esa-sceva/satcom-chunk-collection huggingface dataset:
# Labeled Multi-Bernoulli Filter Joint Detection and Tracking of Radar Targets
## 3 The S-LMB-GOM Filter
### The Property Analysis and the Set of the Point Spread Function  
We analyze the property of the point spread function \(h_{A}^{i}\left(\mathbf{x}\right)\) defined in Equation (6) and set the size of \(T\left(\mathbf{x}\right)\).  
We assume that \(R=5\) m, \(B=\pi/180\) rad, \(D=1\) m/s in Equation (6) and \(c_{0}=\left[R,B,D\right]\). The sampling interval of the measurement space is equal to the resolution. The corresponded range, azimuth and Doppler cell of the target \(\mathbf{x}\) is denoted as \(c_{t}\left(\mathbf{x}\right)=\left[r\left(\mathbf{x}\right),b\left(\mathbf{x }\right),d\left(\mathbf{x}\right)\right]\) and \(c_{t}\left(\mathbf{x}\right)\) is not necessarily the position of a measurement cell as it is not necessarily located in the sampling position. Find the closest cell \(c_{i_{0}}\left(\mathbf{x}\right)\) to \(c_{t}\left(\mathbf{x}\right)\) in the measurement space, then find the nearest \(k\) cells around cell \(c_{i_{0}}\left(\mathbf{x}\right)\) in each dimension to construct the set \(C\left(\mathbf{x}\right)\) where \(C\left(\mathbf{x}\right)=\left\{c_{i}\left(\mathbf{x}\right):i\in T\left( \mathbf{x}\right)\right\}\) is the corresponded position cell set of the index set \(T\left(\mathbf{x}\right)\) and \(c_{i}\left(\mathbf{x}\right)=\left[r^{m},b^{n},d^{q}\right]\). \(C\left(\mathbf{x}\right)\) is a \(k\times k\times k\) matrix and falls within a certain distance from \(c_{i_{0}}\left(\mathbf{x}\right)\). A simple example to show the relations of different cells is given in Figure 6. \(T\left(\mathbf{x}\right)\) is the index set of \(C\left(\mathbf{x}\right)\) and contains \(k^{3}\) index elements. Define the confidence level \(\beta\) as  
\[\beta=\frac{\sum\limits_{i\in T\left(\mathbf{x}\right)}1_{U}\left(i\right)h_{A }^{i}\left(\mathbf{x}\right)}{\sum\limits_{i\in U}1_{U}\left(i\right)h_{A}^{i} \left(\mathbf{x}\right)} \tag{29}\]  
where \(U\) represents the complete index set of the measurement space and \(0\leq\beta\leq 1\). When \(\beta\) is close to one, we think that \(T\left(\mathbf{x}\right)\) can replace \(U\) almost without loss.  
From the result of Table 1, the cell number \(k\) of \(C\left(\mathbf{x}\right)\) in each dimension can be set to 9 when the simulation data is generated and \(T\left(\mathbf{x}\right)\) contains 729 index elements; the cell number \(k\) of \(C\left(\mathbf{x}\right)\) can be set to 5 to reach a compromise between computational efficiency and accuracy when calculating likelihood function and \(T\left(\mathbf{x}\right)\) contains 125 index elements; the cell number \(k\) of \(C\left(\mathbf{x}\right)\) can be set to 2 or 3 to reduce the impact on the other close targets when resetting the associated measurements into noises and \(T\left(\mathbf{x}\right)\) contains 8 or 27 index elements.  
Figure 5: The true cardinality and the estimated cardinality before and after applying the proposed method.
# Fuzzy Logic-Based Geographic Routing Protocol for Dynamic Wireless Sensor Networks
## 3 FLGR Protocol
### Selection Process Based on Fuzzy Logic
#### 3.5.2 Fuzzy Reasoning Rules and Fuzzy Reasoning Rule Base  
In the fuzzy logic system, node parameter \(D\), \(\eta\) and \(\Omega\) are the input variables, and we can get the comprehensive assessment of CN as the output variable, which can be denoted by \(E\). In this paper, five fuzzy sets (very low, low, middle, high, and very high) represent that the comprehensive assessment is very low, low, middle, high, very high, respectively. Therefore, there are a total of \(3\times 3\times 3=27\) fuzzy reasoning rules in the fuzzy reasoning rule base. A part of fuzzy reasoning rule base is presented as follows (The whole table is presented in Appendix A):  
As given in Table 5, taking the node with the ID of 2 for example, its parameter \(D\) belongs to the middle fuzzy set, parameter \(\eta\) belongs to the middle fuzzy set, and parameter \(\Omega\) belongs to the middle fuzzy set, as a result, the output parameter \(E\) belongs to the middle fuzzy set through the fuzzy logic system.  
