esa-sceva/satcom-chunk-collection huggingface dataset:
# The Electric Vehicle Routing Problem with Time Windows, Partial Readers, and Parcel Lockers
## 5 Experimental Results
### Test Instances  
This section generates EVRPTW-PR-PL instances based on EVRPTW instances proposed by ref. [11]. There are 36 small-size instances (i.e., 12 instances with 5, 10, and 15 customers) and 56 large-size instances with 100 customers. These instances are modified to address three types of customers and parcel locker facilities in our problem. The instances are categorized into three classes based on the distribution of customer locations, i.e., clustered (C), randomly distributed (R), and combined clustered and randomly distributed (RC). Furthermore, instances in each class can be divided into two subsets with different widths of time windows, resulting in six subsets, namely C-1, C-2, R-1, R-2, RC-1, and RC-2. The subsets R-1, C-1, and RC-1 have shorter time windows compared with subsets R-2, C-2, and RC-2. In particular, based on [45], we randomly assign each customer to be one of three types of customers (i.e., home delivery, self-pickup, and flexible customers) with an equal probability. There are \(\left\lceil\frac{|\mathbb{C}|}{\Delta\theta}\right\rceil\) parcel locker locations generated for each instance. Each parcel locker is randomly located within the range between the smallest and largest values of the x- and y-coordinates of customers. Parcel locker's service time is set to half of the customer's service time, and their time window is set the same as the depot. Regarding the self-up method, for self-pickup and flexible customers, the parcel lockers nearest them are assigned to be the designated parcel lockers.
# The Pin Structure Design of LaVai-1.5-1400 Microwave Vertical Tubular Furnace Based on COMSOL
## 3 Modeling
### Geometric Model  
The microwave heating device mainly consists of a waveguide, a flare, and a cylindrical heating cavity, and the microwave source transmits TE\({}_{10}\) mode transverse electromagnetic waves to the resonant cavity through the rectangular waveguide. The resonant cavity is a multi-mode resonant cavity, which mainly consists of a flare and a cylindrical heating cavity with a height of about 550 mm and a radius of about 75 mm. The cylindrical heating chamber is divided into four layers, namely multite insulation I, refractory glass insulation, multite insulation II, and heating layer. In the center of the heating layer, a crucible with a radius of 16 mm, a height of 63 mm, and a wall thickness of 3 mm is placed. At the bottom of the crucible, a circular glass base with a radius of 15 mm and a thickness of 5 mm is  
Figure 1: Schematic diagram of the internal structure of microwave heating.  
constructed on which the Al\({}_{2}\)O\({}_{3}\)/SiC composite material to be heated is placed, and the upper and lower parts of the crucible are filled with mullite insulation. The geometric model of the microwave heating unit is shown in Figure 2:  
The electromagnetic and thermophysical parameters of the air and the metal wall are built into COMSOL Multiphysics and can be called directly. The manually entered material parameters in the simulation are given in Table 1 below:
# Scalable Block Preconditioners for Linearized Navier-Stokes Equations at High Reynolds Number
## 2 Problem Setting and Discretization
### Stokes Equations  
Define the Reynolds number as the ratio between inertial and viscous forces, namely \(Re=\frac{UU}{\
u}\), where \(L\) is a characteristic length of the domain \(\Omega\) and \(U\) is a representative velocity scale of the fluid. It turns out that, if the Reynolds number is sufficiently small (i.e., if the flow is particularly slow or if the viscosity is high enough), then the Navier-Stokes equations can be simplified: in fact, the term \((\mathbf{u}\cdot\
abla)\mathbf{u}\) is negligible with respect to the viscous term and the nonlinear Equation (1) become a linear system of equations, known as the stationary Stokes equations:  
\[\begin{cases}-\
u\Delta\mathbf{u}+\
abla p=\mathbf{f},&\mathbf{x}\in\Omega,t>0\\ \
abla\cdot\mathbf{u}=0,&\mathbf{x}\in\Omega,t>0\end{cases} \tag{2}\]  
to which the same previously cited initial and boundary conditions must be applied. In these equations there is no more the presence of convection, but only diffusive processes survive.  
The Stokes Equation (2) have the great advantage of being linear, which makes them easier to work with, both analytically and numerically.
