esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Neural Network and Long Short-Term Memory for Electric Power Load Forecasting
## 3 Proposed Electric Power Load Forecasting Methodology
### Proposed DNN Model  
The proposed DNN model comprises three layers (Figure 9), namely the input, hidden, and output layers. The input layer (\(i_{t-1}\)) is composed of one node. The hidden layer is composed of 100 nodes. The output layer (\(o_{t}\)) is composed of one node for load forecasting. As described in Section 3.1, the input layer (\(i_{t-1}\)) is the final dense layer with a value of dense_4: Dense. The proposed DNN was implemented using four hidden layers (\(H_{1}\)-\(H_{4}\)).
# Interpretable Optimization: Why and How We Should Explain Optimization Models
## 3 Problem Reduction and Surrogate Model Building
### Decomposition Approaches
Benders' decomposition is well suited for problems exhibiting a clear division between complicating and non-complicating variables, often reflected in a constraint matrix where a small set of linking constraints connects otherwise independent blocks. It scales effectively by iteratively solving a relatively small master problem alongside multiple parallelizable subproblems, although performance may degrade as the number of Benders' cuts increases. Dantzig-Wolfe decomposition is the most efficient for problems with a block-angular constraint matrix, wherein several independent submatrices are coupled through a limited set of global constraints. This structure facilitates scalable column generation, making the method particularly appropriate for large-scale linear programs. Lagrangian relaxation achieves strong scalability when the coupling constraints are dualized, yielding fully separable subproblems that may be solved in parallel. However, its reliance on dual optimization may result in slow convergence and solutions that are not feasible in the primal space. ADMM is highly scalable in large convex optimization problems where the constraint structure supports variable splitting or consensus formulations. In such cases, subproblems can be solved independently, with coordination achieved through simple consistency constraints, making ADMM particularly effective for distributed and parallel computing environments. Table 1 summarizes the decomposition methods mentioned in this section. Each method is particularly well suited for a different type of problem structure. These decomposition methods are essential for solving large-scale linear, mixed-integer, and nonlinear optimization problems, particularly in logistics, energy systems, and network design. Unfortunately, these decomposition approaches may obscure interpretability, as they obscure the original formulation. In Benders' decomposition, the fact that cuts describe the recourse function can be repurposed to provide an interpretation of the consequences of first-stage (or complicating) decisions. However, the potential for interpretability in Dantzig-Wolfe decomposition is lower, as the variables are introduced sequentially and the link between them and the initial problem statement is obscured. On the contrary, Lagrangean Relaxation can simplify the problem, and dual multipliers can provide interesting insights for interpretation. Finally, ADMM also has an interesting interpretability potential in its decomposition of the problem. However, as explained above, in virtually all applications, the focus of stochastic decomposition techniques is efficiency rather than interpretability, and additional research should focus on tailoring these techniques to improve the explainability of optimization results.
# Feasibility Assessment of a Fine-Grained Access Control Model on Resource Constrained Sensors
## 3 Access Control Model  
In this section, a brief description of the access control model subject to the feasibility assessment is presented to facilitate the test-bed implementation and performance analysis understanding. Further details are conveyed in [7], but the analyzed E2E access control model is based on an efficient policy language and codification, which are specifically defined to gain expressiveness in the authorization policies and to ensure viability in very-constrained devices. In addition to the policy language, the access control model conveys the E2E security association between two mutually authenticated peers through a security protocol named Hidra. This Hidra protocol, based on symmetric key cryptography, relies on a three-party architecture to enable multi-step authorization as well as the instant provisioning of a dynamic security policy in the CDSs and delegated accounting. Consequently, the CDS is enabled to enforce an ad-hoc context-based security policy not only at the security association establishment but also on every derived resource access during such association. Moreover, the active security policy might be updated under any condition detected through accounting during the security association lifetime, which enables the dynamic revocation and cancellation of permissions.
