esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Conclusions  
The development and evaluation of a virtual assistant designed to support users within an academic setting by utilizing a generative language model enhanced by the Retrieval-Augmented Generation method are presented in this study. The VA was subjected to testing across several dimensions, including response accuracy, contextual relevance, user satisfaction, and the ability to minimize hallucinations--issues in which information is generated by the model without a basis in the query context. The results indicate that context-based responses significantly improve accuracy and reduce hallucinations, affirming the RAG method's importance in providing grounded, relevant answers.  
Although the VA has observed positive performance in structured and context-rich queries, challenges exist in handling complex, enumerative, and structured data queries, along with occasional hallucinations. The limitations identified indicate areas for future refinement, including enhancing the model's context retrieval accuracy and integrating capabilities to interpret structured data, such as schedules or tabular information. Furthermore, implementing feedback mechanisms to facilitate continuous model improvement based on real-world interactions is expected to enhance user trust and reliability.  
The adaptability of VA across various contexts is demonstrated, indicating potential applications beyond academic environments. Such applications may include healthcare, legal advisory, and customer support settings, where providing accurate and contextually relevant responses is essential. Future work is expected to address current limitations, ensure ethical safeguards, and expand multimodal interaction capabilities. Through these advancements, a robust, user-centered tool is poised to be developed by the VA, which is adaptable to diverse professional environments and capable of supporting users with reliable, contextually relevant information. This research demonstrates the potential of AI-driven virtual assistants in enhancing user support and information retrieval across various domains, paving the way for more reliable and context-aware applications.  
**Author Contributions:** Conceptualization, Z.M. and M.F.; methodology, Z.M. and M.F.; software, L.M.; validation, Z.M. and L.M.; formal analysis, M.F.; investigation, M.F.; resources, Z.M.; data curation, G.D.; writing--original draft preparation, Z.M. and L.M.; writing--review and editing, L.M. and G.D.; visualization, G.D. and L.M.; supervision, Z.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Efficient Feature Selection Using Weighted Superposition Attraction Optimization Algorithm
## 2 Methodology
### Metaheuristics
#### 2.3.1 Differential Evolution  
DE is a population-based search algorithm in continuous space, initially propounded by [PERSON] and [PERSON] [11]. It starts by initializing a population of search agents. The search agents move about in the search space using pre-defined mathematical rules to adjust their positions with respect to the position of the existing agents. The new position of any agent is only accepted if it provides an improvement over the existing ones. This process of improvement is carried out iteratively over generations until the termination criterion is met. The pseudocode for DE can be generally represented, as shown in Algorithm 1.  
```
Objective function \(f(x)\), \(x=(x_{1},\ x_{2},\ldots,x_{d})\) Initialize population \(G=0\); Initialize all NP individuals \(Iterations<Maximum\) Iterations  FOR \(i\ \leftarrow\ 1\) to NP  GENERerate three individuals randomly based on the condition that \(r_{1}\
eq r_{2}\
eq r_{3}\
eq i\)  MUTATION Form the donor vector using the formula: \(v_{i}=x_{r_{1}}+F(x_{r_{2}}-x_{r_{3}})\)  CROSSOVER The trial vector \(u_{i}\) is developed either from the elements of the target vector \(x_{i}\) or the elements of the donor vector \(v_{i}\) as follows: \[u_{i,j}=\left\{\begin{matrix}v_{i,j}\text{if}r_{i,j}\leq CRorj=j_{rand}\\ x_{i,j}\text{otherwise}\end{matrix}\right.\] where \(i=\{1,\ldots,NP\},j=\{1,\ldots,D\}\), \(CR\) is the crossover rate, \(r_{i,j}\ \sim\cup\ (0,\ 1)\) is random number generated for each \(j\) and \(j_{rand}\in\{1,\ldots,D\}\) is random integer to ensure that \(u_{i}\
eq x_{i}\) in all cases  EVALUATE if \(f(u_{i})\ \leq\ f(x_{i})\) replace the individual \(x_{i}\) with the trial vector \(u_{i}\) \(Iterations=Iterations+NP\)  END \(G=G+1\)  END
```  
**Algorithm 1**Differential evolution  
Figure 1: Wrapper-based feature selection framework.
