esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results
### IMGO for the PV Parameter Estimation Problem
At lower configurations, such as 50 solutions and 500 iterations, IMGO achieves a standard deviation of 0.000105, a substantial improvement over MGO's 0.000286. As iterations and solutions increase, IMGO continues to demonstrate superior accuracy, with a standard deviation of 1.24 \(\times\) 10\({}^{-6}\) at 100 solutions and 2000 iterations compared to MGO's 1.29 \(\times\) 10\({}^{-6}\). These results validate IMGO's refinement capabilities and its ability to deliver highly precise solutions. Figure 20: Sensitivity analysis by varying the solutions and iterations for MGO and IMGO for R.T.C. cell parameter estimation.
## 3 Results
### Optimizing the Model
#### 3.1.7 Light GBM  
The hyperparameters for LightGBM were set with the same conditions as XGBoost [84]. Among the different CV values, the CV as 2 resulted in the lowest Delta RMSE of 0.74 MPa. However, when considering other evaluation metrics, CV values ranging from 3 to 10 showed better performance. Specifically, CV values between 3 and 10 achieved the highest R\({}^{2}\) value of 0.94, along with the lowest RMSE of 4.16 MPa and MAE of 3.03 MPa. Furthermore, the Delta RMSE was the second lowest at 1.4 MPa. Taking all these factors into account, the optimal LightGBM model was selected with a CV value of 3. Consequently, the optimal hyperparameters for the LightGBM model are 200 estimators, a learning rate of 0.1, a maximum depth of 7, and a subsample of 0.5, as summarized in Table 3.
# First Evidences of Ionospheric Plasma Depletions Observations Using GNSS-R Data from CYGNSS
## 4 Discussion
### Comparison with Plasma Density Data from Swarm
This fact could difficult the correlation between both databases, but here, there are some evidences that motivates further study.
## 1 Introduction
Much of the research in water transport in the membrane in the last two decades examined water content of the membrane and the inlet humidity of gases affect proton conductivity, activation overpotentials, and durability [19]. However, most of these studies did not involve open-cathode fuel cells. Open-cathode PEM fuel cells are commonly found in low-power stacks with the cathode air supply subsystem coupled to an air-cooled coolant system [20]. Affected by the structure of open-cathode fuel cells, the catalytic layer (CL) of the cathode side is exposed to the atmosphere; consequently, the relative humidity of the cathode side is highly nonlinear to the flow speed of input air and ambient humidity [21]. The open-cathode PEM fuel cell also has the characteristics of low pressure of the input gas and small pressure difference between the cathode and anode, which indicates that the open-cathode fuel cell has low requirements on the auxiliary system. The MEA of a PEM for an open-cathode fuel cell is the core component of the fuel cell system. The central part of the MEA is the PEM, which acts as a gas separator and an ion conductor. Porous CLs (gas diffusion electrodes) are located adjacent to the membrane, one on each side as shown in Figure 1a. The catalytic layers contain three separate phases: gas pores for the reactants, an electron conducting electrode phase, and an ion-conducting polymer (ionomer) electrolyte phase as shown in Figure 1b. The surface of the electrode phase in the CLs contains noble metal catalysts to minimize the reaction overpotentials. GDLs are placed outside the CLs. GDLs are also porous and carry out the task of conducting electrons and allowing the passage of gases. For low-temperature fuel cells, water management is of crucial importance for the performance of the MEA. Running the cell under excessively wet conditions may result in mass transport limitations of gases due to the flooding of liquid water in the pores, whereas running the cell under excessively dry conditions may result in poor performance due to the low ohmic conductivity in the ionomer used in the membrane and CLs. Therefore, modeling is needed to investigate the effect of relative humidity of the inlet gases on the performance of a low-temperature polymer electrolyte MEA. The model includes gas-phase mass transport, water transport in the ionomer, and humidity-dependent ionomer conductivity. Hydrogen cross-diffusion over the ionomer membrane is not included in the model. The developed model is coupled with multiple physical fields. The manuscript of the study is presentedas follows: The mathematical model with mass transport phenomena is described, and the MEA component with a focus on water transport is presented in Section 2. The numerical approach is introduced in Section 3. The simulation results and some experimental data are presented with further analysis and deep discussion in Section 4. The conclusion is drawn in Section 5.