# On Reliable and Efficient Data Gathering Based Routing in Underwater Wireless Sensor Networks
## 11 Simulation Results and Discussions
### Non Optimized and Energy Optimized Protocols
#### 11.3.2. Packets Loss and PAR  
To minimize energy consumption we employ a constraint Equation (20a) that limits the predecessors attached to every node. However, there exists a tradeoff between energy utilization and packet drop rate. It is to note that, although energy remains conserved in optimized protocols but at the cost of high packet drop rate due to inability of sender node to find the next hop node. RE-AEDG shows the minimum packet drop rate because AUV itself comes in the vicinity of nodes and shortens  
Figure 12. Number of alive nodes per interval in comparison to energy optimized protocols.  
Figure 13. Energy consumption in energy optimized and non optimized protocols.  
their communication path. Eventually, lowered packet collision rate and improved reception of data at destination. Additionally; more the number of packets being generated more is the possibility of successful packet reception at sink. Since the packet drop rate is measured only for alive nodes, therefore; the down fall of the packet drop curves in Figures 14 and 15 shows the rate at which the network becomes sparse.
## 3 Testing Results and Analysis  
### Strength Characteristics Testing  
#### 3.1.1 Unconfined Compressive Strength Testing  
The UCS test results of phosphogypsum-modified lime-ash stabilized soil are presented in Figure 5. Several observations can be derived from Figure 5: (1) The addition of phosphogypsum significantly influences the UCS of the lime-ash-stabilized soil. When the phosphogypsum content is 4%, the 7-day UCS of the lime-ash stabilized soil (0.97 MPa) increases by 67.2%, the 14-day UCS (2.49 MPa) increases by 3.3 times, and the 28-day UCS (4.2 MPa) increases by 3.1 times. However, when the phosphogypsum content exceeds 4%, the UCS value begins to decrease. (2) The UCS of the lime-ash stabilized soil gradually increases with an extended curing period. For the lime-ash stabilized soil, its strength exhibits relatively rapid growth in the early curing stages but slower growth in the later stages. Conversely, for the lime-ash stabilized soil with added phosphogypsum, there is a significant acceleration in strength growth during the early curing period, followed by even faster growth during the mid-curing period (from 28 days to 60 days). However, this phenomenon diminishes in the later stages (from 60 days to 90 days) of curing as the strength of the lime-ash stabilized soil gradually stabilizes. Additionally, for the specimens with a phosphogypsum content exceeding 8%, there is no significant increase in UCS values. The reason for these observations is that the addition of phosphogypsum enhances the cohesion and stability of soil particles, allowing their strength to continue increasing over an extended period. However, the change in strength is related to the generation of ettrin-gite. Ettrinigite can enhance soil strength within a certain range, but an excess of ettrinigite can cause expansion, disrupt soil stability, and lead to a reduction in soil strength [21]. Therefore, this study initially determines the optimal phosphogypsum content to be 4%.
# Service Life Prediction and Life Cycle Costs of Light Weight Partitions
## 3 Methodology
### Review of the Physical and Visual Condition of Components at Different Ages
& & at oneThe performance concept implies the translation of basic user requirements into flexible and technically non-prescriptive performance requirements and criteria. The performance level of interior partitions is thus defined as the ability to meet functional and durability requirements, rather than technical specifications that extend over the expected service life, and encompasses factors such as structural integrity, resistance to wear and tear, maintenance needs, and visual appearance. The performance level of each partition is assessed using a five-grade Component Performance (CP) scale, which is categorized into five levels. A top grade of 5 (100%) indicates optimal performance, signifying a component without any signs of wear and a perfect performance condition. Conversely, a bottom grade of 1 (20%) signifies complete dysfunction or severe deterioration. Each grade on the scale is accompanied by descriptive definitions, facilitating consistent and accurate assessments across different surveyors. These definitions provide a detailed verbal explanation for each level, as shown in Table 1, ensuring uniformity and clarity in the rating process. This refined grading system effectively captures the range of partition conditions, extending from excellent to poor, allowing for a comprehensive evaluation of component deterioration and performance level. construction, which are inherent in construction defects and do not progress over time. The ICP for partitions is determined by a formula, as expressed in Equation (1). \[ICP=\frac{(\sum_{i=1}^{n}CP_{i})}{n} \tag{1}\]  
The \(CP_{i}\) and \(n=5\) are the ratings for the inherent construction deviations, which includes evaluating deviations and imperfections that are inherent in their construction and do not evolve over time. These include:  
* Deviations from flatness or undulations on the partition surface. * Deviations from vertical or horizontal alignment. * Noticeable or visible seams and joints. * Quality of the intersection between the partition and exterior walls. * Quality of the intersection between the partition and the ceiling. This formulation provides a standardized assessment of the initial quality and performance of partitions.
