esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries: Main Tools and Requirements
### Tolerance Proportionality and Computational Stability  
If a method and its step size have been chosen properly for a given problem, it typically operates in the asymptotic regime. Thus, if the method order is \(p\), the norm of the local error at time \(t_{n}\) is well approximated by  
\[l_{n}=\psi(t_{n})h_{n}^{p}\,, \tag{1}\]  
where \(h_{n}\) is the current step size and \(\psi(t)\) is the principal error function. The latter is smooth provided that the regularity of the solution is sufficiently high. When the regularity is low, it is often preferable to use a low-order method, i.e., reduce \(p\) to match the regularity of the solution so that we again have a local error of the form (1) with a smooth function \(\psi(\cdot)\). In adaptive step-size selection based on local error control, the objective is to keep the local error constant, equal to a prescribed tolerance tol, i.e.,  
\[l_{n}=\psi(t_{n})h_{n}^{p}=\textsc{tol}\quad\Rightarrow\quad h_{n}=\left( \frac{\textsc{tol}}{\psi(t_{n})}\right)^{1/p}.\]  
It follows that the \"proper\" step-size sequence is smooth, and that  
\[\log h_{n}=\frac{1}{p}\big{(}\log\textsc{tol}-\log\psi(t_{n})\big{)}.\]  
Hence, if the logarithm of the step-size sequence \(\{\log h\}\) is plotted vs. time, a change in tol merely shifts the graph given by \(\{\log\psi\}\) vertically. In other words, the proper step-size sequence is a smooth function negatively correlated with the principal error function, with \(\{\log h\}\) proportional to \(\log\textsc{tol}\).  
Figure 2: Multigrid cycle types: V-cycle, W-cycle, and F-cycle.  
For these reasons, any step-size selection scheme should aim to replicate these properties. Thus, the generated step-size sequence should be smooth, and if well implemented, the tolerance proportionality should be readily visible in real computations. If the global error accumulation is regular, this implies that the global error should be proportional to the tolerance. This can, however, only be achieved with careful implementation (see, e.g., [28]). In practice, tolerance proportionality means that the global error \(u_{i}-u(t_{i})\) is bounded below and above by the expression  
\[c\cdot\textsc{tol}\leq\|u_{i}-u(t_{i})\|\leq C\cdot\textsc{tol}\,,\]  
where the two constants satisfy \(c/C\lesssim 1\). Note that this is a fairly rigorous requirement; we not only require that the local error is less than tol but also that it is near tol. Thus, if tol is increased or reduced by a small factor, the error must change accordingly, increasing or decreasing in proportion. This is also referred to as computational stability, as the computational algorithm is required to produce accurate approximations in the sense that the error stays near that of previous results. In the present context, we aim to investigate whether it is also possible to retain such properties in parallel integration.
# Development of Communication Protocols between BIM Elements and 3D Concrete Printing
## 2 Literature Review
### Additive Manufacturing and 3D Concrete Printing  
Additive manufacturing or 3D printing is a process that uses technology that, in an automation process, allows users to design and create personalized three-dimensional objects, directly from a digital model, without using formworks in their manufacture [17]. Only the material required for the creation of the element designed is used without generating waste. This lowers cost and allows greater efficiency and more sustainable production [18]. Specifically, in the architecture industry, additive manufacturing is replacing manual construction techniques used in traditional structural works [19].  
There are six types of standardized categories to classify additive manufacturing technology [20], as follows: (1) extrusion-based additive manufacturing of concrete; (2) vat photopolymerization; (3) powder bed fusion; (4) injection by a binder or \"binder jetting\"; (5) material injection or \"material jetting\"; and (6) laminated object manufacturing (LOM).  
All this has advanced the construction industry in the development of new additive manufacturing technologies, such as 3D concrete printing [21], where concrete is considered the most used material in the construction industry [20], given that the components for its manufacture are easily accessible and inexpensive in the most places in the world. As is known, concrete has the advantages of being durable and resistant, and because of its fluidity before setting, it is possible to create almost any concrete element [22].  
Concrete elements require formworks for their manufacture [10], where the cost of formworks can vary between 35% to 60% of the total cost of the concrete structure [23].  
Therefore, the possibility that structures can be built without the need of using formworks creates great advantages for the construction industry, reducing costs and manufacturing times [24].  