# Address Space Layout Randomization Next Generation
## 1 Introduction  
Address Space Layout Randomization (ASLR) is a well-known, mature and widely used protection technique which randomizes the memory address of processes in an attempt to deter forms of exploitation [1], which rely on knowing the exact location of the process objects. Rather than increasing security by removing vulnerabilities from the system, as source code analysis tools [2] tend to do, ASLR is a prophylactic technique which tries to make it more difficult to exploit existing vulnerabilities [3].  
Unlike other security methods [4, 5], the security provided by ASLR is based on several factors [6], including how predictable the random memory layout of a program is, how tolerant an exploitation technique is to variations in memory layout and how many attempts an attacker can make practically.  
ASLR is a wide spectrum protection technique, in the sense that rather than addressing a special type of vulnerability, as the renewSSP [7] does, it jeopardizes the programming code [8]of the attackers independently of the vector [9] used to inject code or redirect the control flow. Similarly to other mitigation techniques, ASLR mitigates code execution attacks by crashing the application, and so the attack is degenerated into a denial of service.  
The ASLR is an abstract idea which has multiple implementations [10; 11; 12; 13], though there are important differences in performance and security coverage between them. We therefore need to make a clear distinction between the core concept of ASLR, which is typically described as something which \"_introduces randomness in the address space layout of user space processes_\" [14], and the exact features of each implementation.  
Although ASLR is more than 14 years old [15], it is still a very effective protection against modern attacks [16; 17] there is still a lot of work and innovations to be done, both on the design and the implementation. For example, the implementation of the KASLR (Kernel ASLR), which loads kernel code and drivers or modules in random positions [18] is still under development and improvement in some ecosystems such as the IoT [19] and other 32-bit low powered devices.  
This paper is organized as follows: a full taxonomy of the ASLR is presented in Section 2, followed by a critical analysis of limitations of current ASLR designs in Section 3. Later, we introduce ASLR in Section 4, a tool t automatically analyze and detect ASLR weaknesses. Section 5 presents the weaknesses found in Linux, PaX and OS X. Then in Section 6 we describe the constraints that must be taken into account when designing a practical ASLR. In Section 7 we propose a new ASLR named ASLR-NG, which overcome all limitations and weaknesses. Section 8 evaluates our proposal in a real implementation, and finally, Section 9 concludes the paper.
## 7 Summary  
In this paper, a Tensor-train decomposition-based hybrid beamforming method for millimeter-wave massive MIMO/FSO in UAVs with RIS networks was investigated. Firstly, the system channel model of millimeter-wave massive MIMO/FSO in UAVs with RIS was established. The application of a Bidirectional Gated Recurrent Unit (BIGRU)-Attention neural network model in an FSO system can effectively solve the problem of light PE caused by UAV jitter. The multidimensional received signal of millimeter-wave massive MIMO in UAV with RIS was represented as a low-rank tensor signal by Tensor-train decomposition. The problem of maximizing the system spectral efficiency was decomposed into two subproblems of optimizing the RIS phase shift matrix and solving the hybrid beamforming matrix. The hybrid channel matrix was decomposed by Singular Value Decomposition. The RIS phase shift matrix was optimized. Finally, the hybrid beamforming matrix was solved by decoupling the transceiver and transmitter using an improved hybrid beamforming method based on Phase Extraction Alternating Minimization.  
For future work, we plan to expand and improve from the following aspects: Study more optimization algorithms and neural network models for different unmanned aerial vehicle communication scenarios, to further improve system performance; consider evaluating the performance and stability of our method under more complex atmospheric conditions and dynamic changing environments of unmanned aerial vehicles; explore the application of our method in other wireless communication systems, such as satellite communication and mobile communication, to verify its applicability in different fields; and conduct research on how to reduce the computational complexity and energy consumption of our method in actual hardware implementation to achieve efficient and sustainable communication systems.  
