esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### FTIR-ATR Results for TPS/DES Films Obtained via Thermocompression (Test 1)
For CCU mixtures there is no relation between swelling or dissolving degree. However, for BaU the higher urea content in the material is, the higher the swelling and dissolving degrees will be. The samples obtained via extrusion (eTPS) defragmented in water, due to their higher hydrophilicity than thermocompressed TPS, caused there to be a higher amorphous structure and better distribution of the plasticizer in the polymer matrix. Surface contact angle measurement results indicate that both types of ammonium salt, as well as urea content in the DES, affect the parameter. For TPS with CC, the higher U content is, the higher contact angle of the film surface will be. Similar results were also obtained for TPS with U/glycerol mixtures [22]. However, for samples with Ba, there is no linear relation with the urea content. Comparing thermocompressed and extruded samples, eTPS exhibits higher values of the parameter.
# Integrated Design Methodology of Automated Guided Vehicles Based on Swarm Robotics
## 4 Case-Study: Application to the Design of AGVs
### PHASE 1: SysML Modeling  
4.1.1. Design Requirements  
AGVs are deployed in many different application domains and vehicle types have increased to meet the customer's needs. These types of vehicles are used in the manufacturing, automotive, warehousing, food, chemical, and healthcare industries [43; 44]. This variety of applications specifies the general-purpose system requirements. The first SysML diagram to be addressed is the requirements diagram for the AGV model shown in Figure 10. As shown in this figure, a traceability relation \"satisfy\" is added to ensure traceability between the different design views. Indeed, when dealing with complex systems design, this traceability is essential because it helps in verifying that the requirements are met for each component of the system and for the system as a whole.  
The design of AGV systems is costly in terms of money and time because it should take into consideration the whole system life cycle, including the installation phase and logistics. Indeed, defining the AGV traffic rules and the path map is a time-consuming operation that could increase both installation and operation costs. In addition, one of the main requirements for the design of AGVs systems is efficiency. It is the main reason for adopting an AGV system for industrial logistics. In addition, the use of AGV introduces flexibility into the logistics system. Finally, AGVs need to be intrinsically safe, to never cause injuries to humans. Some major AGV design requirements have been listed above, but the list could be as large as this depending on specific needs. In addition, emerging design requirements may appear during the design cycle depending on the decisions made and the choices of solutions and technologies used. At this level, we limit ourselves to the major requirements which allow us to begin the study of the behavior of AGVs and, in particular, their behavior in swarms.  
Figure 10: Requirement diagram of automatically guided vehicles (AGV) in SysML.
# Conversion between Logic and Algebraic Expressions of Boolean Control Networks
## 3 Conversion of Expressions of Boolean Functions
### From Logic Expression to Algebraic Expression  
There exists a one-to-one correspondence between the truth table and the structure matrix of a Boolean function.  
**Lemma 4** ([29]).: _If the truth table of Boolean function (1) is \((s_{0}\,s_{1}\;\ldots\;s_{2^{n}-1})^{\intercal}\), then the structure matrix of \(f\) is_  
\[M_{f}=\begin{bmatrix}s_{2^{n}-1}&\ldots&s_{1}&s_{0}\\ 1-s_{2^{n}-1}&\ldots&1-s_{1}&1-s_{0}\end{bmatrix}.\]  
The structure matrix of a Boolean function can be constructed from the minterm canonical form.  
**Theorem 1**.: _If the minterm canonical form of Boolean function (1) is shown in (2), then the structure matrix of \(f\) is_  
\[M_{f}=\delta_{2}[c_{2^{n}-1},\ldots,c_{1},c_{0}], \tag{8}\]  
_where_  
\[c_{i}=\left\{\begin{array}{ll}1,&i\in\{i_{1},i_{2},\ldots,i_{k}\},\\ 2,&i\in\{0,1,\ldots,2^{n}-1\}\backslash\{i_{1},i_{2},\ldots,i_{k}\}.\end{array}\right.\]Proof.: The minterms presented in (2) are in one-to-one correspondence with the logical value 1 in the truth table. Suppose that the truth table of \(f\) is \((s_{0}\ s_{1}\ \ldots\ s_{2^{n}-1})^{\intercal}\). Then \(s_{i}=1\) if and only if \(m_{i}\) is a minterm of \(f\), i.e., \(i\in\{i_{1},i_{2},\ldots,i_{k}\}\). It follows from Lemma 4 that \(c_{i}=1\) if and only if \(s_{i}=1\). Hence, \(c_{i}=1\) if and only if \(i\in\{i_{1},i_{2},\ldots,i_{k}\}\).  