Concrete printing uses additive manufacturing techniques based on placing layers of concrete successively; this allows creating structures with more complex geometries compared to the traditional process [25]. This construction technique can become an efficient process, but to achieve it, it is necessary to overcome its limitations [26]. These limitations are related to the hardening and the placement of concrete layers over time, because the time between layers must be long enough to ensure the strength of the concrete, without experiencing deformations when the subsequent layers are deposited, and short enough to ensure the union between them in an optimal way [24].
###### Abstract  
The electrochemical stability of ceftaxone (CFTX), belonging to the third generation of cephalosporon antibiotics, was studied by electrochemical measurements recorded on a platinum electrode (Pt) in aqueous solutions containing sodium halides. The electrochemical behavior of ceftaxone was investigated by cyclic voltammetry (CV) and constant current density electrolysis assisted by UV-Vis spectrophotometry. Cyclic voltammetry highlighted that the addition of CFTX in sodium halide solutions leads to significant changes in the hysteresis characteristics due to specific interactions with active species from electrolytes, as well as with the platinum electrode surface. After CV, when an exterior electric stimulus in short time (40 s) was applied, the UV-Vis spectra illustrated that CFTX is stable in the presence of F\({}^{-}\) ions, it is electro(degraded/transformed) in the presence of Cl\({}^{-}\) and Br\({}^{-}\) ions and interacts instantly with I\({}^{-}\) species. Electrolysis at constant current density confirms the results obtained from cyclic voltammetry, showing that (i) in the presence of fluoride ions CFTX gradually decomposes, but not completely, in about 60 min, without identifying a reaction product. (ii) chloride and bromide ions determine the almost complete CFTX electro(degradation/transformation) in 10 and 5 min, respectively, with completion of the electro-transformation reaction after 60 and 30 min, respectively; (iii) instantaneous interactions between CFTX and the iodide ions occurred.  
cefriaxone; electrochemical degradation; sodium halides; UV-Vis spectrophotometry; electrodegradation mechanism +
Footnote †: journal: Applied Sciences
# Study on Fire Smoke Distribution and Safety Evacuation of Subway Station Based on BIM
## 4 Fire Smoke Analysis of Subway Station
### Fire Model of Subway Station
#### 4.1.3 Grid Division  
When the PyroSim software is used for fire simulation, it is necessary to mesh all objects. If the mesh is too small, the results are accurate, but the existing equipment will consume a lot of time and energy; if the mesh partition is too large, the calculation accuracy will be affected and the expected simulation effect cannot be obtained. In order to ensure the final calculation results, the appropriate accuracy must be selected. Whether the grid setting is reasonable is generally judged by the ratio of the special diameter of the fire source to the element size of the grid. The expression is as follows:  
\[D^{*}=\left(\frac{Q_{\text{max}}}{\rho_{0}c_{p}T_{0}\sqrt{g}}\right)^{\frac{2 }{5}} \tag{2}\]  
where \(D^{*}\) is the characteristic value of fire; \(Q_{\text{max}}\) is the maximum heat release rate of fire; \(\rho_{0}\) is the relative density of air, under the normal temperature \(\rho_{0}\) = 1.2 kg/m\({}^{3}\); \(c_{p}\) is the specific heat capacity of air at constant pressure, generally 1.02 kJ/kg\(\cdot\)K; \(T_{0}\) is the environment temperature and \(g\) is the acceleration of gravity.  
From the above formula, when the heat release rate of the fire source is 5 MW, the fire source diameter is 1.82 m. Referring to the relevant literature at home and abroad, it is generally believed that the grid size is between 0.0625 D and 0.25 D, that is, the grid division is between 0.11 m and 0.45 m. In this study, considering the accuracy of the calculation results and the running ability of the computer, the multi-grid division method is adopted to divide the grid of the platform layer into 0.5 m \(\times\) 0.5 m \(\times\) 0.5 m, and other areas into 1 m \(\times\) 1 m \(\times\) 1 m. The total number of the calculation grids is 3,950,364.