Figure 5: Subordinating degree function of parameter \(\Omega\).
# Grating-like Terahertz Metasurface for Large-Deflection-Angle Beam Manipulations
## 2 Principle and Design
The large beam-deflection angle is mainly owing to the subwavelength metasurface element and the resultant large values of the ratio of working wavelength to the grating constant. Figure 1: Schematic configuration of the metasurface grating. The geometric parameters of the unit cell are as follows: \(a\) = 160 \(\upmu\)m, \(b\) = 40 \(\upmu\)m, \(c\) = 6 \(\upmu\)m, \(g\) = 16 \(\upmu\)m, \(w\) = 16 \(\upmu\)m, \(D_{x}\) = 400 \(\upmu\)m, and \(D_{y}\) = 200 \(\upmu\)m. The metasurface consists of seven layers, which from top to bottom, are quartz, complementary split-ring resonator (CSRR), Polyimide (PI), liquid crystal (LC), PI, metal ground, and silicon substrate. The thickness of the metal layer (gold), PI, and LC are 300 nm, 90 nm, and 45 \(\upmu\)m, respectively. Silicon substrate and ultrathin quartz (which are not shown in the schematic) are used to support the metal ground and metallic CSRR, respectively, and PI layers engineered with parallel grooves are contributed to the pre-orientation of liquid crystal molecules.
## 1 Introduction  
Biosurfactants are surface-active compounds that are produced by various micro-organisms, e.g., bacteria, yeast, and fungi [1; 2]. Their amphipathic characteristics make it possible to enhance the solubility of hydrophobic organic materials, leading to various applications to detergents, emulsions, dispersion systems, fabric softeners, and paints over food texture [3; 4]. Compared to chemical surfactants, biosurfactants show several advantages, i.e., high bioavailability, biodegradability, and low toxicity.  
Surfactin is a lipopeptide biosurfactant that consists of four isomers, i.e., surfactin A-D, with different chain length and branches of its hydroxyl fatty acid components. It is mainly produced by a rod-shaped Gram-positive bacterium, _Bacillus subtilis_, and shows biological potential for antimicrobial, antiviral, and insecticidal activities [5; 6]. However, it has not been industrialized because of the high cost of production and purification [7]. Although the cost competitiveness of surfactin is lower than that of chemical surfactant, it should be used in some high-value products such as cosmetics, medicine, and biocontrol agents, due to increasing demand for safety.  
From an industrial point of view, high surfactin productivity in a submerged culture of _B. subtilis_ is necessary to step forward toward commercial production. Various fermentation strategies, e.g., strain development, media optimization, trace-element optimization, fermentation types, reactor design, and cultivation conditions, were investigated to increase surfactin yield [8]. Among these parameters, the cultivation conditions of dissolved oxygen and oxygen-transfer efficiency have been actively investigated for surfactin production. Sufficiently dissolved oxygen had a positive effect on surfactin concentration in the submerged culture of _B. subtilis_[9; 10; 11]. Increases in agitation speed resulted in increasing oxygen-transfer efficiency and surfactin yield. However, the accumulation of foam at high agitation speed resulted in poor surfactin productivity, limiting the increase of agitation speed in the vessel [8].  
We previously isolated the surfactin-producing _B. subtilis_ Y9 [6], whose culture filtrate contained surfactin isomers (C\({}_{14}\)[Leu\({}_{7}\)], C\({}_{14}\) [Val\({}_{7}\)], and C\({}_{15}\)[Leu\({}_{7}\)]). In this study, _B. subtilis_ Y9 fermentation parameters, including oxygen-uptake rate, oxygen supply, volumetric productivity, and sporulation, were investigated under various agitation speeds. In addition, the effect of the use of equipment to enhance oxygen supply on the parameters was investigated to enhance surfactin concentration in the submerged culture of _B. subtilis_ Y9.
