidifies into ice below 0 \({}^{\circ}\)C. In the oil, water exists in the form of tiny ice crystals. In theory, when the oil is contaminated with impurities, the electric field distribution in the oil is affected, leading to a significant decrease in breakdown voltage [48; 49; 50]. In order to investigate the applicability of nanomodified transformer oil under extremely cold conditions, 45\(\mu\) transformer oil was selected as the base liquid in this study. Spherical anatase TiO\({}_{2}\) nanoparticles were added to the base oil to prepare nanomodified transformer oil. Breakdown tests were conducted under different water content, temperature conditions, and electric field types to investigate the breakdown characteristics of TiO\({}_{2}\) nanomodified transformer oil. Furthermore, the polarization process of nanoparticles in a uniform electric field and the influence of charged particle trajectories in oil were simulated using COMSOL. The impact of polarized nanoparticles on the trajectories of charged particles in oil was investigated, aiming to further validate the mechanism of how nanoparticles in oil affect the breakdown voltage of oil gaps.
## 1 Introduction
Next, the size of the reflectarray panel is increased (and consequently the number of reflectarray elements) and synthesized, taking as a starting point the results of the previous stage. Thus, the reflectarray is iteratively synthesized in different stages until it reaches the desired size of the reflectarray panel. Since the size of the reflectarray is increased in each stage, the number of degrees of freedom is also increased in each stage. In this procedure, fewer local minima (trap points) are expected. However, this idea is not practical since modifications to the geometry of a reflectarray have to be made in each stage. In [5], this idea was revisited without requiring increments in the size of the reflectarray panel. Instead of using different sizes for each stage, different illuminations from the feed horn are used. The effect of decreasing the size of the antenna is the same as decreasing the illumination at its edges. However, in [5], this strategy for the intersection approach using sequential stages with different illumination levels was only carried out with the conventional MAP (without taking into account possible trap points). Moreover, the mathematical formalism which supports this strategy has never been shown. In this work, we revisit this synthesis technique with sequential stages supported by mathematical formalism, and the results are shown using the algorithm proposed in [9] to avoid trap points. The organization of this work is as follows. In the next section, the mathematical formalism which supports the statement of a reduction in edge illumination involving fewer trap points will be described. In Section 3, the process of the synthesis-only phase through sequential stages with different illuminations will be described. In Section 4, the results when using the proposed algorithm in [9] will be compared with results obtained using the same algorithms with sequential stages having different illumination levels. Finally, in Section 5, some conclusions will be presented.