## Abbreviations  
GIS Geographic Information System  
GF-2 Gaofen-2 satellite  
DEM Digital Elevation Model  
ALOS Advanced Land Observing Satellite  
PALSAR Phase Array type L-band Synthetic Aperture Radar  
RTC Radiometric Terrain Correction  
ALOS DEM Advanced Land Observing Satellite Digital Elevation Model  
ASTER GDEM Advanced Spaceborne Thermal Emission and Reflection Radiometer Global  
Digital Elevation Model  
SRTM DEM Shuttle Radar Topography Mission Digital Elevation Model  
RS Remote sensing  
UAV Unmanned Aerial Vehicle  
NNDiffuse Nearest Neighbor Diffusion  
RT Rochester Institute of Technology  
JAXA Japan Aerospace Exploration Agency  
RDLS Relief Degree of the Land Surface
# Soil Moisture Retrieval from Multi-GNSS Reflectometry on FY-3E GNOS-II by Land Cover Classification
## 3 Methods
### Calibration of Reflectivity from Multi-GNSS Observations
The comparison of calibrated reflectivity of the three systems is displayed in Figure 5b; they are shown to be in agreement with each other. In this way, a general retrieval model can be applied to observations from the three systems. The GNOS-II reflectivity of the global land surface is shown in Figure 6. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Calibration Coefficients** & **P1** & **P2** \\ \hline GPS & 1.075 & 0.94 \\ BDS & 1 & 0 \\ GAL & 1 & 0.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Calibration coefficient for different systems. Figure 5: Density scatter plots for the comparison between reflectivity from different GNSS systems (7 July–31 October 2022): (**a**) before calibration; (**b**) after calibration. Figure 6: The GNOS-II reflectivity of land surface (10 July–9 September 2021). The reflectivity is related to several physical characteristics of land surface. Firstly, due to the surface soil dielectric constant, soil moisture has a primary influence on reflectivity over land surface with low vegetation (<5 kg/m\({}^{2}\)) and flat terrain. Secondly, vegetation attenuation plays an important role in high vegetation areas, e.g., over tropical forests. Thirdly, surface roughness also has an effect on the reflectivity due to yielding diffuse scattering. It is worth noting that for spaceborne observations, the impacts of topography are more substantial than those of small-scale roughness [29].
# Cooperative Localization Using Distance Measurements for Mobile Nodes
## 3 Resolving Rotation and Flip Ambiguities
### Analysis of Rotation and Flip Ambiguities
It should be noted that, since \(\Delta\mathbf{\mathcal{S}}^{\prime}\) and \(\mathbf{D}^{\prime}\) are fixed, it is unnecessary to take any new measurements and the true position \(\mathbf{\mathcal{S}}\) can be resolved by  
\[\mathbf{\mathcal{S}}=\mathbf{M}\big{(}\theta_{R}(\mathbf{F}\mathbf{\mathcal{S}}^{*}, \Delta\mathbf{\mathcal{S}}^{\prime},\mathbf{D}^{\prime})\big{)}\mathbf{F}\mathbf{ \mathcal{S}}^{*}. \tag{32}\]
# Modeling the Transient Response of Thermal Circuits
## 3 Elementary Thermal Circuits
### Transient Response Model
#### 3.2.1 Single Thermal Resistance  
By applying the second Kirchhoff law in the closed loop of the thermal element of Figure 2a, one can arrive at \(Rq_{2}=U/C\), where \(U\) is the energy stored in \(C\). By differentiating both sides of this equation and by using the first Kirchhoff law applied to one node of the thermal circuit (note that \(q_{1}=\frac{dU}{dt}\)), one can easily arrive at the following simultaneous equations:  
\[R\frac{dq_{2}}{dt}=\frac{q_{1}}{C}, \tag{19}\]  
\[q_{1}=Q-q_{2}. \tag{20}\]  
Substituting the expression of \(q_{1}\) of Equation (20) in Equation (19), one can obtain the following differential equation:  
\[\frac{dq_{2}}{dt}+\frac{q_{2}}{RC}-\frac{Q}{RC}=0. \tag{21}\]  
This differential equation is linear, ordinary and of first order, admitting solutions of the type \(q_{2}=Ae^{\lambda t}+B\). Substituting this function in Equation (21), one can obtain \(\lambda=-\frac{1}{RC}\) and \(B=Q\). Because \(q_{2}(t=0)=0\), \(A=-Q\). Therefore,  
\[q_{1}(t) =Qe^{-\frac{1}{RC}t}, \tag{22}\] \[q_{2}(t) =Q(1-e^{-\frac{1}{RC}t}). \tag{23}\]  
Since \(\Delta T=Rq_{2}\),  
\[\Delta T(t)=RQ\Big{(}1-e^{-\frac{1}{RC}t}\Big{)}. \tag{24}\]  
Note that, for \(t\rightarrow+\infty\), \(\Delta T=RQ\). Therefore, the steady state is verified.
