esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review on Millimeter-Wave Hybrid Beamforming for Wireless Intelligent Transport Systems
## 4 Current Research Trends, Challenges, Solutions, and Future Research Directions
### Challenges, Solutions, and Future Research Directions
#### 4.2.3 Future Research Directions  
* **Algorithm-Hardware Co-Design:** Research that brings algorithmic design in sync with hardware capabilities will be key to optimizing performance and cost.
* **Deep Integration of AI:** AI could be deeply integrated into the communication systems for proactive interference management, predictive maintenance, and self-optimizing networks.
* **Quantum-Enhanced Technologies:** Incorporating quantum computing principles may help solve complex optimization problems related to beamforming and provide significant improvements in computational speed and efficiency.
* **Cross-Layer Optimization:** Efforts to optimize the interaction between various layers of the communication protocol stack can yield efficiency gains in hybrid beamforming systems.
* **Advanced Materials for Antenna Design:** Research into new materials that allow for more flexible and efficient antenna designs could revolutionize the way beamforming is implemented.
* **Testbeds and Prototyping:** Large-scale testbeds for real-world prototyping can help in understanding the practical challenges and performance boundaries of hybrid beamforming technologies.
* **Security in Beamforming:** As the directionality of communication increases, so does the need for secure beamforming to prevent eavesdropping and jamming. Advanced security protocols must be researched.
* **Vehicle-to-Vehicle Communication:** When both the transmitting and receiving sides are mobile, the RIS locations, number of phase shifters required, derivation of time-changing approximate/exact angles and distances, efficient capacity of the channel, and significant outage probability are things of concern. Optimizing all these parameters are still open areas of research.  
By addressing these challenges, refining solutions, and continuously pushing the boundaries of research, millimeter-wave MIMO communication can achieve its full potential, providing the necessary foundations for next-generation wireless networks.
# The Effect of Countermeasure Readability on Security Intentions
## 2 Theoretical Background
### Protection Motivation Theory, Theory of Planned Behavior, and Self-Efficacy  
Protection motivation theory (PMT) comes from research on fear appeals, which is mainly centered around how fear-arousing communication affects behavior [23]. In brief, PMT assumes that human behavior is formed through a cost-benefit analysis where risks associated with certain behavior are held up against the costs of trying to eliminate or mitigate these risks [29].  
PMT distinguishes multiple components within fear appeals. This makes it possible to determine common variables that impact attitude, which affects behavioral change [30]. These components are one's cognitive processes and are defined by coping appraisal (perceptions of the recommended coping response to the threat in question) and threat appraisal (perceptions of how threatened one believes they are). The two components each have their antecedent (coping awareness and threat awareness, respectively) and are both split up into multiple factors.  
[PERSON] and [PERSON] [10], looking at the impact of users' security awareness on desktop security behavior from a PMT perspective, adjusted the model to fit their context. That is, they translated coping awareness to countermeasure awareness (CA). Results demonstrated that there was support for their suggested model (Figure 1).  
A few years prior, [PERSON] [11] saw an overlap between the theory of planned behavior (TPB) and PMT and sought to integrate these to help explain users' compliance in information system security policy. Later, [PERSON] et al. [12] also synthesized PMT and TPB, showing elements of TPB (mainly subjective norm) are valuable to add to PMT.  
TPB is based on the premise that, when behaving in certain ways, individuals make logical, reasoned decisions by evaluating the information available to them [31]. TPB states that intention is the primary antecedent of planned behaviors. Intention is influenced by three determinants, attitude toward a behavior, subjective norm, and perceived behavioral control.  
[PERSON] and [PERSON] [32] define subjective norm as follows: \"[someone's] perception that most people who are important to him or her think he or she should or should not perform the behavior in question\". Perceived behavioral control represents \"the person's belief as to how easy or difficult performance of the behavior is likely to be\" and is based upon SE as introduced by [PERSON] in 1977 [22, 33].