## 2 Study Area and Data Sources
### Data and Processing
Comparative analysis of eigenvalues was performed using the Pauli, Cloude-Pottier, Freeman-Durden, and Yamaguchi decomposition methods [24; 25; 26; 27; 28; 29], obvious differences were observed in the characteristics of various types of objects (Figure 4). The \(x\)-axis represents different land cover types in the image, the \(y\)-axis represents the eigenvalues after polarization decomposition, and r, g, and b represent the eigenvalues of the RGB spectral bands after polarization decomposition, respectively. Among these, after the decomposition using the Pauli method, the eigenvalues of the various types were observed to be significantly different from one another, which is conducive to better distinguish the types of objects. The RGB bands of Pauli decomposion correspond to three physical scattering mechanisms (i.e., odd-scattering, even-scattering, and volume scattering mechanisms) and can be used to qualitatively analyze the physical significance of the land covers in the polarimetric SAR image. The subsequent processing of ALOS 2 data is based on the results of the Pauli decomposition. Figure 2: Different cloud-cover Landsat OLI data. (**a**) Cloudiness: 26.28%; (**b**) Cloudiness: 8.43%. Figure 3: Polarization filtering of ALOS-2 data. (**a**) Original image; (**b**) refined Lee (window \(5\times 5\)); (**c**) refined Lee (window \(7\times 7\)). Without high-precision ground-control locations, firstly, the Range-Doppler (RD) geo-location model is established on the basis of the unoptimized parameters extracted from the SAR metadata; afterward, the indirect positioning method is used to obtain the SAR image coordinates corresponding to the ground target points. Subsequently, the geometric relationship between the ground and SAR images obtained using the indirect localization method employs a certain backscatter coefficient model in order to generate a simulated SAR image whose texture is similar to that of a real SAR image [30]. On the basis of the simulated SAR image, the geometric relationship between the ground and real SAR images can be obtained to complete the orthorectification. Owing to the large relief of the mountainous terrain, the local incident angle of the pixel exerts considerable influence. Different local incident angles will result in a large difference in the radar backscattering coefficient; this large difference is highly unfavorable for land-cover-information extraction. In this study, according to the high-precision digital elevation model (DEM) data generated on the basis of terrain correction and the local incident angle, the backscatter coefficient values of all the pixels are normalized to remove the local incident angle. Figure 5 depicts the ALOS 2 data both before and after the terrain correction.
## 3 Results of the Literature Review and Discussion
### Review of Studies of External Enclosures Considering Operating Impacts  
When studying the energy efficiency of external enclosures, it is necessary to consider the strength properties of the structure under operating loads. Essentially, when calculating such loads, engineers consider one or a combination of loads according to the specific natural and climatic conditions; see Figure 16.  
A review of the studies on the impact of the operating loads shows that this problem is also popular among the scientific community; see Figure 17.  
Figure 16: Operating loads on external enclosures. The literature review focusing on the period since 2001 yielded 741 publications, of which 132 were reviews and 609 were research articles. Over the specified period, the number of publications increased by almost 30 times. Moreover, all of these works were published in well-known international journals; see Figure 18.  
Figure 17: Statistics of studies on operating loads of external enclosures. Figure 18: Pie chart of scientific journals publishing research on the operating loads of facade systems. According to the presented analysis, the most relevant literature regarding the operating loads, as shown in Figures 15-17, is grouped and presented in Table 2. Its relevance is highlighted in the form of a word cloud in Figure 19.