# An Improved Marker Code Scheme Based on Nucleotide Bases for DNA Data Storage
## 4 Improved Marker Code Scheme  
The procedure of the improved marker code scheme is shown in Figure 3. The encoding process is shown in Figure 4. First, binary sequence \(\mathbf{m^{\prime}}\), including information bits and marker bits, is encoded as the information part of the LDPC code. Then, the LDPC code is interleaved to obtain the codeword \(\mathbf{c}\). Second, sequence \(\mathbf{c}\) is arranged to guarantee that the marker bits are uniformly distributed. In other words, \(D_{M}\) marker bits are placed at each interval \(D_{E}\). Next, the arranged sequence is mapped into a nucleotide sequence and cut into several short nucleotide chains \(\mathbf{s_{1}},\ldots,\mathbf{s_{n}}\) because DNA long chains are difficult to synthesize. Lastly, sequences \(\mathbf{s_{1}},\ldots,\mathbf{s_{n}}\) enter the channel and correspondingly output sequences \(\mathbf{r_{1}},\ldots,\mathbf{r_{n}}\).  
Due to synchronization errors in a DNA channel, the first step in decoding is to regain synchronization. This step is performed in the base MAP detector, where a new base-symbol-based synchronization algorithm is used. The details of this algorithm are described in the next section. The base detector outputs several log-likelihood ratio (LLR) sequences; then, these sequences are spliced together to obtain a new LLR sequence, \(I\). The LDPC decoder receives sequence \(\mathbf{I}\) and corrects residual substitution errors. To further improve correction performance, the LDPC code decoder feeds back information to the detector for resynchronization. The error correction performance is improved with the joint decoding between the detector and the LDPC decoder.  
Let the code length of the LDPC code be \(N\), and the check bit length of the LDPC code be \(M\). If \(N\) can be divided by \(D_{M}+D_{E}\), the storage efficiency of the improved marker code scheme can be expressed as follows:  
\[\eta_{3} =\frac{N-M-\left(\frac{N}{D_{M}+D_{E}}\right)D_{M}}{N} \tag{3}\] \[=R_{L}-\frac{D_{M}}{D_{M}+D_{E}}. \tag{4}\]  
If \(N\) cannot divided by \(D_{M}+D_{E}\), the storage efficiency is expressed as follows:  
\[\eta_{3} =\frac{N-M-\left(\frac{N}{D_{M}+D_{E}}+1\right)D_{M}}{N} \tag{5}\] \[=R_{L}-\frac{D_{M}}{D_{M}+D_{E}}-\frac{D_{M}}{N}. \tag{6}\]  
Thus, the storage efficiency of this scheme is expressed as follows:  
\[\eta_{3}=\left\{\begin{array}{cc}R_{L}-\frac{D_{M}}{D_{M}+D_{E}},&\text{ if }N=0\text{ }mod(D_{M}+D_{E})\\ &\\ R_{L}-\frac{D_{M}}{D_{M}+D_{E}}-\frac{D_{M}}{N},&\text{ otherwise}\end{array} \right.. \tag{7}\]  
Compared to the concatenated marker code scheme, in the proposed scheme, marker bits are inserted before LDPC encoding, which means that the marker codes are part of the LDPC code. Therefore, marker codes in the proposed scheme not only assist in regaining synchronization, but also improve the decoding performance of the LDPC code.
## 3 Results and Discussion  
### Spectra Data of Samples That Were Not Pre-Treated  
Figure 1 shows the NIR spectra of the soybean, algarrobo, broad bean, maca, and oatmeal samples without pretreatment. Additionally, each sample was assigned a letter and a number: H1--soybean, H2--algarrobo, H3--broad bean, H4--maca, and H5--oatmeal. The raw data spectra showed a change in the baseline values, and it was observed that a set of them presented more intense bands probably caused by the grinding conditions.  
For a better visualization of the behavior, the averages of the spectra established for each flour product were obtained. In this case, the soybeans were not in a fine powder form; this could justify their position in the upper zone of the spectra (see Figure 2). However, similar effects of a spectrum baseline shift could be reduced by performing detrending pre-treatment on the data to better visualize the chemical changes between samples. In Figure 2, the characteristic vibrational modes of soybean (H1 mean) can be identified, among which it is important to mention the region from 6000 to 5600 cm\({}^{-1}\) associated with the C-H functional group, while the N-H and C-O functional groups associated with the proteins lie in the region of 5000-4500 cm\({}^{-1}\)[45].  