Let us compute the structure matrix of the Boolean function given in ([29], Example 2.5) using Theorem 1.  
**Example 1**.: _Consider the Boolean function_  
\[f(x_{1},x_{2},x_{3})=(x_{1}\leftrightarrow-x_{2})\wedge(x_{2}\leftrightarrow- x_{3})\wedge(x_{3}\leftrightarrow-x_{1}\wedge-x_{2}).\]  
_The minterm canonical form of \(f\) is \(m_{2}(=\ x_{1}x_{2}\ s_{3})\). Then, in (8),_  
\[c_{i}=\left\{\begin{array}{ll}1,&i=2,\\ 2,&i\in\{1,3,4,5,6,7,8\}.\end{array}\right.\]  
_Hence, the structure matrix of \(f\) is \(M_{f}=\delta_{2}[2,2,2,2,1,2,2]\)._  
Comparing the above example with ([29], Example 2.5), the method based on the minterm canonical form can avoid the complex calculations of STP.
## 3 Neural Inverse Optimal Control
### Neural Identification with Recurrent High Order Neural Networks (RHONN)
#### 3.1.2 Inverse Optimal Control  
Optimal control finds a control law for a system such that a performance criterion is minimized. The criterion is a cost functional based on the state and control variables. The solution of the optimal control leads to the HJB equation which solution is not an easy task. Inverse optimal control is an alternative to optimal control, avoiding the HJB equation solution. For the inverse optimal control approach a stabilizing feedback control law based on a priori knowledge of a control Lyapunov function (CLF), is designed first, and then it is established that this control law optimizes a cost functional, then, the CLF is modified in order to achieve asymptotic tracking for given trajectory references [15]. The existence of a CLF implies stability and every CLF can be considered as a cost functional. The CLF approach for control synthesis has been applied successfully to systems for which a CLF can be established, such as feedback linearizable, strict feedback and feed-forward ones [15].  
The system (1) is supposed to have an equilibrium point \(\mathbf{x}(0)=\mathbf{0}\). Moreover, the full state \(\mathbf{x}(k
# Query-Based Cascade Instance Segmentation Network for Remote Sensing Image Processing
## 2 Methods
### Multistage Cascade Task Head  
In SAR images, the edges of inshore ships often appear blurry and are susceptible to background factors, such as coastlines and town buildings, which can affect the segmentation process. Similarly, in optical images, distinguishing objects such as aircraft and runways, baseball stadiums, and surrounding vegetation from the background can be difficult due to their high similarity, leading to lower quality edge segmentation.  
To overcome these limitations, in our work, we have adopted the cascade structure of the detection heads mentioned in Section 1. This cascading transmission enables the network to gradually extract and learn more abstract and high-level features, excelling in handling challenging tasks. But, unlike that, we replace the original detection head with a dynamic detection head and a dynamic segmentation head. This dynamic head design is motivated by dynamic convolution [43].
# Reliable and Energy-Efficient LEO Satellite Communication with IR-HARQ via Power Allocation
## 5 Conclusions  
In order to ensure reliable and energy-efficient transmission in the LEO satellite communication systems, we introduced an IR-HARQ-VPA scheme. In this scheme, the NI-FFT method was proposed to calculate the outage probability accurately. Based on this, the genetic algorithm was used to optimize the transmission power sequence. The energy efficiency of the proposed scheme is much higher than that of the equal power transmission scheme and the previously proposed variable-power allocation scheme. Simulation results for a realistic satellite orbit also indicate that the energy efficiency can be significantly improved by the proposed IR-HARQ-VPA scheme in a LEO satellite communication window. Our future work will take variable-rate transmission into account. Through the joint rate and power optimization in each IR-HARQ round, the energy efficiency of LEO satellite communication systems can be further improved.  
[PERSON] researched the literature, built the model, derived the formulas, and carried out the simulations. She also finished the whole manuscript. [PERSON] revised the manuscript and assisted with the integrity of the entire study. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Beijing Natural Science Foundation (L202003).  
The authors declare no conflict of interest.
## 2 Materials and Methods  
Figure 1 shows the methodological process for failure prediction employed in this study. The figure describes the process from raw data acquisition to feature cleaning and selection, proposed model development, and fault prediction. The raw data undergoes a data cleaning and filtering process, with variables related to the target component then being selected. These variables are the input to the LSTM-MA-AE model, which reconstructs the output signal for each variable. Subsequently, the predicted signals enter a fault prediction system where they are compared with each actual signal, and the reconstruction error is used to calculate the abnormality score. This system generates an alarm when a significant discrepancy is detected, allowing it to predict and alert of the possibility of a failure.