# A Review on Design Considerations and Connection Techniques in Modular Composite Construction
## 3 Area of Research
### Design Considerations in Modular Construction
#### 3.2.2 Layout of Modules  
The design of the layout of modules is mainly affected by the size, modularisation, weight of the module, and ease of transportation. The size of the module and modularisation of the building should be designed with coordination between the designer and module manufacturer so that the module formwork moulds are used efficiently. Proper vertical alignment of load-bearing walls should be ensured while designing the module layout. Generally, two methods of module layout are adopted in modular construction: (i) cluster of modules and (ii) corridor arrangement of modules, as shown in Figure 7[37]. The cluster configuration entails the organisation of multiple modules surrounding a central core or communal area, typically for functions such as shared amenities, stairways, or elevators. This design is frequently employed in residential or hotel buildings, where modules can  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Component** & **Required Strength at 28 Days (N/mm2)** & **Demoulding Strength (N/mm2)** \\ \hline Slab & 40 & 20–25 \\ \hline Load-Bearing Wall & 50 & 25–30 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Strength of concrete elements [1].  
effectively utilise common spaces while preserving structural efficiency. Cluster configurations provide compact designs and reduce the necessity for extensive interconnections between modules. The corridor configuration of modules presents a linear design, with modules arranged along a corridor or passageway. This layout is favoured in institutional constructions, such as schools and hospitals, where accessibility and circulation are prioritised. Corridor configurations guarantee uninterrupted connectivity between modules and enhance the straightforward incorporation of services such as heating, ventilation, air conditioning (HVAC), plumbing, and electrical systems along the corridor axis.
# Developing Productivity-Safety Effectiveness Index Using Data Envelopment Analysis (DEA)
## 2 Background  
### Importance of Safety Management for Social Responsibility  
As the concept of ESG proliferated for international trade, the safety management for workers' health and lives became very important [18]. The negative impact of fatal injury on corporations should be followed such as loss of human lives, regulatory costs, reputation damage, and financial losses. Thus, the CEO should consider the effectiveness of safety investment on productivity in terms of production economics [7; 17]. Many firms have also tried to invest in these R&D projects on advanced technologies such as sensors, AR/VR, drones, and machine learning for structuring safety systems. For instance, sensor-based safety systems in construction sites are developed to detect the anomaly information derived from unsafe conditions and behaviors. CCTV-based surveillance systems are also adopted to detect dangerous situations in real-time monitoring, in which the workers do not wear safety helmets or fires occur.  
Many benefits of safety management to increase productivity are reported in the previous literature. First, when the incident occurs in industrial sites, the manufacturing systems are stopped to treat the risky situation. It leads to the loss of working time and influences facility utilization [7; 19]. The production schedule will be then delayed by this failure [14; 25]. Second, fatal incidents leading to death hurt corporate brand image. As society matures, the compliance systems become rigorous. Society can blame the corporation due to the loss of workers [20]. For example, the Deepwater Horizon, an offshore drilling rig operated by BP (British Petroleum), experienced a catastrophic explosion and subsequent fire, leading to the deaths of 11 workers and causing one of the largest oil spills in history. Thus, the efficiency of both safety performance and productivity should be examined and the managerial implications should be given to the industry or company.