Figure 1: Raw NIR spectra of samples: soybean grain (H1), algarrobo (H2), broad bean (H3), maca (H4), and oatmeal (H5).  
Figure 2: Mean raw spectra obtained from soybean mean (H1m), algarrobo mean (H2m), broad bean mean (H3m), maca mean (H4m), and oatmeal mean (H5m).
# Improved PBFT Algorithm Based on Comprehensive Evaluation Model
## 4 Evaluation
### Communication Complexity Analysis  
* The communication complexity of the PBFT algorithm: Assuming the number of nodes of the PBFT algorithm is \(n\), the consensus process of PBFT algorithm can be divided into five phases: The request phase, pre-prepare phase, prepare phase, commit phase, and reply phase. The communication complexity can be calculated to be \(1+(n-1)+(n-1)(n-1)+n(n-1)+n\), which is simplified to \(2n^{2}-n+1\). Therefore, we can obtain the communication complexity of the PBFT algorithm as follows: \[C_{1} = 2n^{2}-n+1\] (18)
* The communication complexity of the TB-PBFT algorithm: For convenience of description, it is assumed that \(n\) nodes are divided into n/m groups on average, and each group has \(m\) nodes. There are four stages of local consensus within the group: The pre-prepare phase, prepare phase, commit phase, and respond phase, and the communication complexity of local consensus is calculated as \(((m-1)+(m-1)(m-1)+m(m-1)+(m-1))n/m\). There are three phases in the global consensus: The request phase, verify phase, and reply phase, and the communication complexity of global consensus is calculated as \(n/m+(n/m-1)n/m+n\). Therefore, the total communication complexity of the TB-PBFT algorithm is:  
\[C_{2}=\frac{n}{m}(m-1)(2m+1)+(\frac{n}{m})^{2}+n \tag{19}\]  
Therefore, the ratio of the communication complexity of the TB-PBFT algorithm to that of PBFT algorithm is \(R=C_{2}/C_{1}\). We take \(m=4\), 10, and 16 to conduct simulation experiments when the total number of network nodes is less than 1000 and compare the ratio of the communication complexity of the TB-PBFT algorithm to the PBFT algorithm. The experimental results are shown in Figure 6. It can be seen from the figure that regardless of the value of \(m\), compared with the PBFT algorithm, the communication complexity of the TB-PBFT algorithm is extremely low.
# Heterogeneous Graph Structure Learning for Next Point-of-Interest Recommendation
## 4 Heterogeneous Graph Structure Learning Model  
### Model Structure Overview  
The proposed heterogeneous graph contrastive learning model for next POI recommendation (HGSL-POI) is illustrated in Figure 1. The model comprises three main components: (1) the heterogeneous graph contrastive learning; (2) the graph structure learning; (3) the sequence modeling. Specifically, in the heterogeneous graph contrastive learning component, we first construct a heterogeneous graph at the leftmost part in Figure 1. Then, we extract the interaction graph and four meta-path-based subgraphs according to different meta-paths from the constructed heterogeneous graph. Different graph encoders are used to learn the representation vectors for different graphs. Note the graph encoder in the middle produce representations for users and POIs since it operates on the user-POI interaction graph, while other encoders only yield representations for one type of node. Then, we use two types of contrastive learning methods to assist the learning of representation vectors for users and POIs. How the contrastive learning works is provided in Figure 2, Section 4.2. After we obtain the representations from different encoders, we use graph structure learning to reconstruct the corresponding meta-path-based subgraphs. By feeding the reconstructed graphs into graph encoders, we can obtain the final representations for users and POIs. The graph structure learning process can be found in Section 4.3. Finally, the sequence modeling uses the representations from graph structure learning and the users' check-in sequences, supplemented by other extra information such as time and distance, to predict the POIs that users are likely to visit in the future. Due to the space constraints, we put the details of sequence modeling in Section 4.4.
# Autonomous Active Tag Using Energy Harvesting Strategies
## 2 Materials and Methods
### Processing Module
The first starts in the transceiver, which receives a package through the antenna. The package is then sent to the microcontroller to be processed and, here, read or written in the EEPROM. The second follows the inverse process: the microcontroller reads the EEPROM data, processes them to form a package, which then arrives at the transceiver, which sends it to the reader that had initially requested the data. The system stays in low power mode until data is received. In our application, performing transmissions with no prior request is not allowed because the data will not have a receiver, but this is not a real limitation of the tag. The reason for that restriction is that this behavior would squander energy and, therefore, shorten the system's lifetime. An Operating System has been mounted on the device in which the system has been developed. The one used is FreeRTOS (Free Real-Time Operating System), a real-time operative system kernel for embedded devices [17]. Including an operating system adds complexity to the system as well as some overheads in the processing time and power consumption. However, this decision was taken due to the potential to greatly expand the capability of the device; specifically, by providing a firmware architecture that can enhance the software development speed through a hardware abstraction layer, multitasking management and dynamic resource management [18].