## 3 Research Methodology
### Security Implications of OTP\({}^{3}\)
#### 3.3.1 Setup Plane  
Referring to Figure 1, the zero-trust network system essentially comprises a \"Zero-Trust Server\" and multiple zero-trust devices, each equipped with at least one \"Zero-Trust Device Module\", as depicted in Figure 2. During device deployment, users can preconfigure two modules with identical Serial Numbers (SNs) to form a communication pair, ensuring that the used SN does not repeat in other communication pairs (refer to Step 0 in Figure 4). In operations, the SN is employed to generate a non-repeating key for each transmission (refer to Step 3 in Figure 4). Unlike traditional Pre-Shared Key (PSK) methods, the SN serves as an initial PSK but is not transmitted over the public network. In practice, the SN is stored in a Read-Only Memory (ROM), making it accessible only by directly reading the ROM in the device module. Moreover, even if the SN of a specific zero-trust module becomes known to a third party, the one-time use principle prevents its utilization in compromising other device modules.
## 2 Materials and Methods
### Diagnostic Method Flow and Simulation Analysis  
For the short-time signals of rolling bearing, the proposed method of this paper, which combines LMD and the ratio correction method, is suitable for the online fast diagnosis. Firstly, the improved LMD decomposes the raw signal into a set of PFs and the residual signal. The PF, which contains most of the fault information, is selected by the correlation coefficients \(\rho\) between the raw signal and PFs. The correlation coefficient \(\rho\) is a statistical index that describes the degree of correlation between two variables. Its values range from \(-1\) to \(1\). The closer the absolute value of \(\rho\) is to \(1\), the stronger the correlation between two variables is; the closer the absolute value of \(\rho\) is to \(0\), the weaker the correlation between two variables is. The formula for the correlation coefficient \(\rho\) of the discrete signals \(m_{i}\) and \(n_{i}\) is as following  
\[\rho=\frac{\sum\limits_{i=1}^{N}{(m_{i}-\overline{m})(n_{i}-\overline{n})}}{ \sqrt{\sum\limits_{i=1}^{N}{(m_{i}-\overline{m})^{2}\sum\limits_{i=1}^{N}{(n_ {i}-\overline{n})^{2}}}}} \tag{7}\]  
where \(N\) is the number of \(m_{i}\) or \(n_{i}\). Secondly, the Hilbert envelope spectrum of the selected PF is calculated by HT and spectrum analysis. Additionally, the ratio correction method is then used to correct the Hilbert envelope spectrum of the selected PF. Finally, higher precision fault frequencies are extracted and the fault location is accurately determined. In order to highlight the advantages of the proposed method in processing the short-time signals, this paper makes a comparative analysis between the long-time signals and the short-time signals. The specific process of the proposed method is shown in Figure 2.
# Remote Sensing of Snow Cover Using Spaceborne SAR: A Review
## 5 Discussion  
The studies investigated in this review show that SAR-based methods to detect and characterize snow cover have been developing rapidly and profoundly within the last three decades. This development includes the design of new spaceborne SAR sensors, new algorithms to detect snow, higher spatial and temporal resolutions of the derived products, increasing accuracy, and a deeper understanding of the underlying processes. The developments identified will be discussed in more detail in the following sections.