###### Abstract  
Phosphogypsum (PG)-based cemented paste backfill (PCPB) is a widely used method for the large-scale disposal of phosphoric acid waste. However, fluoride contaminants in PG pose a significant risk of groundwater pollution. Effective pretreatment methods have been developed to mitigate this issue, but there is limited research on the specific pretreatment standards needed to control fluoride leaching. This study investigates the impact of initial fluoride content (IFC) in PG samples on the strength and leaching behavior of PCPB to establish appropriate pretreatment standards. Pure CaSO4 2H2O and NaF were used to simulate PG samples with varying IFC levels. The findings indicate that lower IFC leads to stronger PCPBs after 28 days of curing. To ensure compliance with Chinese national standards, the initial fluoride concentration must be below 0.0093 wt% to prevent excessive leaching. Microlevel analyses reveal that fluoride influences the hydration reactions of C2S and C3S in cement, affecting the formation of C-S-H and Ca(OH)2. Fluoride is primarily immobilized in PCPB as CaF2, which can either form gypsum crystals within the complex structure or be adsorbed onto hydration products. This research provides crucial insights into the pretreatment standards required for controlling fluoride leaching, offering a foundation for the industrial application of PG.  
phosphogypsum; initial fluoride content; cement paste backfill; phosphogypsum; pretreatment; fluoride immobilization +
Footnote †: journal: applied sciences
## 3 Proposed Scheme to Secure Device Layer Network
### Proposed Secure Communication Protocol
#### 3.2.3 Authentication Phase  
The authentication of any device takes place with a valid Message Authentication Code (MAC) because the smart contract is deployed on all base stations, and allows finding the hash of the precedent block that contains the identifier of the device. This mechanism brings more security to the network. The authentication phase is composed of seven steps (Figure 9) depicted as follows:  
Step 1: The device generates its private key, and calculates its public key.  
Step 2: The device computes Z where Z = Hash(Mac \(|\) PSK \(|\) DPbK).  
Step 3: The device sends DID, Z, DPbK to the BS.  
Step 4: The BS checks the timestamp (the value of the timestamp must be within an acceptable range of the current time). The BS after that checks if the device is already registered, if not, it cancels the authentication, else, it checks if the device was already authenticated (by checking its existence in the authenticated devices table), if it does not exist in the table, the BS collects the device information by calling the function GetAllTheInfo(DID) from the smart contract, then it computes X where X = DID XOR PH, after that, it checks whether Y is equal to hash(X \(|\) PsK' \(|\) DPbK \(|\)\(|\) Tstmp), the authentication fails if the comparison does not hold, otherwise the device is successfully authenticated.  
Step 5: The BS generates its private key, and computes its public key where BSPbK = BSPrK*G, and calculates the session key where SK = BSPrK*DPbK.  
Step 6: The BS encrypts SK and BSPbK using the device public key, the result will be sent to the device  
Step 7: The device decrypts M4 and computes the session key, the result will be compared to the session key received in M4, the BS is successfully authenticated if the comparison holds, otherwise the authentication fails.
## 3 Methodology  
The study technique was organized, commencing with the collecting and preparation of the WSN-DS dataset. This included balancing the dataset using SMOTE and manual resampling, then scaling the features and labeling them. The next step required creating a deep learning architecture that incorporated Conv1D, GRU, and Self-Attention components. The model was constructed and trained using improved hyperparameters before being verified on a different test set. Finally, performance was assessed using a variety of measures, including accuracy, precision, recall, F1-score, ROC-AUC, detection time, and latency. An ablation research and node-based partitioning were also carried out to  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref** & **Feature Set** & **Approach** & **Classification** \\ \hline
[14] & 23 & COA-GS optimized IDNN & Multi-class classification \\ \hline
[15] & 50 & Stacked Autoencoder + Unsupervised K-Means & Impersonation class \\ \hline
[16] & 100, 50 & Stacked Autoencoder + Deep K-Means Clustering (k = 2) & Normal vs. impersonation \\ \hline
[17] & 71 & Stacked Autoencoder, 3-layer DNN, 7-layer DNN & Multiclass \\ \hline \hline \end{tabular}
\end{table}
Table 2: Deep learning approach.  