Conceptualization, X.Z. and P.F.; Methodology, X.Z.; Software, P.F. and J.L.; Validation, X.Z. and J.C.; Formal Analysis, X.Z. and P.F.; Investigation, X.Z. and J.C.; Resources, X.Z. and J.L.; Data Curation, J.L.; Writing--Original Draft Preparation, P.F.; Writing--Review and Editing, Y.W.; Visualization, J.L.; Supervision, X.Z.; Project Administration, X.Z. and Y.W. All authors have read and agreed to the published version of the manuscript.  
Figure 10: Maximum Doppler shift performance impact for each method.  
**Funding:** This research was funded by Shanghai Capacity Building Projects in Local Institutions, grant number 19070502900, Science and Technology Commission of Shanghai Municipality, grant number 22142201900.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Model Based Robust Predictive Control of Ship Roll/Yaw Motions with Input Constraints
## 2 Problem Formulation
### Ship Lateral Motion Model Control with Rudder-Fin Joint System
\begin{array}{ccc}0&-Y^{\prime}_{\phi}&0\\ 0&(W^{\prime}\overline{GM^{\prime}}-K^{\prime}_{\phi})&0\\ 0&-N^{\prime}_{\phi}&0\end{array} \right], \tag{5}\]  
Due to the limited space in this paper, refer to [28] for more definition details of the elements in coefficient matrices above. It is assumed that the inflow angle of the rudder is equal to acting angle of the rudder \(\delta\) which is mathematically regarded as a \"small rudder\" limitation in controller design of rudder reduction damping (RRD) system. The full rudder angle actually goes up to \(35^{\circ}\) in a practical steering gear. The'maximum angle of the \"small rudder\" \(\delta_{\max}{=}\;20\), and the maximum angle of fin stabilizers \(\alpha_{\max}{=}\;22\). In addition, one has the following kinematic equations (assuming \(q=\theta=0\))  
\[ \begin{array}{l}\dot{\phi}=p\\ \dot{\psi}=\cos(\phi)r\approx r\end{array}  \tag{6}\]  
that can be augmented to the system model. Thereby the nonlinear model Equation (2) together with Equation (6) can be written by defining the state vector as \(\mathbf{x}:=\left[ \begin{array}{cccc}\mathbf{x}^{\prime}&\mathbf{p}^{\prime}&\mathbf{r}^{ \prime}&\phi&\mathbf{\psi}\end{array} \right]^{T}\), \(\mathbf{y}:=\left[ \begin{array}{cccc}\mathbf{\phi}&\mathbf{\psi}\end{array} \right]^{T}\), \(\mathbf{x}=\mathbf{A}_{c}\mathbf{x}+\mathbf{B}_{c}\mathbf{u}+\mathbf{d}_{c}\) (7) \(\mathbf{y}=\mathbf{C}\mathbf{x}\) where \(\mathbf{A}_{c}=\left[ \begin{array}{cc}(\mathbf{A}_{c11})&(\mathbf{A}_{c12})\\ (\mathbf{A}_{c21})&(\mathbf{A}_{c22})\end{array} \right]\), \((\mathbf{A}_{c11})=-\mathbf{M}^{\prime-1}\mathbf{N}^{\prime}\), \((\mathbf{A}_{c12})=-M^{\prime-1}G^{\prime}(\cdot,[2,3])\), \((\mathbf{A}_{c21})=\left[ \begin{array}{cc}0&\mathbf{u}_{0}/L&0\\ 0&0&\mathbf{u}_{0}/L\end{array} \right]\), \((\mathbf{A}_{c22})=\left[ \begin{array}{cc}0&0\\ 0&0\end{array} \right]\), \(\mathbf{B}_{c}=\left[ \begin{array}{cc}\mathbf{B}_{c1}&\mathbf{0}\end{array} \right]^{T}\), \(\mathbf{B}_{c1}=\mathbf{M}^{\prime-1}\mathbf{b}^{\prime}\), \(\mathbf{C}=\left[ \begin{array}{cccc}0&0&0&1&0\\ 0&0&0&0&1\end{array} \right]\), \(\mathbf{d}_{c}=\mathbf{M}^{\prime-1}[\mathbf{F}^{\prime}_{w}+\mathbf{\alpha}^{\prime}(x),0,0]^{T}\). Here the notation \((\cdot,[2,3])\) denotes the 2 nd and 3 rd columns of the matrix. In our work, a discrete-time model is considered for the control purpose, which can be discretized from the above continuous model by setting the sampling period as \(T_{\mathrm{S}}=0.2\)s.