## 3 Dataset  
### Dataset Description  
In this study, two public datasets were selected to verify the effect of the SELMWK algorithm, namely:  
1. Dataset 1: The gas delivery platform facility at the Chemical Signaling Laboratory of the Institute of Biological Circuits, University of California, San Diego, was selected using sensors from [PERSON] et al. and published this dataset on the UCI Machine Learning Repository [26,27]. The dataset has a total of 13,910 measurements, using a sensor array consisting of 16 gas sensors to collect data for 6 different gases at different concentrations. The measurement system platform provides the versatility to obtain the concentration of desired chemicals with high accuracy and reproducibility, thereby minimizing common errors caused by human intervention and allowing focus on chemical sensors. The resulting dataset consists of records from six different pure gaseous substances, with 8 data points selected from each specific sensor as feature points over the entire time series, so a set of data yields a total of 8 \(\times\) 16 = 128-dimensional feature vectors. The dataset was divided into 10 batches in total, which were collected in different months. Table 1 shows the details of dataset 1.
2. Dataset 2: Select the gas data collected by [PERSON] et al. using 8 MOX sensors [28]. They exposed the sensor arrays to 10 different ethanol, methane, ethylene, and carbon monoxide concentrations. The duration of each experiment is 600 s, and the conductivity of each sensor is 100 Hz, so a set of data has 60,000 \(\times\) 8 = 480,000 data. In the experiments of this chapter, in order to train a classifier with better applicability and make it better to deal with gases of different concentrations, the same type of gas with different concentrations is set as the same sample, and only the type of target gas is analyzed. For classification prediction, the labels of the same gas samples with different concentrations are set to the same class. Table 2 is the information of 8 MOX sensors, and Table 3 is the number of samples and concentrations of different gases in this data set.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Batch ID** & **Month** & **Acetone** & **Acetaldehyde** & **Ethanol** & **Ethylene** & **Ammonia** & **Toluene** & **Total** \\ \hline Batch 1 & **1-2** & 90 & 98 & 83 & 30 & 70 & 74 & 445 \\ Batch 2 & 3–10 & 164 & 334 & 100 & 109 & 532 & 5 & 1244 \\ Batch 3 & 11–13 & 365 & 490 & 216 & 240 & 275 & 0 & 1  
3. It can be known from Tables 2 and 3 that the 8 MOX sensors have good sensitivity to the four gases to be measured so that the response curve of the sensor can be avoided because the gas to be measured does not react with the sensor array. The lack of more useful information will eventually make the gas classification effect less effective. At the same time, it can be seen from the number of samples that the number of four samples is relatively uniform so that the data skew problem will not be caused due to the excessive amount of data of certain sample gas, thereby reducing the accuracy of gas classification.
## 4 Simulations Results and Discussion with Performance Evaluation
### Parameters to Optimize
#### 4.4.1 Linear Optimization for Energy Minimization  
In SPF-Three and BFS-SPF-Three, the cross nodes broadcast the data packets using IP broadcasting mechanism. However, the relay nodes unicast the data packets. Due to which, minimum EC is required to increase the lifespan of the network. According to the authors in [19], maximum energy is consumed in transmission and reception phase. Nodes consume maximum energy during the active mode and minimum energy during idle mode. However, EC is less as compared to the energy dissipation during transmission and reception. Therefore, the idle node's EC is not considered. The main objective of  
Figure 13: RPER.  