# Joint Design of Transmit Waveforms for Object Tracking in Coexisting Multimodal Sensing Systems
## 3 Spectrum Sharing Radar and Communications Systems
### Wireless Multiuser Communications Receiver
#### 3.3.2 Communications Receiver Processing  
At the receiver of the communications system, the \(q\)th symbol, \(q=0,\dots,Q-1\), that is transmitted by the \(m\)th user, \(m=1,\dots,M\), during the \(k\)th PRI, \(k=1,\dots,K\), can be estimated by:  
\[\hat{p}_{m,q,k}=\arg\max_{p_{m,q,k}=1,\dots,P}\ \left\{\Lambda_{m,q,k} \right\}. \tag{22}\]This optimization results in the index of the phase shift that yields the maximum correlation given by:  
\[\Lambda_{m,q,k}=\text{Re}\Bigg{\{}\sum_{n=qn_{\text{c}}}^{(q+1)n_{\text{c}}}z_{ \text{c,}k}[n]\,s_{\text{c,}m}(nT_{s}-qT_{\text{c}}-kT_{\text{PRI}})\,e^{-j2 \pi(p_{m,q,k}-1)/P}\Bigg{\}}. \tag{23}\]  
Note that this correlation term includes not only the noisy transmitted waveform from the \(m\)th user, but it also assumes the presence of the radar return. From Equation (23), the correlation is shown to depend on the transmit waveform \(s_{\text{c,}m,k}[n]\) and thus \(s_{\text{c,}m}[n]\) in Equation (20). Using CoWS, the \(m\)th user's communication signal depends on the parameter set \(\Psi_{\text{c}}\!=\!\{b_{m},\ T_{\text{c}}\}\) in Equation (8) (for one user); thus, \(s_{\text{c,}m}[n]\!=\!s_{\text{c,}m}[n;b_{m},\ T_{\text{c}}]\). In addition, the signal term \(z_{\text{c,}k}[n]\) in Equation (23) depends on \(x_{\text{r,}k}[n]\) in Equation (21), which is defined in Equation (9); this radar interference term causes the correlation in Equation (23) to also depend on the radar parameter set \(\Psi_{\text{r}}\!=\!\{b_{\text{r}},\ T_{\text{r}}\}\) in Equation (7) since \(x_{\text{r,}k}[n]\!=\!x_{\text{r,}k}[n;b_{\text{r}},\ T_{\text{r}}]\). As a result, in order to obtain a better estimate of \(p_{m,q,k}\) in Equation (22), the correlation has be optimized over all possible radar and communication parameters. This dependence is shown as:  
\[\Lambda_{m,q,k}(\Psi_{\text{c}},\ \Psi_{\text{r}})=\text{Re}\Bigg{\{}\sum_{n=qn_{ \text{c}}}^{(q+1)n_{\text{c}}}z_{\text{c,}k}[n;\Psi_{\text{c}},\ \Psi_{\text{r}}]\,s_{\text{c,}m}(nT_{s}-qT_{\text{c}}-kT_{\text{PRI}};\Psi_{ \text{c}})\,e^{-j2\pi(p_{m,q,k}-1)/P}\Bigg{\}}. \tag{24}\]  
Note that in Equation (24), it is already assumed that the receiver has determined that the received waveform is from the \(m\)th user.
# Predictive Maintenance Based on Identity Resolution and Transformers in IIoT
## 6 Conclusions  
In this paper, the authors present a study that focuses on the development of an efficient framework, ITPM, which enables the predictive maintenance of devices in IIoT space. ITPM facilitates accurate and efficient real-time prediction of sensor signal sequences, thereby serving as a remarkable addition to the predictive maintenance field. The proposed framework comprises two interdependent components: identity resolution and an attention mechanism. Firstly, the identity resolution component utilizes DIDs to allocate unique IDs to production equipment or devices, while the attention mechanism component performs state awareness modeling based on existing sensor signal sequences to evaluate the current health condition of the monitored equipment or devices. Furthermore, the authors put forth a sequence prediction model that estimates future signal sequences of production devices for use in conjunction with the underlying state awareness model, assisting in determining the health status of targeted machines over subsequent iterations, hence minimizing potential losses. Our experimental findings indicate superior performance outcomes such as 99% classification accuracy and favorable predictive abilities, showcasing the effectiveness of the proposed ITPM framework. Our future studies will explore more streamlined structural designs and enhanced universality for our proposed framework.  
Figure 8: Prediction sequences for different categories of dataset.  