# Thermoresponsive Behavior of Magnetic Nanoparticle Complexed pNIPAm-_co_-AAc Microgels
###### Abstract  
Characterization of responsive hydrogels and their enhancement with novel moieties have improved our understanding of functional materials. Hydrogels coupled with inorganic nanoparticles have been sought for novel types of responsive materials, but the efficient routes for the formation and the responsivity of complexed materials remain for further investigation. Here, we report that responsive poly(_N_-isopropylacrylamide-_co_-acrylic acid) (pNIPAm-_co_-AAc) hydrogel microparticles (microgels) are tunable by varying composition of co-monomer and crosslinker as well as by their complexation with magnetic nanoparticles in aqueous dispersions. Our results show that the hydrodynamic diameter and thermoresponsivity of microgels are closely related with the composition of anionic co-monomer, AAc and crosslinker, \(N\),_N_\({}^{\prime}\)-Methylenebisacrylamide (BIS). As a composition of hydrogels, the higher AAc increases the swelling size of the microgels and the volume phase transition temperature (VPTT), but the higher BIS decreases the size with no apparent effect on the VPTT. When the anionic microgels are complexed with amine-modified magnetic nanoparticles (aMNP) via electrostatic interaction, the microgels decrease in diameter at 25 \({}^{\circ}\)C and shift the volume phase transition temperature (VPTT) to a higher temperature. Hysteresis on the thermoresponsive behavior of microgels is also measured to validate the utility of aMNP-microgel complexation. These results suggest a simple, yet valuable route for development of advanced responsive microgels, which hints at the formation of soft nanomaterials enhanced by inorganic nanoparticles.  
microgel; magnetic nanoparticle; electrostatic complexation; thermoresponsivity +
Footnote †: journal: Applied Physics Letters  
1
## 7 Applications for Flexible Wearable Antennas  
With current monitoring systems, users need wearable gadgets more. They are rising rapidly in healthcare, which will benefit society. Physiological signs can detect any disease early. Intelligent wearables can reduce health issues including illness control and cost. Clinical instruments are usually non-wearable for measurement, so researchers have concentrated on smart designs that enable low-profile, sturdy, and high-performance wearable devices. Standard body-centric communications applications are recognized by IEEE 802.15 [119]. Figure 16 illustrates the numerous applications of wearable antennas in different industries. Transparent antennas, along with antennas that are worn, display several uses, including but not limited to IoT applications, integrated circuits, solar energy systems, smart city initiatives, digital displays, vehicle-to-vehicle communications, and the healthcare sector [29].  
Wearable antennas have important uses in a collection of sectors, including Body Area Networks (BANs) and wearable gadgets. Additionally, these antennas are meant to be incorporated into clothing, implanted in the body, or even tattooed on the skin, which provides a broad variety of options for communication [119]. For example, a crescent-shaped wearable antenna that was created for the ISM band at 5.4 GHz offers comfort and flexibility because it uses denim fabric as a substrate. There is also an example of a tiny, conformal textile antenna that operates in the 2.45 GHz ISM band. This antenna is appropriate for wristband applications and has an optimal performance even when it is in close contact with the human body [29]. These antennas have been designed to fulfill characteristics, such as having the factor of a compact form, being biocompatible, being flexible, and experiencing reliable performance. As a result, they are perfect for applications that include wearable technology [62].
# Complex-Valued Phase Transmittance RBF Neural Networks for Massive MIMO-OFDM Receivers
## 2 Background
Space-time block codes (STBCs) combine multiple symbols from a digital modulation, creating a block of symbols. The components of this block (i.e., matrix of symbols) are indexed by the transmitting antenna and the transmitting time. At the transmitter, STBC decoding is performed in linear processing. Another technique is the space-time labeling diversity (STLD), a variation of STBC that takes two bit-streams and outputs two pairs of symbols. Two symbols in each pair are transmitted by two transmit antennas in two time slots, which results in full-diversity and half-rate [50]. In addition, STLD only works with a limited number of transmit antennas.