## 3 Results and Discussion  
### Meat Quality Traits  
No difference in meat quality traits between the control and study samples was observed after 21 days of the dry-aging process. Table 1 summarizes the pH value and color parameters of raw meat before and after 21 days of dry-aging. After 21 days of aging, the samples from both groups did not differ significantly in the same parameters or in terms of chemical composition. Similar results were previously obtained by [PERSON] et al. [17] for pH and color parameters and by [PERSON] and [PERSON] [12] for other meat quality traits. However, the results show that significant changes occur already during 21 days of dry-aging of beef for both groups together. The pH value increased form 5.67 to 5.80 (\(p<0.05\)), the protein content changed significantly from 17.57% to 22.37% (\(p<0.05\)), the fat content from 17.92% to 21.55%, and the collagen content from 1.12% to 1.54% (\(p<0.05\)). The significant changes in protein, fat, and collagen content were the result of moisture loss during aging. Similar results were reported in the research of [PERSON] and [PERSON] [12]. In case of color parameters the results are varied, and both an increase and a decrease in the values of parameters characterizing meat color can be observed [26; 27]. Among the factors that may influence these changes are, for example, the counted fat content (Table 1) or changes in mitochondrial activity [27; 28]. The pH changes during aging are in agreement with results presented in the study of [PERSON] et al. [29] and [PERSON] et al. [30], but in contrast to the research of [PERSON] et al. [31]. [PERSON] et al. [32] stated that the increase in pH during aging can be attributed to nitrogenous compounds deriving from protein hydrolysis. It should be emphasized that an increase in pH during meat aging may favor the growth of microorganisms. [PERSON] et al. [10] showed that _Pseudomonas_ spp. was the dominant bacterial group in dry-aged beef samples. The obtained showed a correlation between bacterial counts and surface pH (r = 0.7-0.8; \(p<0.01\)). The study by [PERSON] et al. [17] also showed that the bacterial community of samples aged without biostarter was more variable than those aged with the _M. flavus_. It was also shown that the increase in the pH value during beef aging was also associated with the degradation of myofibrillar proteins, the increase in meat tenderness, and an improvement in sensory meat quality. The proteolysis of muscle protein during aging, as a result of the activity of endogenous muscle endopeptidase and microbial proteases, has already been confirmed in other studies [33]. The results showed that the beef dry-aged for 21 days with _M. flavus_ biostarter had less shear force after grilling, with significant differences (\(p<0.05\)) (Table 2). Similar results were obtained by [PERSON] et al. [26]. The lower shear force after heat treatment in meat aged with biostarter corresponds to the higher juiciness of the meat. In addition, similar sensory quality results were obtained over 21 days of aging in the study by [PERSON] et al. [26].
## 2 Distributed Fair Rate Allocation Algorithm  
Let us assume a large-scale Wireless Sensor Network with \(N\) nodes that is monitoring a certain area. The mission of the network, as usual, is to gather the node measurements at the sink, where the information is processed. Let us assume that the nodes find their routes to the sink using best-effort solutions such as the Routing Protocol for Low Power and Lossy Networks (RPL) [13] or alternatives [14, 15]. As a result, the initial graph of the network turns into a minimum spanning tree. Note that these techniques are very interesting in the context of WSNs since they keep the complexity of the routing protocol reduced, even given that they are suboptimal according to a certain performance metric. As important as routing is the Multiple Access Control (MAC) protocol, that is, how the sensorsgain access to the wireless channel and how the available rate is finally distributed among the nodes. As discussed previously, two different families of approaches are being considered: (i) contention-based (e.g., CSMA/CA) and (ii) demand-based techniques (e.g., GTS allocation). The former is usually implemented due to its simplicity but it becomes inefficient in terms of throughput as the traffic load grows. The latter gives us the optimal solution once a global cost function is determined but it suffers from computational complexity and signalling requirements. In this paper, we propose a distributed algorithm for the second approach that converges fast to the optimal solution, which implies a reduced amount of signalling in the network. Furthermore, it is computationally lightweight.  
