esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Cavity Vacuum Pressure Diminution on Thermal Performance of Triple Vacuum Glazing
###### Abstract  
Long-term durability of the vacuum edge seal plays a significant part in retrofitting triple vacuum glazing (TVG) to existing buildings in achieving progress towards a zero-energy building (ZEB) target. Vacuum pressure decrement with respect to time between panes affects the thermal efficiency of TVG. This study reports a 3D finite element model, with validated mathematical methods and comparison, for the assessment of the influence of vacuum pressure diminution on the thermal transmittance (U value) of TVG. The centre-of-pane and total U values of TVG are calculated to be 0.28 Wm\({}^{-2}\) K\({}^{-1}\) and 0.94 Wm\({}^{-2}\) K\({}^{-1}\) at the cavity vacuum pressure of 0.001 Pa. The results suggest that a rise in cavity pressure from 0.001 Pa to 100 kPa increases the centre-of-pane and total U values from 0.28 Wm\({}^{-2}\) K\({}^{-1}\) and 0.94 Wm\({}^{-2}\) K\({}^{-1}\) to 2.4 Wm\({}^{-2}\) K\({}^{-1}\) and 2.58 Wm\({}^{-2}\) K\({}^{-1}\), respectively. The temperature descent on the surfaces of TVG between hot and cold sides increases by decreasing the cavity vacuum pressure from 50 kPa to 0.001 Pa. Nonevaporable getters will maintain the cavity vacuum pressure of 0.001 Pa for over 20 years of life span in the cavity of 10-mm wide edge-sealed triple vacuum glazing, and enable the long-term durability of TVG.  
vacuum pressure; triple vacuum glazing; finite element modelling; thermal performance; towards zero-energy buildings +
Footnote †: journal: Applied Physics Letters
## 3 Proposed Cascadable Voltage-Mode Inverse Filters
### Second-Order VM Inverse Filter Realization  
Figure 2b illustrates the proposed realization of the second-order VM inverse filter, which is constructed using the cascade connection of the first-order VM inverse filter in Figure 2a. This topology still exhibits a low output impedance, making it appropriate for cascading in VM operation. The voltage transfer function of the circuit is given by  
\[G_{v2}(s)\ =\ \frac{v_{out2}}{v_{in}}\ =\ \frac{N(s)}{s^{2}C_{1}C_{2}Z_{A}Z_{B}}, \tag{3}\]  
where  
\[N(s)\ =\ s^{2}R_{1}R_{2}C_{1}C_{2}+s(R_{1}C_{1}+R_{2}C_{2})+1. \tag{4}\]  
Similarly, the suitable impedance choices for \(Z_{A}\) and \(Z_{B}\) as specified in Table 4 would lead to the required second-order ILP, IHP, and IBP filter responses.  
Figure 2: Proposed VCII-based VM inverse filters. (a) 1 st-order filter; (b) 2 nd-order filter.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Filter Type**} & \multirow{2}{*}{\(Z_{A}\)} & **Transfer Function,** & **Minimum Gain (\(G_{0}\))** & **Cut-Off Frequency (\(\omega_{0}\))** \\  & & \(G_{v1}(s)\)=\(\frac{V_{out(s)}}{V_{in(s)}}\) & & **(\(G_{0}\))** & **Cut-Off Frequency (\(\omega_{0}\))** \\ \hline ILP & \(\frac{1}{sC_{A}}\) & \(G_{0}(sR_{1}C_{1}+1)\) & \(\frac{C_{A}}{C_{1}}\) & \(\frac{1}{R_{1}C_{1}}\) \\ IHP & \(R_{A}\) & \(G_{0}\left(\frac{sR_{1}C_{1}+1}{sR_{1}C_{1}}\right)\) & \(\frac{R_{1}}{R_{A}}\) & \(\frac{1}{R_{1}C_{1}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Selection of impedance \(Z_{A}\) for the first-order VM inverse filter in Figure 2a.
## 6 Case Studies
### Computational Performance of the MCS-LN Method  
In this subsection, correlation coefficients among uncertainties are set as follows: \(\rho_{\rm EE}=\rho_{\rm GG}=\rho_{\rm HH}\) = 0.5, \(\rho_{\rm WW}=0.6\) and \(\rho_{\rm PP}=0.8\), where the subscripts E, G, H, W and P denote electricity load, gas load, heat load, wind speed and photovoltaic power. Both \(\lambda_{\rm P2G}\) and \(\lambda_{\rm P2H}\) are set to be 0.1, where \(\lambda_{\rm P2G}\) and \(\lambda_{\rm P2H}\) denote the ratio of electrical power consumed by P2G and P2H to the total amount of power generated by wind farms and photovoltaic plants, respectively.  