## 5 Discussion  
This study presented an approximated ODE-based model to analyse the performance of OORS, a mobile data offloading scheme that ensures the delivery of content within a strict time deadline, where nodes support the content dissemination process by temporarily storing copies of the content. Additionally, we developed a utility-aware optimisation problem to maximise the utility of the content dissemination process by leveraging the properties of the stationary regime. We validated the model's accuracy using two different mobility datasets: (i) a synthetic mobility trace generated from a simplified vehicular scenario, and (ii) a real-world human-based mobility trace. Simulation results confirm the good accuracy of the proposed model in both scenarios. Finally, we compared the performance of OORS, optimally tuned with respect to protocol parameters, with two state-of-the-art data offloading schemes.  
As future work, we plan to consider heterogeneous and community-based mobility patterns. Additionally, we aim to extend our ODE-based model to account for the simultaneous dissemination of multiple content items and limited cache sizes. Furthermore, while this study focused on scenarios where the dissemination process converges towards a stationary regime, we acknowledge that certain conditions, such as natural disasters or highly-dynamic-mobility environments, may disrupt this quasi-stationary behaviour. Investigating the optimisation of protocol parameters under non-stationary traffic regimes will be an important direction for future work.  
Conceptualization, A.P. and R.B.; methodology, A.P.; validation, A.P.; formal analysis, A.P.; writing and editing, A.P. and R.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data used in this study were obtained from publicly available sources and have been cited in the manuscript.  
The authors declare no conflicts of interest.
# Multi-Domain Communication Systems and Networks: A Tensor-Based Approach
## 4 Harnessing the Domain Trade-Off
### Multi-Domain Beamforming (Precoding)
The first two beamforming techniques presented in [50] can also be seen as multi-domain beamforming albeit in a vector/matrix framework. These account for all the domains of transmission by concatenating the input signals across various domains to form a vectorized input. Hence, as far as finding the beamforming operation under a sum-power constraint is concerned, the vectorized approach is conceptually equivalent to the tensor approach. However, under practical constraints such as per antenna power constraints, the tensor model has a significant advantage over the vectorized model. In the vectorized input signal various indices corresponding to all the antennas, sub-carriers, and sub-symbols have been reduced to a single index. This makes the incorporation of any per domain constraints difficult and not intuitive, as the domains are not separated by different indices. On the other hand, the tensor model allows to keep the structure of the channel and the input/output intact, and thus can easily incorporate practical constraints which are domain specific, as in (27), while designing the transceiver schemes. The proposed tensor framework is intuitive and flexible, allowing for convenient incorporation of such power constraints for any number of antennas, and even extend the constraints to per sub-carrier or per sub-symbol in MIMO GFDM or per user in a multi-user system.
# Determining Factors of Fixed Offshore Platform Inspections in Indonesia
## 2 Conceptual Background
### Fixed Offshore Platforms  
Fixed offshore platforms are one of the most used types of offshore platforms; about 95% of the world's offshore platforms use a fixed platform [21]. There has been a significant increase in offshore structures, mainly in the renewable energy field [1]. These fixed platforms consist of a topside, jacket, and pile [22]. The topside is the part that is above the water surface and is the main facility on offshore platforms; this part determines the function of a platform. Topside constructions consist of several groups of types of constructions where the number, completeness, and facilities available on a topside depend on the main function of the platform [22]. Each fixed offshore platform has different functions, including extraction, drilling for exploration, the processing of crude oil and natural gas, and storage [23]. A platform is supported by a steel truss consisting of support plates or a deck truss structure supported by welded tubes stacked on the seabed. A steel frame is called a jacket, which is used underwater at depths of 500 m or less [21]. Fixed offshore platforms are designed where there are several optimizations, including size and shape, strength, stiffness, ultimate strength, and natural frequency [24]. Fixed offshore platforms are also designed considering the load of ocean waves, wind, and seismic vibrations. In addition, they also take into account the weight of the power plant, the top of the crane, and possible vibrations caused by ships or ice [25].