In the following, we assume that all the nodes in the network first make a rate request according to the amount of information they have to send. After running the distributed allocation algorithm, all of them get a certain transmission rate. The allocation depends on the rate requests, the priorities of the sensors, the minimum guaranteed rates and the maximum transmission rate at each level of the tree topology. All these parameters are known beforehand and the goal is to provide a fair rate distribution so that all the sensors in the network receive their transmission opportunities. On the contrary, note that a solution that maximizes the network throughput may prevent users with bad channel condition from transmitting. In practice, our solution is adequate for Frequency Division Multiple Access (FDMA) or Time Division Multiple Access (TDMA) since in both cases, it is possible to make an arbitrary division of the available total transmission rate in terms of bandwidth and time, respectively. Figure 1 provides a general view of our demand-based solution, where each dashed box represents one multiple-user communication channel between the parent node and its child nodes. Furthermore, a circle represents a node (sensor) and a line between two nodes simply indicates that they can communicate (using the corresponding multi-user channel).  
Figure 1: Demand-based multiple access. Rate grants are emitted and depend on the requests and on the rate capacities \(\{c_{i}\}\) inside each cluster.
## 2 Preliminaries and Background
### Limited-Memory Warping LCSS Gesture Recognition Method
#### 2.4.1 Quantization Step (Training Phase)  
At each time, \(t\), a quantization step assigns an \(n\)-dimensional vector,  
\[x(t)=[x_{1}(t)\dots x_{n}(t)], \tag{6}\]  
representing one sample from all connected sensors as a symbol. In other words, a prior data discretization technique is applied on the training data, and the resulting discretization scheme is used as the basis of a data association process for all incoming new samples. Specifically to the LM-WLCSS, [PERSON] et al. [21] applied the K-means algorithm and the nearest neighbor. Despite the fact that K-means is widely employed, it suffers from the following disadvantages: the algorithm does not guaranty the optimality of the solution (position of cluster centers) and the optimal number of clusters assessed must be considered the optimum. In this paper, we investigate the use of the Ameva and ur-CAIM coefficients as a discretization evaluation measure in order to find the best suitable discretization  
Figure 1: A binary classifier based on the Limited-Memory Warping LCSS [21].  
scheme. The nearest neighbor algorithm is preserved, where the squared Euclidean distance was selected as a distance function. More formally, a quantization step is defined as follows:  
\[Q_{c}(x(t))=\underset{i=1,\ldots,|\mathcal{L}_{c}|}{\operatorname{argmin}}\frac{ \|x(t)-L_{ci}\|^{2}}{\operatorname{max}}\frac{\|x(t)-L_{ci}\|^{2}}{\|L_{ci}-L_{ ck}\|^{2}} \tag{7}\]  
where \(Q_{c}(.)\) assigns to the sample \(x(t)\) the index of a discretization point \(L_{ci}\) chosen from the discretization scheme \(\mathcal{L}_{c}\) associated with the gesture class \(c\). Therefore, the stream is converted into a succession of discretization points.
## 2 The Proposed Method and Theoretical Background  
### Overview of the Study  
Parameter identification of TV systems is an important part of dynamic analysis. How to globally decompose the multi-component signal is the premise for addressing the parameter identification problem of a multiple DOF TV system under random excitation. The decomposed component signals contain valuable system information that can be utilized for further investigation. Furthermore, effectively modeling the decomposed component signals in the time domain, based on the short-term linear variation assumption, holds the key to enhancing the parameter identification accuracy. Consequently, this paper proposes a novel physical parameters identification method of TV systems under random excitation. To visually illustrate the proposed method, a detailed flowchart is presented in Figure 1, and the major study contents are as follows.  
By employing multi-level KRRD, the collected raw acceleration signal can be decomposed into a set of acceleration components representing modal components. In this step, the multiple DOF parameter identification problem can thus be transformed into a single DOF parameter identification problem. At the same time, the SNR of the signal is improved by eliminating noise interference. Here, the single decomposed acceleration component is considered as the analysis object, which can be represented by a linear combination constructed by PCT basis functions. Firstly, the corresponding first and second derivative basis functions are derived using the basis function of PCT. Secondly, PCT is performed on the decomposed acceleration component based on the obtained derivative basis functions, and the corresponding velocity and displacement signals can be reconstructed, which exhibits significant potential in resolving TV differential equations. Then, the cross-correlation theory is employed, and a linear model is established between the TV physical parameters and the acceleration signal, thereby transforming the problem of solving the differential equation of the TV system into solving a linear equation. Finally, the TV physical parameters are identified by solving the simultaneous linear equations.