EC is expressed in Equation (14) for the total transmission time (\(t_{tot}\)) to prolong the network lifetime. Main points of Equation (14) are as follows:  
\[Min\sum_{t=1}^{t_{tot}}\ E(t),\ \ \ \ \forall\ t\ e\ R. \tag{14}\]  
* Energy of sensor nodes is less than and equal to the initialized energy of nodes (because of limited energy resources)
* The DR is less than and equal to DR which make the bits erroneous for transmission. Otherwise, the PER will increase which results in packet drop and packet needs to be retransmitted
* Transmission energy is greater than and equal to energy of the sensor node for transmission
* Link quality should be good for reliable data transmission without error to prevent the packet drop
* Total '\(E_{t}\)' and '\(E_{r}\)' is calculated using Equations (15) and (16) \[Min\sum_{DP=1}^{Nodes}\ E_{t},\ \ \ \ \forall\ i\ e\ R,\] (15) \[Min\sum_{DP=1}^{Nodes}\ E_{r},\ \ \ \ \forall\ i\ e\ R.\] (16)  
Graphical Analysis of '\(E_{t}\)' and '\(E_{r}\)'  
Let us consider a scenario in which initial energy of the nodes be 6 J, transmission power of 0.66 mW, PL of 500 bytes, DR of 10 kbps with distance ranges from 100-300 m and the receiving power of 0.395 mW. So,  
* 3.3 mJ \(\leq E_{t}\leq\) 9.9 mJ
* 1.98 mJ \(\leq E_{r}\leq\) 5.93 mJ  
Figure 14 shows feasible region of proposed protocols. Every point in feasible region indicated the feasible solution of the proposed protocol. In vertices of feasible solution, point \(P_{1},P_{2},P_{3}\) and \(P_{4}\) cover the corner points lies within the range of initial energy. Therefore, all the solutions are valid. Every value of energy is taken from this feasible solution while transmitting and receiving of data packets.  
Figure 14: EC Feasible Region for Both Proposed Protocols.
# Designing Hydrogel-Based Bone-On-Chips for Personalized Medicine
## 4 Hydrogel-Based Bone-On-Chips
### Hydrogels  
Given their remarkable biocompatibility and non-toxicity, natural hydrogels are often selected to imitate the native ECM in BOC systems (Table 1). The hydrogel choice depends on the specific environment to be modeled. For example, the formation of a fibrin matrix immediately after a bone injury makes fibrin gels a suitable model to study the healing of bone. By embedding osteoprogenitor cells, a fibrin gel can generate a healing bone-mimicking (BMI) microenvironment and induced the functional formation of a microvascular network [95]. Moreover, in the same project, the same microenvironment recreates a bone inflammatory model with the addition of a macrophage-like cell line (RAW264.7 cells). In search of a model representing mature bone, fibrin was mixed with type I collagen which increased the hydrogel stiffness and its mechanical stability [96]. Due to its ubiquitous presence in the bone matrix, hydrogel-based BOC systems normally used type I collagen when culturing primary Human Osteoblasts (HOBs) (Table 1). The collagen mechanical properties varied between the different applications of the BOCs. In general, collagen concentration was higher when the system meant to induces osteogenic differentiation and mineralization [88; 97], while it was lower when studying osteoblast migration [84; 85]. Collagen-based hydrogels have a fibrous architecture whose interaction with the embedded cells alters both the hydrogel mechanical properties and the cellular activity within the BOC. It has been demonstrated that collagen fiber reorientation due to interstitial fluid flow increases osteoblasts migration [84]. Moreover, osteoblasts remodel the collagen hydrogel with proteolytic enzymes, the metalloproteinases, which in turn regulate cell migration through the matrix [85]. Hydrogels mechanical properties can be enhanced with composite systems. Besides mixing with fibrin, collagen I was also mixed with Matrigel prior to load within BOCdevices (Table 1). Given that Matrigel is a gelatinous protein mixture, a composite hydrogel made of collagen I and Matrigel provides a variety of structural proteins and growth factors resembling the native ECM [63]. Although the incorporation of a bioactive inorganic phase in hydrogels has been widely investigated to boost hard tissue regeneration [98], bone formation was never assessed in hydrogel-based BOC while incorporating calcium phosphates or bioglases. However, HA nanoparticles were incorporated in fibrin hydrogels and loaded into BOC systems and showed the inhibitory effect HA in cancer cell migration [99] and a stimulative effect on endothelial sprouting [100]. Instead of incorporating inorganic phases, recent hydrogel-based BOCs recreated mineralized microenvironments by combining a collagen I hydrogel with osteogenic factors [101] or by culturing osteo-differentiated MSCs that deposited newly mineralized matrix [97].