In order to test and validate the computational performance of the MCS-LN method, a similar method based on MCS with simple random sampling and Nataf transformation, denoted by MCS-SN, is also developed to solve the PEF problem. The PEF solution by the MCS-SN method with a sample size of 30,000 will be used as a reference for comparison.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Wind Farm**} & **Cut-in Speed** & **Cut-out Speed** & **Rated Speed** & **Shape** & **Scale** \\  & **(m/s)** & **(m/s)** & **(m/s)** & **Parameter** & **Parameter** \\ \hline WF 1 & 4 & 30 & 15 & 1.4 & 6.0 \\ WF 2 & 5 & 28 & 14 & 1.8 & 6.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the wind farms.  
Figure 5: The EGH-IES sample.  
The PEF results obtained by the MCS-LN method (with a sample size of 300) and the MCS-SN method (with a sample size of 30,000) are compared, and some results are given in Table 2. The \(\mu_{\text{loss}}\) and \(\sigma_{\text{loss}}\) are the mean value and SD of the active power loss of the electricity network. The \(\mu_{\text{F}}\) and \(\sigma_{\text{F}}\) are the mean value and SD of the gas flowing through pipeline 10-11 (from node 10 to node 11). The \(\mu_{\text{T}}\) and \(\sigma_{\text{T}}\) are the mean value and SD of the supply temperature at node 5 of the heating network, respectively. The relative errors are the errors in percentage between the two methods by taking the results obtained by the MCS-SN method as a reference.  
As shown in Table 2, results obtained by the two methods are almost identical, and the largest relative error is less than 5%, which indicates that the MCS-LN method proposed can achieve high computational precision. In addition, the CPU time of the MCS-SN method and the MCS-LN method are, respectively, 41.85 s and 1285.92 s (both in a 2.5-GHz PC with a CPU of Inter(R) Core(TM) i7-6500U and 4G RAM), which highlights the computational efficiency of the MCS-LN method.
# Background and Recent Advances in the Locata Terrestrial Positioning and Timing Technology
## 4 Locata Applications
### Locata Working Independently, or in Combination with GNSS/INS  
As can be seen from the descriptions above, all of these applications treat Locata as a type of localised RF signal \"constellation\", which is able to provide signal coverage to support high accuracy positioning where GNSS may not be available. Locata can also be tightly-coupled with GNSS due to its accurate external time synchronisation capability. This enhances the geometric strength and measurement redundancy. In early applications using Locata's prototype system, investigations focused on either augmenting the GPS solution with additional Locata measurements or verifying the precise positioning capability of the Locata standalone system. More recently, academic research has focused on integrating Locata with GNSS and INS while using advanced data fusion algorithms and architectures. Multi-sensor integration is necessary for a variety of seamless indoor/outdoor positioning and navigation applications. The Locata technology has several advantages in high accuracy positioning: (1) TimeLoc allows the LocataNet to precisely synchronised (e.g. with respect to a GNSS time scale), so that the integration system can maintain a common time reference; (2) the VRay antenna provides angle measurements, which are rarely available from other navigation sensors; and, (3) the VRay antenna is highly effective in mitigating multipath signal disturbances.
# Model Predictive Evolutionary Temperature Control via Neural-Network-Based Digital Twins
## 2 Materials and Methods
### Dataset  
The training of the digital twin model necessitates a substantial amount of data. In this study, we performed 323 experiments, with each experiment saved as an individual HDF-file. The dataset was split into an 87.5/12.5% training-validation split and the frames were captured at a fixed rate of 1 image per 30 s. Each frame is stored as a grayscale image. The selection of the frame rate was based on preliminary experiments aimed at identifying the system's thermal inertia and response time. A higher frame rate would yield negligible differences between the images, making it challenging for the model to capture the temperature field's evolution. Conversely, longer time intervals may result in the loss of crucial information, such as heat propagation mechanisms and the formation and dissipation of local hot spots.  
Figure 3: Evolution of consecutive frames over the course of an experiment. (**Left**): Mean pixel value change compared to the first steady-state condition. (**Right**): The percentage of pixels with a deviation larger than 3%.  
Figure 2: Visualization of two experiments from the dataset.  