## 4 Design
### Overview  
Here, similar to in D-TSN, the PSFP is used to change the flows' priority based on a time schedule. In particular, using a suitable configuration of the PSFP, the property of changing the queue to which the incoming frames are enqueued is exploited in order to implement the EDF policy within the switches. The frames are scheduled online and this allows D-ST to support ED traffic.  
Both the switches and the end nodes need to be fully compliant with both the [27] and the [24] standards. At the physical layer, full-duplex Ethernet connections at a fixed data rate (\(\delta\)) are also required. D-ST transmissions use a given number of transmission queues in each port. Such a value, here referred to as \(Q\), is chosen, taking into account that the highest priority queue is reserved to ST, and therefore it is not used for EDF scheduling. Moreover, if BE traffic is present, to make sure that the RT frames would always be transmitted with a higher priority than the BE frames, the latter would be reserved the lower priority transmission queue and would exploit the spare bandwidth left by the traffic with higher priority. In this case, also the lower priority queue would not be used for EDF scheduling.  
The switches are required to be fully compliant with the PSFP, while the end nodes are not required to have this property. In D-ST, a given number of stream gates are used to enable deadline-based frame priority. Such a number, here called \(N\), is configured to be a multiple of \(Q\) and its maximum value depends on the adopted hardware. Each flow \(F_{i}\) has a relative deadline \(D_{i}\).  
According to the PSFP standard, a frame can be mapped to a stream gate according to the value of one or multiple Ethernet frame fields. In D-ST the mapping is based on the 12-bit VLAN Identifier (VID) value. In particular, the stream gate control list rules have to be defined so that the IPV of each stream gate is shifted at regular time intervals. This way, as time passes, the incoming frames traversing a given stream gate (i.e., the one corresponding to the frame VID value) are enqueued to higher priority queues. For each VID, the stream gate control list follows a cyclical priority shifting. In D-ST, the period of the cyclical shifting is equal to the number of stream gates used multiplied by the time unit. Moreover, the number of rules is equal to the number of queues used.  
The following subsections describe, in detail, the frame transmission mechanisms for the end nodes and for the switches, respectively.
## 3 Methodology
### Decomposition Modules  
#### 3.1.1 Wavelet Transform Block  
The object edges represent abrupt changes around smooth regions and are distributed in the high-frequency signals of an image. For the motion and coverage of clouds, cloud edges offer crucial information. Wavelet transform is a powerful analysis tool widely used in image signal processing [32]. Compared with Fourier transform, it can capture frequency properties without location information loss. Therefore, we build the wavelet transform block (WTBlock) to extract frequency features and provide the RSI encoder with additional cloud details. As shown in Figure 2, the image signals are passed through high-pass and low-pass filters, sequentially from the horizontal and vertical directions. The high-pass filter (HPF) is to extract high-frequency components such as edges, while the low-pass filter (LPF) is to obtain low-frequency components for approximation. We summarize this 2D discrete wavelet transform as  
\[\mathbf{I}_{\mathrm{LL}},\mathbf{I}_{\mathrm{LH}},\mathbf{I}_{\mathrm{HL}}, \mathbf{I}_{\mathrm{HH}}=\mathrm{WTBlock}(\mathbf{I}), \tag{2}\]  
where image signal \(\mathbf{I}\) is decomposed into four components: the approximation \(\mathbf{I}_{\mathrm{LL}}\) (passing through LPFs in both directions), and three details (\(\mathbf{I}_{\mathrm{LH}},\mathbf{I}_{\mathrm{HL}},\mathbf{I}_{\mathrm{HH}}\)) in horizontal, vertical, and diagonal orientations, respectively.  
Figure 1: Model architecture of DualET.