**Author Contributions:** Conceptualization, Z.Q.; Methodology, Z.Q. and L.D.; Software, L.D.; Validation, Z.Q.; Investigation, L.D.; Writing--original draft, Z.Q.; Supervision, R.H. and T.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China (No. 62171046 and No. 92367104).  
**Data Availability Statement:** The original data presented in the study are openly available in [[https://engineering.case.edu/bearingdatacenter/download-data-file](https://engineering.case.edu/bearingdatacenter/download-data-file)]([https://engineering.case.edu/bearingdatacenter/download-data-file](https://engineering.case.edu/bearingdatacenter/download-data-file)).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 6 Results  
In this section, we report the performance evaluation for all the considered solutions. Our study evaluates various parameters. First, we measure the amount of RAM and ROM memory used by each solution; we then evaluate the latency of request/response transactions; after that, we measure the energy consumed by each different solution for processing and replying to a request. Moreover, we evaluate the client goodput obtained in 802.15.4 links affected by Rayleigh fading. This test allows us to evaluate the performance of the reliability mechanism proposed for CoAP [11]. Finally, we evaluate the effect that high workloads have on the server performance. In this case, we analyze the rate of the requests per second that it can serve as a function of the client request rate.  
The results concerning latency, energy and reliability are reported according to the payload size with which a server replies to a client request. The maximum payload size varies depending to the implementation used. We found the cause of this variation on the different methods used to allocate RAM memory. The implementations using dynamic memory allocation achieve the lowest payload size. In this sense, CoapBlip is able to reach 650 bytes as maximum payload size while HTTP/TCP and HTTP persistent reach 800 bytes. TinyCoAP benefits from the use of static allocation and it is able to reach 1,200 bytes. The same size is achieved by HTTP/UDP. In this case the low complexity of the implementation and the absence of HTTP buffers allow to HTTP to work with high payload sizes.
# Monitoring Land Cover Change on a Rapidly Urbanizing Island Using Google Earth Engine
## 3 Results  
### Land Cover Classification and Accuracy Assessments  
The land cover classification maps of the years 1990, 2000, 2007, and 2019 are shown in Figure 3a-d, which were produced using random supervised classification. The RF classifier produced good overall accuracies for Haitan Island, with overall accuracy assessments of 90.09% (1990), 90.31% (2000), 91.08% (2007), and 88.43% (2019), respectively, and the Kappa accuracies were 0.88 (1990), 0.89 (2000), 0.90 (2007), and 0.86 (2019) (Table 3). For each class type, the user's accuracies and producer's accuracies ranged between 80-100% in each year. Water had high classification accuracies in each year because its reflectance is easier to distinguish from other categories, whereas sandy land and other land had low accuracies.  
Figure 3: Land cover classification on Haitan Island: (**a**) 1990, (**b**) 2000, (**c**) 2007, and (**d**) 2019. A is the Xingfuyang area. A, B in (**d**) 2019 is the different area compared to 1990, 2000, and 2007, due to reclamation.
## 3 Target Data Processing and Computing
### Three Data Processing Paradigms  
In the area of data processing, three basic data processing paradigms, namely batch processing, stream processing, and function processing, have become prominent, each offering distinct methodologies to handle information efficiently [51]. The first paradigm, batch processing, involves the systematic analysis and processing of data in predetermined, fixed-size sets. It is well-suited for tasks that can tolerate a degree of latency and operate on collected data. In contrast, stream processing is designed for real-time analysis, enabling the continuous processing of data as they flow, making it particularly suitable for applications requiring immediate insights or actions. The third paradigm, function processing, represents a paradigm shift toward event-driven computing, where discrete functions or microservices are triggered by specific events, offering a highly responsive and modular approach to data processing. These three paradigms offer a broad range of tools that can be used to handle a variety of data processing needs, large-scale, periodic analyses to real-time, event-driven applications.