# Experimental Demonstration of a Multichannel Elastic Wave Filter in a Phononic Crystal Slab
## 4 Conclusions  
In summary, we have demonstrated an elastic multichannel filter for Lamb waves, which used three different phononic waveguide defects inside a square array of cylindrical pillars mounted on the top of a plate. The device was fabricated using 3D metal printers based on aluminium alloy, _AlSi7 Mg_06. The band gap and the selective waveguiding properties have been experimentally demonstrated using a scanning laser Doppler vibrometer (PSV-500 3D HV) to image the out-of-plane displacement field and using a micro system analyzer (MSA-500) to measure the transmission of the acoustic waves inside each of the three waveguiding channels. The device shows a complete band gap for _Lamb_ wave gap centred at 300 kHz with a relative band-width of 30%. Tailorable waveguides are realized inside this phononic crystal by inserting several space gaps between pillars allowing the transmission of three frequency bands of 280 kHz \(\pm\) 7%, 293 kHz \(\pm\) 8% and 308 kHz \(\pm\) 7%. The splitting of the acoustic signal to corresponding channels was achieved with a high rejection level of 60 dB. Each waveguide appears to transmit the wave exclusively to their cuttoff frequencies without a significant loss to other waveguide routes, signifying the demultiplexing capabilities of the proposed design, which can be used in droplet manipulation for biological applications.  
Figure 6: Comparison between simulation and experiment for surface cartography displacement in z-direction for (**a**) \(W_{3}\) = 0.88 mm at 274 kHz (**b**) \(W_{2}\) = 1.22 mm at 288.4 kHz (c) \(W_{1}\) = 1.52 mm at 314 kHz.  
**Author Contributions:** Conceptualization, M.A. and A.K.; investigation, M.A.; resources, K.S.S.; writing--original draft preparation, M.S.F.; writing--review and editing, A.R.M.Z. and A.C.; supervision, A.K.; All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Publication Acceleration Fund HICOE AKU254, Ministry of Higher Education of Malaysia and Double-Degree PHD scholarship from French Embassy in Malaysia.  
**Acknowledgments:** This work was supported by the EIPHI Graduate School (contract \"ANR-17-EURE-0002\"), French ambassy in Malaysia. We thank _Polytech_ staff for assistance with the optical measurment technique and [PERSON] for comments that greatly improved the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Atomic Layer Deposition of Inorganic Thin Films on 3D Polymer Nanonetworks
###### Abstract  
Atomic layer deposition (ALD) is a unique tool for conformally depositing inorganic thin films with precisely controlled thickness at nanoscale. Recently, ALD has been used in the manufacture of inorganic thin films using a three-dimensional (3D) nanonetwork structure made of polymer as a template, which is pre-formed by advanced 3D nanofabrication techniques such as electrospinning, block-copolymer (BCP) lithography, direct laser writing (DLW), multibeam interference lithography (MBIL), and phase-mask interference lithography (PMIL). The key technical requirement of this polymer template-assisted ALD is to perform the deposition process at a lower temperature, preserving the nanostructure of the polymer template during the deposition process. This review focuses on the successful cases of conformal deposition of inorganic thin films on 3D polymer nanonetworks using thermal ALD or plasma-enhanced ALD at temperatures below 200 \({}^{\circ}\)C. Recent applications and prospects of nanostructured polymer-inorganic composites or hollow inorganic materials are also discussed.  
atomic layer deposition; conformal deposition; low temperature; nanostructure; nanofabrication +
Footnote †: journal: Applied Physics Letters
## 3 Methodology
### Features Extraction
#### 3.1.3 Fused Features  
The fusion of deep-learned and hand-crafted features was aimed to provide a holistic description of the images. As mentioned previously, several studies have reported that fusing specific hand-crafted features with images can improve the performance of CNNs [30; 32]. For this purpose, the extracted deep-learned features were concatenated with the hand-crafted features, namely LBP, HOG, and PCA, yielding fusion of HOG and deep-learned, fusion of LBP and deep-learned, and a fusion of PCA and deep-learned features. The fused features are then fed to the classification model, as shown in Figure 1.