## 2 Materials and Methods
### Test Procedure  
Data collection was included in the rowers' habitual training sessions. Since the measurements were scheduled to accommodate the rowers' availability, they were performed under varying environmental conditions. On average, the flow of water was 11.3 \(\pm\) 9 m\({}^{3}\)/s (based on the characteristics of the basin, we did not collect when the flow was above 50 m\({}^{3}\)/s), the temperature was 16 \(\pm\) 4 \({}^{\circ}\)C, and the wind speed was 4.5 \(\pm\) 1.6 m/s (we did not collect when the speed exceeded 10 m/s). The rowers first performed a warm-up session, consisting in reaching the body of water designated for the measurement. This covered a distance of about 2 km, during which the rowers rowed at low intensity, changing the sequencing of the movements (e.g., only legs, only arms) and including a few accelerations towards the end. Afterwards, they were asked to row at their competitive stroke rate with their usual rowing technique to closely simulate the mid-section of a 2000 m race. The body of water used for training only allowed for 500 m in straight line and the presence of other rowers on the basin severely limited the capability of the boat with the sensors' receiver (see \"Data collection\") to follow the rower for the entire session, as the waves generated by the boat could severely disturb the other rowers' training. For this reason, once the rowers reached their target stroke rate and the sensors were within range, it was decided to limit recording to 15 consecutive strokes. Thus, for each rower, three bouts of 15 strokes were recorded, as rowers have shown to have a high degree of repeatability and other studies performed their analyses on a similar number of strokes [6; 29]. Between recorded bouts, rowers had at least three minutes of rest.
# Privacy-Preserving Federated Learning Using Homomorphic Encryption
## 6 Performance Analysis
### Communication Overhead  
#### 6.2.1 Communication Overhead  
In an FL scenario involving many clients, the communication overhead has a tremendous impact on performance. If a cryptosystem is utilized to preserve data privacy in an FL-based system, the communication overhead may be more significant than sending local parameters as a plaintext. In this paper's cryptosystem, the length of the ciphertext is affected only by the key size, regardless of the length of data the client sends to the server, and the length of data must be less than the key size. Thus, the closer the data length is to the key size, the less communication overhead is incurred because more information can be conveyed in one ciphertext. In the cryptosystem, since encryption requires a modular operation with a dividend \(K^{2}\), the length of the ciphertext becomes \(K\times 2\) bits. When the key size is \(K\) bits and the length of data to be transmitted is \(L_{d}\) bits, the transmission data volume after encryption becomes \(2N/L_{d}\) times larger. As \(L_{d}\) is closer to \(K\), the transmission data volume is approximately doubled. In the proposed system, \(K\)-bit data representing multiple local model parameters are generated to reduce the communication overhead.
# A Taxonomy of Machine Learning Clustering Algorithms, Challenges, and Future Realms
## 2 Taxonomy of Clustering Algorithms
### Partitional Clustering Algorithm
#### 2.2.1 Fuzzy Clustering  
A clustering technique called fuzzy clustering was created by [59] and is based on the fuzzy set. Each pattern concurrently belongs to more than one of the fuzzy sets that form the clusters. Allocating data points to more than two clusters with varying degrees of participation in the mirrored clusters yields a non-binary relationship [22]. By allowing clusters to overlap in this manner, the fuzzy overlap is demonstrated. Fuzzy overlap counts the number of data points with meaningful membership in the overlapping clusters while reflecting the fuzziness of the cluster borders. This clustering method is useful for groupings of data points whose boundaries are fuzzy or poorly differentiated [15; 49]. Information on a point's level of affiliation in a cluster might shed light on the item's inherent relationship to those clusters.
## 6 Pipeline Safety Evaluation and Vibration Reduction Solutions
### Vibration Reduction Technology Using Empty Holes and Barrier Holes  
Because the maximum PPV of 28.32 cm/s under electronic detonator blasting exceeded the safe allowable vibration velocity, in order to ensure the operational safety of the pipeline, scientific vibration reduction design should be carried out before construction. The appropriate placement of empty holes and vibration reduction holes will help achieve safe standards for PPV.  
1. The cutting hole that is detonated first will be subjected to significant surrounding rock confinement, so it is necessary to have a vibration-reduction design for it. Placing large-diameter empty holes around the cutting hole can significantly reduce the adverse effects of surrounding rock. In this project application, ten empty holes with a diameter of 10 cm and a depth of 20 m were designed to surround the cutting holes;
2. To further reduce vibrations, a series of large pipe roofs were designed on the upper portion of the tunnel excavation profile. It can not only enhance the supporting effect in the middle interbedded rock layer but also has a good effect in blocking explosive impact [53]. In this application case, widely used on-site pipe roofs with a diameter of 159 mm and a length of 40 m have been used. Translation: Their spacing was designed to be relatively dense at 20 cm.  