Within each experiment, the first six frames (2 min and 30 s) represent the initial steady state, denoted as \(s_{0}\). This allows for the use of up to six frames as an input sequence, ensuring that all subsequent frames after thermal disturbances can be utilized as the ground truth at least once, maximizing the information within the dataset. Furthermore, to cover the parameter space for heating and cooling loads, we randomly sampled fan settings from a 0 to 100% workload with 20% increments and heating strip loads from 0 to 100% workload with 25% increments. In other words, fan settings and heating loads were randomly sampled from a pool of \(6^{3}\) and \(5^{3}\) possible configurations, respectively. These settings are also saved in the labels of the HDF5-files for post-processing purposes.  
It is important to note that different initial conditions, heat loads, and fan settings influence the behavior of the system. Consequently, each configuration requires a varying amount of time to reach a steady-state operation, leading to variations in the sequence lengths across different experiments. Table 1 summarizes the distribution of experiment durations in the dataset.
# Improving Spaceborne GNSS-R Algal Bloom Meteorological Data
## 2 Datasets  
### Area of Interest  
Cyanobacteria bloom (a kind of algal bloom) is a commonly seen disaster and has caused serious environment problems in Taiwan Lake, China. For this reason, it is chosen as the area of interest (30.9\({}^{\circ}\)-31.6\({}^{\circ}\)N, 119.9\({}^{\circ}\)-120.6\({}^{\circ}\)E) of this paper (shown in Figure 1). Taihu Lake is the third largest freshwater lake in China, located in the densely populated Yangtze River Delta. It experiences a subtropical monsoon climate [33]. Taihu Lake became eutrophic in the 1980s. Later in the 1990s, cyanobacteria bloom started to emerge [2]. The average water depth of Taihu Lake is only about 2 m [34]; the shallow water and large amount of lakebed silt also contribute to the growth of cyanobacteria [35; 36], as they make the lake water warmer and more fertile. Considering that the eastern part of Taihu Lake is the aquatic plant zone, the growth of cyanobacteria and remote sensing observation here are both influenced, so east of the lake surface to 120.24\({}^{\circ}\)E is excluded.  
Figure 1: MODIS image of Taihu Lake on 17 September 2021 and the response of three GNSS-R observables (introduced in Section 3.1), where the lake surface is covered by cyanobacteria bloom or not.  
In this work, information about CYGNSS observation data, auxiliary land component of the fifth generation of European Reanalysis (ERA5-Land) data and reference moderate-resolution imaging spectroradiometer (MODIS) data are introduced as follows.
# A Survey on Machine-Learning Based Security Design for Cyber-Physical Systems
## 3 Taxonomy of Cyber-Physical Attacks
### Network Layer
#### 3.2.2 Flooding Attack  
The flooding attack is defined as an intentional exhaustion of network resources, such as network bandwidth or the memory of network devices, by generating massive amounts of network traffic [76; 77]. When an attacker launches a flooding attack, the massive number of packets generated deprives legitimate communication nodes of transmission opportunities, or they fill up the memory in network devices. As the result of a flooding attack, transmission delays for legitimate packets increase and violate real-time constraints on the CPS. To succeed with a flooding attack, the attacker must simply rapidly generate a massive amount of data over the network, and therefore sophisticated knowledge about the network is not required.  
In control theory, it is known that time-varying delays in a network negatively affect stability and the control performance of the physical system [41; 78]. The effect of the network delay on physical dynamics (1) is represented as follows:  
\[x(k+1)=e^{AT}x(k)+\sum_{j=0}^{\bar{d}-d}\int_{T-\bar{t}_{j+1}^{\bar{t}_{j}}}^{ T-\bar{t}_{j}^{\bar{t}_{j}}}e^{As}dsBu(k+j-\bar{d}), \tag{8}\]where \(T\) is the sensor measurement sampling period of the physical system, \(t_{j}^{k}\) is the \(j\)-th time instance at time step \(k\) with \(0<t_{j}^{k}<T\), while \(\bar{d}\) and \(\bar{d}\) are \(\left\lceil\tau_{max}/T\right\rceil\) and \(\left\lfloor\tau_{min}/T\right\rfloor\), respectively, in which \(\tau_{max}\) and \(\tau_{min}\) are the maximum and minimum time-varying network delay bounds, respectively. The stability of the physical system with a large time-varying delay environment is explained in [41], where the allowable delay bound depends on the system dynamics and controller design. Network delay violates the stability condition; the physical state \(x(t)\) diverges to infinity, and therefore the flooding attack makes the physical system unstable.  
Like the DoS attack, when a flooding attack is launched against the network, physical systems are destabilized or switch into a fail-safe mode that stops or reduces the operation of the physical system to guarantee safety. For UAV control systems in IEEE 802.11-based wireless network environments, although a ground control station (GCS) sends control messages to the UAV, the UAV under an internet control message protocol (ICMP) flooding attack simply hovers in place, because the control-related packets are not received by the pre-configured transmission deadline due to the delay induced by ICMP flooding [79].