determine component contribution and distributed detection capacity. The proposed intrusion detection model in Figure 1 is designed to effectively classify multi-class attacks within WSNs using a hybrid deep learning architecture that integrates Conv1D, GRU, and Self-Attention mechanisms. The model begins with a comprehensive data (packet arrival rate, number of hops, source and destination node IDs, traffic rate, sequence numbers, routing path length) preprocessing pipeline applied to the WSN-DS dataset, including SMOTE for class balancing, one-hot encoding of categorical labels, standardization of input features, and reshaping of data for temporal modeling. The model architecture initiates with a 1D Convolutional layer to capture local spatial patterns in the input sequences, followed by a MaxPooling layer to reduce dimensionality and computational cost. Next, two stacked GRU layers are employed to extract long-term dependencies from the time-series data. A dropout layer is inserted between GRUs to mitigate overfitting. To enhance the model's focus on relevant temporal features, a Self-Attention layer is integrated, allowing dynamic weighting of sequence components based on their importance. The attention outputs are then passed through a GlobalAveragePooling1D layer and a Dense softmax output layer for final classification. The model employs the Adam optimizer, utilizes categorical crossentropy as the loss function, and measures accuracy as the main metric. The hyperparameters consist of 10 training epochs, a batch size of 62, and a dropout rate of 0.3. This amalgamation of convolutional, recurrent, and attention-based elements endows the model with the capacity to identify nuanced patterns and abnormalities in WSN data, resulting in elevated detection accuracy and strong generalization performance.  
Figure 1: Proposed model.
## 6 Application of Algorithm to Various Scenarios for Fault Identification: Case Studies
### Effect of Variation in Sampling Frequency  
The frequency used for sampling the current signals might affect the performance of the protection scheme. Hence, the algorithm was tested for various sampling frequencies. It is observed that during the event of phase-A to ground fault, the FI corresponding to the faulty phase-A was found to be \(9.7\times 10^{8}\), \(2.8\times 10^{9}\) and \(3.1\times 10^{9}\) with sampling frequencies of 1.92 kHz, 3.84 kHz and 7.68 kHz, respectively. Hence, for sampling frequency below 3.84 kHz, the peak magnitude of FI decreases and it may go below the set threshold magnitude. A sampling frequency higher than 3.84 kHz improves the accuracy of protection scheme but increases the fault detection time due to the bulky input dataset. Therefore, the optimum sampling frequency was selected as 3.84 kHz for the protection scheme.
## 1 Introduction
### Hot Climate Energy Requirements and Codes  
In 2008, air conditioning equipment represented 19.7% of the electricity consumed in the residential sector [38]. Regarding 2018, 30% of the residential sector's energy consumption was used to achieve adequate comfort levels in hot climates [39]. To reduce energy consumption in the residential sector, various public policies for efficient use and energy savings have been implemented in Mexico. These efficiency measures have allowed an average user's consumption in temperate climates to decrease since 2001 until reaching consumption levels similar to those of 1989 in 2014. On the other hand, the consumption of average users in hot climates has increased by 19.4% for this same period (1989-2014). These differentiated phenomena are explained, to a large extent, because the most effective public policies have been aimed at improving the efficiency of electrical equipment (lighting and refrigerators). In contrast, they have made very little progress regarding the envelope of buildings, which is what determines in a more significant way the energy consumption in the houses located in regions with a hot climate [40].  
The NOM-020-ENER-2011 criterion is based on comparing the heat gain of a projected building, which must be less than a reference building. The heat gain of the reference building is established in this standard and depends on the climatic zone where the construction is planned. This code does not specify a minimum R-value for the materials that comprise the walls or ceilings, but this physical property must be considered to calculate heat gains. On the other hand, the standard NMX-C-460-ONNCCE suggests R-values for roof and walls in three categories (minimum habitability and energy savings) for each climate zone [34]. However, this standard is not mandatory in comparison to NOM-020. Although NOM-020 does not establish an R-value, this property can be determined indirectly from the heat fluxes considered for the design of the reference building. In the particular case of Hermosillo, this reference value is 2.1 m\({}^{2}\)K/W. In comparison, NMX-C-460-ONNCCE suggests a minimum R-value of 1.00 m\({}^{2}\)K/W and qualifies as an energy-saving level when it presents an equivalent value of 1.40 m\({}^{2}\)K/W or higher.  