After the above design, a scientific blast-damping design scheme was formed, as shown in Figure 19.  
In accordance with the above-designed damping scheme, TC-4850 wireless network velocity testers were used for tracking and monitoring during construction. The PPV of the maximum vibration direction was recorded according to a pushing distance of 1.6 m, and the monitoring result is shown in Figure 20. The maximum PPV in the monitoring result is 7.19 cm/s, which meets the standards of national safety regulations [20]. The application result can demonstrate the rationality of the above analysis and optimization design.
# Guided Semi-Supervised Non-Negative Matrix Factorization
## 2 Related Work  
### Classical Non-Negative Matrix Factorization  
Non-negative matrix factorization (NMF) is a powerful framework for performing unsupervised tasks such as topic modeling and clustering [1]. Given a target dimension \(k<\min\{d,n\}\), the classical NMF method approximates a non-negative data matrix \(\mathbf{X}\in\mathbb{R}^{d\times n}\) by the product of two non-negative low-rank matrices: the _dictionary matrix_\(\mathbf{W}=[\mathbf{w}_{1},\mathbf{w}_{2},\cdots,\mathbf{w}_{k}]\in\mathbb{R}_{\geq 0}^{d \times k}\) and the _coding matrix_\(\mathbf{H}=[\mathbf{h}_{1},\mathbf{h}_{2},\cdots,\mathbf{h}_{n}]\in\mathbb{R}_{\geq 0}^{k \times n}\). Both matrices \(\mathbf{W}\) and \(\mathbf{H}\) can be found by solving the optimization problem  
\[\operatorname*{argmin}_{\mathbf{W}\in\mathbb{R}_{\geq 0}^{d\times k},\mathbf{H}\in \mathbb{R}_{\geq 0}^{k\times n}}\frac{1}{2}\|\mathbf{X}-\mathbf{W}\mathbf{H}\|_{F}^{2}, \tag{1}\]  
where \(\|\mathbf{A}\|_{F}^{2}=\sum_{i,j}a_{ij}^{2}\) is the Frobenius norm of \(\mathbf{A}\), and the constant of \(\frac{1}{2}\) is to ease the calculation when taking the gradient. In the context of topic modeling, the dimension \(k\) is the number of desired topics. Each column of \(\mathbf{W}\) encodes the strength of every dictionary word's association with a learned topic, and each column of \(\mathbf{H}\) encodes the relevance of every topic for a given document in the corpus. By enforcing non-negativity constraints, NMF methods can learn topics and document classes with high interpretability [1; 7].
## 5 Results and Analyses
### Experimental Analyses
#### 5.2.2 Attention Mechanism Comparison  
To validate the effectiveness of our PDWA mechanism, we compare it with established attention approaches.  
**Baseline attention methods:**  
* **Uniform Attention**: Equal weights assigned to all path pairs.
* **Graph attention network (GAT)**: Using graph-based attention on entity-document graphs.
* **Metric learning attention**: Learning distance metrics through contrastive learning.
* **Standard multi-head attention**: Transformer-style attention without distance weighting.  
**Results:** As shown in Table 6, PDWA consistently outperforms all the baseline attention mechanisms, with improvements of 2.3% in F1 score over GAT and 1.8% over the metric learning approach. The semantic distance weighting proves particularly effective for long-range cross-document reasoning.
## 3 Experimental Results  
While reducing the stiffness of the valve support is possible, experimental results for this study were gathered from a valve configuration considered rigid. Across the operating window, pressure signals were assessed with respect to the presence of pressure fluctuations. As already discussed in [8; 9], using the standard deviation of pressure signals as a measure of flow instability, regions of increased instability were identified consistent to literature data [4; 10]. For a diffuser wall pressure sensor (corresponding sensor position for experimental results in Figure 3 is Diffuser 3/3, third position on the diffuser wall in streamwise direction in Figure 4 (sensors marked as \(\circ\))), the normalized distribution of average pressure fluctuations is shown in Figure 3a. Indicated by the shade and magnitude (both normalized without specific scale), pressure fluctuations increase as the opening and pressure ratio are decreased and reach maximum values around \(OR=0.05\).  
Figure 4: Flow field at \(OR=0.05\), \(PR=0.20\) (CFD, sensor positions: \(\circ\)): (**a**) time averaged; and (**b**) instantaneous.  
Figure 3: Overview of diffuser wall pressure fluctuations across the operating window and frequency analysis for \(OR=0.05\) (Experiment): (**a**) average standard deviation (normalized); and (**b**) frequency spectra for \(OR=0.05\).  