## 5 Algorithm Design  
Targeting large-scale satellite-terrestrial integrated network scenarios, we propose a multi-modal deep reinforcement learning-based proactive remote sensing data delivery algorithm (Multi-modal-MAPPO). By integrating multi-modal data--including remote sensing imagery, attribute data, and user/satellite network cache states--and combining multi-agent collaboration mechanisms, the algorithm achieves high-precision and low-latency data distribution in dynamic network environments. The core design is elaborated through three key aspects: multi-modal feature modeling, cross-modal dynamic fusion, and collaborative decision optimization.
## 3 Materials and Methods
### Sequential Pattern Mining  
The sequential pattern mining problem aims to extract temporal correlations hidden from a sequences dataset. To better understand the sequential pattern mining problem, we present some key definitions in this section.  
**Definition 1** (Item and itemset).: _An item 1 is a literal value for purchase categories. An itemset, \(IS=(I_{1}\ I_{2}\ldots I_{u})\), is a non-empty set of items such that \(I_{i}\in dom(items)\ \forall\ i\in[1..u-1]\)._  
**Definition 2** (Inclusion of itemsets).: _An itemset \(IS=(I_{1}\ I_{2}\ldots I_{u})\) is included, denoted \(\subseteq\), in another itemset \(IS^{\prime}(I^{\prime}_{1}\ I^{\prime}_{2}\ldots I^{\prime}_{v})\), iff \(\forall I_{k}\in IS\), \(\exists\ i_{k}\), such that \(I_{k}=I^{\prime}_{i_{k}}\)._  
**Definition 3** (Sequence).: _A sequence \(\mathsf{S}\) is an ordered list of itemsets, denoted \(S=[IS_{1}\ I_{2}\ldots IS_{v}]\) where \(IS_{i}\), \(IS_{i+1}\) satisfy the constraint of temporal sequentiality for all \(i\in[1..v-1]\)._  
**Definition 4** (Inclusion of sequences).: _A sequence \(S=[IS_{1}\ \ \ I_{2}\ldots IS_{u}]\) is included in another sequence \(S^{\prime}=[IS^{\prime}_{1}IS^{\prime}_{2}\ldots IS^{\prime}_{v}]\), denoted as \(S\subseteq S^{\prime}\), iff \(\exists\ i_{1}<i_{2}<\ldots<i_{u}\) such that \(IS_{1}\subseteq IS^{\prime}_{i_{1}},IS_{2}\subseteq IS^{\prime}_{i_{2}},\ldots, IS_{u}\subseteq IS^{\prime}_{i_{u}}\)._  
**Definition 5** (Support of a sequence).: _We define the support of a sequence \(S\), denoted as \(supp(S)\), as the number of sequences in the database \(sDB\) that include \(S\)._  
**Definition 6** (Problem of sequential pattern mining).: _Given a positive integer \(\sigma\) (minimal support) and a sequence in the database \(sDB\), a sequence can be considered frequent if its support \(supp(S)\) is greater than or equal to \(\sigma\), i.e., \(supp(S)\geq\sigma\). All frequent sequences are called sequential patterns, and they are stored in a pattern database \(pDB\)._  
Several algorithms have been proposed to tackle the problem of extracting sequential patterns [16; 17; 18]. These algorithms can be divided into two main strategies: Depth-First Search (DFS) and Breadth-First Search (BFS). BFS-based techniques [17] discover patterns by level, in which a pattern is built combining patterns of size \(k\) with patterns of size \(k+1\). For each level, patterns should appear more than a threshold called the minimal support.