# Durability-Aimed Design Criteria of Cement-Stabilized Loess Subgrade for Railway
## 4 The Design Criteria Aiming at Subgrade Durability
### The Design Criteria  
The filling strength design criteria of cement-stabilized loess could be obtained by substituting \(k_{\text{r}}\), \(\eta_{\text{G}}\), \(\eta_{\text{D}}\) and \(\sigma_{\text{dmax}}\) into Equation (3):  
The filling strength of subgrade bottom \(\geq\)50/(0.26 \(\times\) 0.4 \(\times\) 0.3) = 1602 kPa  
The filling strength of embankment below the subgrade \(\geq\)20/(0.2 \(\times\) 0.4 \(\times\) 0.3) = 801 kPa  
The strength of cement-stabilized loess increases with age. 7-day strength is about 0.6 times the critical strength, while 90-day strength is about 0.9 times the critical strength [73; 74]. There are at least 3 months between the completion of constructing subgrade and the beginning of train operation, so 90-day strength is too long to be used as the design index because it is not convenient for engineering application. The 7-day strength is usually used as the design index  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**The Type of Data** & \(K_{30}\) **(MPa/m)** & **90** & **110** & **130** & **150** & **170** & **190** \\ \hline The calculated value & \([\sigma_{0}]\) (kPa) & 231 & 279 & 327 & 375 & 425 & 471 \\ The measured value & \([\sigma_{\text{d}}]\) (kPa) & 104 & 126 & 147 & 169 & 190 & 212 \\ The measured value & \([\sigma_{\text{d}}]\) (kPa) & 118 & 134 & 150 & 166 & 182 & 199 \\ \hline \hline \end{tabular}
\end{table}
Table 8: \(K_{30}\) and the critical dynamic strength of the corresponding subgrade filling.  
in the project. Therefore, the 7-day strength design criterion was determined combined with the relationship between the strength and age of stabilized soil:  
\[q_{\mathrm{u7}}=(0.6\ \times\ q_{\mathrm{u}})/0.9=\frac{2}{3}q_{\mathrm{u}} \tag{14}\]  
According to Equation (14), 7-day strength design criteria of cement-stabilized loess filling could be preliminarily obtained:  
The filling strength of subgrade bottom \(\geq\)1602 kPa \(\times\ 2/3=1068\) kPa.  
The filling strength of the embankment below the subgrade \(\geq\)801 kPa \(\times\ 2/3=534\) kPa.  
Therefore, 7-day strength design criteria based on durability are presented, as shown in Table 9.
## 3 Description of the Environment
### Hindsight Experience Replay  
Hindsight Experience Replay (HER) is a technique that improves an agent's learning efficiency in environments with high complexity or those requiring long action sequences. HER enables experiences that would traditionally be deemed \"failed\" to be leveraged by reinterpreting them as successes for alternative goals. Consequently, the agent can make better use of the collected training data, which is particularly beneficial in sparse-reward settings. In this study, HER is integrated into the SAC, TD3, and TQC algorithms. The HER buffer operates for each transition \(\tau\) as follows:  
* **Success cases:** If the agent achieves the intended goal \(g\), the standard reward \(r_{g}\) is assigned; in this situation, HER provides no additional benefit. The transition parameters \(\tau=(s_{t},a_{t},r_{t},s_{t+1},g)\) are simply stored in the replay buffer.
* **Failure cases:*
* If the agent fails to reach the intended goal \(g\), the HER buffer reinterprets the goal as \(g^{\prime}\) and appends a new trajectory \((s_{t},a_{t},r^{\prime}_{t},s_{t+1},g^{\prime})\) to the replay buffer. The \"future\" strategy is used, which samples random future states as the new goal. The reward is then computed based on the newly selected goal \(g^{\prime}\). The alternative goal can be defined as \[g^{\prime}\ =\ s_{t+k},\] (38) where
* \(g^{\prime}\) is the alternative goal designated by the HER buffer;
* \(s_{t+k}\) is a future state from the same episode.
# Investigation and Optimization of EOR Screening by Implementing Machine Learning Algorithms
## 3 Methodologies
### Artificial Neural Networks  
The ANNs algorithm is a model inspired by the neuron network of the human brain and is used to solve various problems such as classification, regression, and image processing [98]. Its principle is based on information transfer and weight adjustment between neurons. The core is weight. This concept is derived from the simulation of the working principle of the biological nervous system. Artificial neural networks are composed of multiple neurons (simulating biological neurons) distributed in different layers, including the input layer, hidden layer, and output layer. Each neuron receives input from the neuron in the previous layer, performs a weighted sum of the inputs, and then applies an activation function to produce an output. Figure 6 shows the process of the neural networks' operation and the weight calculation and output of a single neuron.