The pressure ratio at which maximum pressure fluctuations are observed decreases as the valve is closed. For a reduction of the pressure ratio for a given valve opening, a sudden reduction of pressure fluctuations can be observed for opening ratios \(OR\geq 0.05\). Based on experimental data and numerical simulations, the diffuser becomes choked and a strong shock evolves within the diffuser section. The approximate transition region is indicated by the solid lines in Figure 2(a). For choked conditions, the flow prior the shock stabilizes and pressure fluctuations are largely reduced or even disappear.  
Considering the pressure fluctuations and flow conditions, the opening ratio of \(OR=0.05\) is investigated in detail and an overview of the frequency analysis of a diffuser wall pressure signal is provided in Figure 2(b). An increase of amplitudes can be identified across all frequencies as the pressure ratio is decreased. It can be concluded that random non-periodic flow structures dominate the flow at most pressure ratios as no specific frequency is identified and only a dominance of low frequency effects is observed. While this behavior is similar for most other sensors and opening ratios, a discrete peak can be observed near \(PR=0.20\) for the opening ratio of \(OR=0.05\) at the transition to a choked diffuser. This observation in the experiment is currently unique across the operating range and the identification of the corresponding flow structure at \(OR=0.05\) and \(PR=0.20\) is the scope of the following section.
## 3 Receiver  
### Channel Estimation  
To address the challenges posed by the reduced number of pilots in a single OFDM-IM symbol and the presence of inter-carrier interference (ICI), the task of estimating the time-varying channel impulse response (CIR) is made more complex. In order to overcome these difficulties, the observation model of the channel estimator is extended to incorporate a sliding window that encompasses adjacent OFDM-IM symbols. This extension is expressed as follows:  
\[ \begin{bmatrix}\mathbf{u}_{r}^{k-1}\\ \mathbf{u}_{r}^{k}\\ \mathbf{u}_{r}^{k+1}\end{bmatrix} = \begin{bmatrix}\mathbf{G}(t,r)^{k-1}\mathbf{s }_{t}^{k-1}\\ \mathbf{G}(t,r)^{k}\mathbf{s}_{t}^{k}\\ \mathbf{G}(t,r)^{k+1}\mathbf{s}_{t}^{k+1}\end{bmatrix} + \begin{bmatrix}\mathbf{ z}_{r}^{k-1}\\ \mathbf{z}_{r}^{k}\\ \mathbf{z}_{r}^{k+1}\end{bmatrix} , \tag{10}\]  
the superscript associated with each variable indicates its position relative to the current \(k\) symbol. To perform channel estimation using this observation model, an extension of the algorithm proposed in [26; 27] is utilized. The basis expansion model (BEM) is employed to obtain a concise representation of the CIR for each transmitter-receiver coupling within the three consecutive OFDM-IM symbols in the interval. This extension can be expressed as follows:  
\[h[r,t;n,l]=\sum_{r=0}^{M_{\tau}-1}\sum_{q=0}^{M_{\tau}-1}\rho_{q,r}\phi_{q}^{I} [n]\phi_{r}^{II}[l]+\varepsilon[n,l]. \tag{11}\]  
In the given expression, \(\rho_{q,r}\) represents the coefficients, \(m=\{0,\ldots,3N_{b}-1\}\), \(M_{\tau}\) and \(M_{D}\) denote the number of functions used to expand the delay-time domain and the time domain, respectively, and \(\{\phi_{q}^{I}[n],\forall q\in[0,M_{D}-1]\}\) and \(\{\phi_{r}^{II}[l],\forall r\in[0,M_{\tau}-1]\}\) are the functions that expand the time domain and the delay-time domain, respectively. In V2V scenarios, where there is a wide range of statistical variations in Doppler and delay dispersion, discrete prolate spheroidal sequences (DPSS) are used as base functions. DPSS functions optimally concentrate energy within a finite time and bandwidth window. The modelling error in this representation within subspaces is concentrated in the term \(\varepsilon[n,l]\). To determine the number of functions required in each of the CIR domains, the approach proposed in [26; 27] is used. \[M_{\tau} =\lceil F_{s}\tau_{max}\rceil+1, \tag{12}\] \[M_{D} =\lceil 2f_{D}3N_{b}/F_{s}\rceil+1. \tag{13}\]  
In the provided equation, \(\lceil\cdot\rceil\) represents the ceiling rounding operator, \(F_{S}\) corresponds to the system's bandwidth, \(\tau_{max}\) denotes the maximum time delay spread, and \(f_{D}\) represents the maximum frequency Doppler spread.