# A Review of the Use of Native and Engineered Probiotics for Colorectal Cancer Therapy
## 3 Genetically Modified-Bacteria for Colorectal Cancer (CRC) Treatment
### Engineered Bacteria Treat CRC via Therapeutic Protein Delivery  
#### 3.1.1 Cytotoxic Proteins  
Cytotoxic proteins, a kind of biological toxin, are produced by a variety of different organisms and are poisonous to animal cells. Killing CRC cells via cytotoxin is a feasible method of CRC treatment. However, because of a lack of selectivity for protein toxins, normal cells may be accidentally injured. Bacteria can specifically colonize tumor sites, which provides an opportunity for toxic proteins to directly contact tumor cells and exert their toxic effects [55; 56]. The cytolysin A (ClyA) protein, also referred to as hemolysis E (HlyE), is a pore-forming cytotoxin expressed by _E. coli_ and enterobacteria (Figure 2). It can cause the formation of transmembrane pores on mammalian cells and macrophages and induce cell apoptosis [57; 58]. A study described that engineered _E. coli_ K-12-expressing ClyA could significantly decrease tumor growth rates, and a combination of therapy and radiation was effective in suppressing tumor growth and metastasis in a CT26 mouse model [59]. Recently, a biohybrid therapeutic platform based on _E. coli_ K-12 was developed to express ClyA. This treatment, in combination with photothermal therapy, significantly inhibited tumor growth for BALB/c mice bearing CT26 tumors [60]. In another example, engineered _E. coli_ K-12-secreting ClyA arrested tumor growth and impeded tumor metastasis by triggering thrombosis and killing tumor cells in CT26 tumor tissues [61]. Another approach involved using an engineered attenuated _S. typhimurium_\(\Delta\)ppGpp strain as a bacteria chassis to express ClyA. This strategy could significantly suppress both primary and metastatic tumors and prolong survival in CT26 tumor-bearing mice [62; 63]. Notably, a recent study employed an _E. coli_ Nissle 1917 (EcN) strain as a chassis to deliver the protein toxin HlyE. The engineered EcN strain was designed to enable the expression of HlyE in a temporally controlled manner under the control of the araBAD promoter. As a result, this engineered strain preferred to be colonized in tumor tissues and induced tumor regression in mice xenografted with either human CRC cells [64]. Other types of cytotoxic proteins have also been tested for their anti-CRC efficacy. TGF\(\alpha\)-PE38 is a cytotoxic protein comprising transforming growth factor alpha (TGF\(\alpha\)), an EGFR ligand, and a modified _Pseudomonas_ exotoxin A (PE38), a potent cytotoxic protein. Attenuated _S. typhimurium_ armed with a recombinant TGF\(\alpha\)-PE38 fusion protein led to notable retardation in solid tumor growth observed in BALB/c mice bearing CT26 tumors, C57 BL/6 mice bearing MC38 tumors, and nude mice bearing SW620 tumors [65].
## 1 Introduction
Additionally, the band bending formed between QDs and 2D materials due to the heterojunction structure can facilitate the efficient separation of photogenerated charges, reducing recombination probability and thus enhancing the photodetection performance. Therefore, a combination of the two can effectively broaden the detection range and improve the responsivity of photodetection. In this work, we present the construction of a type-II Bi\({}_{2}\)O\({}_{2}\)Se/PbS QDs hybrid heterojunction by using chemical vapor deposition and spin-coating. Comparative analysis with the 2D Bi\({}_{2}\)O\({}_{2}\)Se photodetector reveals a substantial enhancement in photoresponsivity for the hybrid heterojunction, achieving a remarkable 14.89 A/W at 450 nm. Furthermore, the Bi\({}_{2}\)O\({}_{2}\)Se-PbS QD-based photodetector demonstrates broadband photoresponsivity spanning from visible (405 nm) to near-infrared (1350 nm) light illumination. Notably, compared to the bare Bi\({}_{2}\)O\({}_{2}\)Se photodetector, the hybrid photodetector exhibits significantly reduced photocurrent response and recovery times.
# An In-Depth Review on Blockchain Simulators for IoT Environments
## 2 Background
### IoT Technology  
IoT, which is known as the Internet of Things, simply refers to the physical objects and devices that are capable of connecting and exchanging data with other devices through the internet or other communication networks. IoT is one of the most important areas of future technology and is beginning to be implemented in multiple industries [17]. IoT devices are now not only able to connect to a network and communicate with one another but are also capable of collecting data from the environment and sharing that data to other devices for analytics, applications, and communication [18]. This is important for creating a smart environment as the connection of multiple devices and sharing information is a must.  
A few examples of IoT being explored to be used in unexpected parts of our lives are transportation systems [19] and in supply chain operations [20]. When it comes to transportation systems, it has been suggested that we use sensors on vehicles, roads, and infrastructure to collect and store huge amounts of data, collectively known as big data. Using this big data traffic control, road conditions, and scheduled travel time can all be viewed and managed with the data collected by the IoT sensors [19]. Similarly, in supply chains, there have been some proposals to use machine learning and artificial intelligence algorithms in conjunction with smart sensors to monitor and collect data on remote equipment and provide suggestions on when maintenance is needed [20]. While both of these examples are not in use yet, we see a common trend of using IoT devices to collect large amounts of data and process them into more useful information. Implementing blockchain technology into the IoT environment has the potential to make collecting, storing, and sharing data more efficient than other proposed technologies. In addition, it may lower cost when it comes time to implement IoT technologies.