Although the current regulations influence the minimum R-value of the walls and ceilings of new homes, they have not been established in the present reality due to various factors. However, some rules and programs allow obtaining social housing subsidies and more generous financing at more affordable interest rates. They include envelope elements as part of a series of \"eco-technologies\" that must be considered to grant these kinds of support. Thus, these programs are the real factor that promotes insulating materials in social housing construction [40].
# Monitoring and Data Analysis in Small-Scale Landslide Physical Model
## 4 Results
### Water-Related Sensors
The rising of the ground water level continued until the water level reached the surface of the slope foot. Seepage through the slope was established with suction decreasing, the slope was saturated from the bottom layers to the slope surface, leading to the loss of the soil strength in the foot of the slope. At depths of 6 and 18 cm, the soil temperatures proportionally fell with the rising of volumetric water content as the water level reached certain layers of the slope that were used as a confirmation of establishing of the ground water level in the slope.
# Controlling Remotely Operated Vehicles with Deterministic Artificial Intelligence
## 1 Introduction
### Proposed Innovations  
This section succinctly and directly lists the innovations presented in this manuscript.  
1. First-time development and presentation of deterministic artificial intelligence applied to yaw control of a remotely piloted submersible (the Seabotix vLBV 300 in this case): includes both self-awareness statements and newly presented parameterization of optimal learning.
2. First instantiation of cubic polynomial autonomous trajectory generation supplied to deterministic self-awareness statements;
3. Illustration of milli-degree tracking accuracy on the very first heading change of an aggressive shaped-square wave command, and an additional sixty percent (root mean square) error reduction by the third heading change of the square wave (illustrating the efficacy of optimal learning to eliminate startup transient behavior).
# Development of Cementitious Mortars for Aerial Additive Manufacturing
## 2 Materials and Methods
### Rheology, Fourier Transform Infrared Spectroscopy (FTIR) and Calorimetry  
To assess whether a suitable balance between workability and buildability may be linked to quantifiable rheological data, oscillatory stress tests to determine the Complex modulus G*, elastic-deformation component storage modulus G' and viscous-flow component loss modulus G\" were carried out on the mortars for two hours immediately following mixing using a TA Instruments DHR-2 rheometer (TA, Hertfordshire, UK). Disposable aluminium smooth parallel plates with a 25 mm diameter upper geometry and 40 mm lower plate were used with a geometry gap of 1000 \(\upmu\)m. The displacement-controlled oscillatory tests used a small angular velocity of 5.0 \(\times\) 10\({}^{-5}\) radians per second, ensuring the material stayed within the linear viscoelastic region. Frequency was kept constant at 1 Hz (an angular frequency of 6.28 radians per second) and a temperature of 25 \({}^{\circ}\)C was maintained. The samples tested for each mix consisted of material taken from the same batches created for the workability and buildability tests with the deposition device.  
Fourier Transform Infrared Spectroscopy (FTIR) was carried out to identify the functional groups of the polyol resin, with particular emphasis on the purpose of identifying and confirming the presence of hydroxyl groups. The instrument used was a Perkin Elmer Frontier 91175 instrument with a diamond Attenuated Total Reflectance head (Pike Technologies Inc., Madison, WI, USA). The resolution was 2 cm\({}^{-1}\) and the wave number parameters ranged between 600 cm\({}^{-1}\)-4000 cm\({}^{-1}\).  
Calorimetry tests were conducted over a period of 48 h to assess how the differing constituents affected the heat evolution rate of the exothermic hydration reactions of the mixes. 40 g samples of each mix were placed into sealed containers immediately following mixing and inserted into the chambers of a Calmetrix I-Cal 4000 (Calmetrix, Boston, MA, USA) high precision isothermal calorimeter linked with CalCommander v.1.67 software. The temperature of the chambers was maintained at 20 \({}^{\circ}\)C. Two samples of each mix were tested, and the mean results were taken.