## 2 Method
### The Classical SCR Systems
#### 2.3.2 ASR-Based SCR System  
Figure 1C shows the ASR-based SCR system, which has three major parts: feature extraction, acoustic model, and language model [62]. First, the speech commands of dysarthric patients (\(x_{i}\)) were processed through the Kaldi feature extraction toolkit to obtain the 120-dimensional MFCC features (\(X_{i}^{MFCC}\)). Note that frame size and frame shift were set to 25 and 10 ms, respectively. After that, the \(X_{i}^{MFCC}\) was used as the input feature, and the related target, 33 phones, shown in Appendix B (Table A2), as an output to train this acoustic model, was used with the TDNN model. TDNN is a feed-forward architecture that can deal with speech signal context information through a designed hierarchical structure and layered process method from narrow context to long context information speech signals [52]. For learning wider temporal relationships, TDNN processes the input signals by slicing the output of the hidden activations from the previous layer through deeper layers to obtain important information [51]. The TDNN structure of the ASR system used in this study is shown in Appendix D (Table A4). In this study, we used the Kaldi toolkit [63] to train the ASR-based SCR system based on 41,097 (=3 patients \(\times\) 19 commands \(\times\) 7 times \(\times\) 103 data augmentation) commands. The trained acoustic model will convert the \(X_{i}^{MFCC}\) to \(X_{i}^{PPG}\); meanwhile, the \(X_{i}^{PPG}\) will be used to train the language model in the ASR system. The language model is an important element of ASR, giving the probability of the next words through speech command data training. Traditionally, the language model of ASR uses an N-gram counting method to achieve its language model [64; 65]. Recently, RNN has been applied to replace the N-gram structure [51] in complex tasks, such as multimanguage transfer. However, considering that RNN requires a long training time for similar accuracy performance, the HMM structure using the N-gram method was applied in this study as a language model. Next, the (\(X_{i}^{PPG}\)) features were used to train the language model. The detailed training approach for the ASR system refers to the Peddinti study [52]. Finally, the trained ASR-based SCR system could be used to predict the input commands (\(x_{i}^{\prime}\)) of dysarthric patients, in the application phase.
# Research on Kalman Filter Fusion Navigation Algorithm Assisted by CNN-LSTM Neural Network
## 2 Kalman Filtering GNSS/INS Fusion Navigation Basic Principles
### Mathematical Model of GNSS/INS Fusion Navigation  
Constructing the mathematical model of GNSS/INS fusion navigation using the Kalman filter tightly coupled algorithm as an example [24].
## 3 Experimental Outcomes
### Optimized Condition  
#### 3.8.1 Studies on Composites  
Preparation of CV Dye-Adsorbed NIFGS  
At total of 100 g of commercial CV dye was transferred to a 100 L barrel. The dye was dissolved in 25 L of TIE. A total of 5 Kg of commercial NIFGS were transferred and the solution was stirred manually using a plastic rod. The solution was kept for about 24 h with occasional stirring. The dye-adsorbed NIFGS was separated using a cloth and the precipitate was washed thoroughly with distilled water until the filtrate was almost colourless. The blue colour dye-adsorbed NIFGS was air dried. The resultant powder containing lumps were grinded and sieved through 177 \(\upmu\)m mesh and dried in an oven at 60 \({}^{\circ}\)C for 24 h. The powder was cooled in a closed container with an airtight lid. CV dye-adsorbed NIFGS was referred to as dye-modified NIFGS powder (dmNIFGS).  
Preparation of the Composites  
The composites of high-density polyethylene/dye-modified nutraceutical industrial fenugreek seed spent (HDPE/dmNIFGS) were prepared in two stages; dry-blending of HDPE resin with different proportions of dmNIFGS in a tumble mixer and melt compounding of master batches. HDPE granules and/or recycled product and dmNIFGS master batch flakes were compounded using a twin-screw extruder. The specimens were prepared by cutting the extrudate strands into pellets and were tested for physico-mechanical and chemical properties. The results were encouraging. The details will be reported elsewhere.
# Flooding Message Mitigation of Wireless Content Centric Networking for Last-Mile Smart-Grid +
## 1 Introduction
IEEE 802.15.4-2015 and NB-IoT shows 1 km communication range and up to several hundred data rates, but in high-density AMI environment, packet error rate is increased because most of smart meter devices are installed in closed steel box and plastic cages [6]. Thus, the multi-hop routing functionalities have to be considered for wireless LMN environment. In this paper, we propose a Wireless Topology Aware Content Centric Networking (TOP-CCN) for LMN. In our previous work, we focused on the mobile ad-hoc network environments and voice and video traffic delivery. Different from our previous work, we proposed Smart Grid content naming, system model, and content delivery architecture in this paper. Also, this proposed architecture focused on network heterogeneity as IP and CCN mixed environment to guarantee interoperability between Smart Grid standard application protocols such as DLMS/COSEM, and CoAP. Flooding mitigation and reduction in layer 2 communication, TOP-CCN employs Multiple Point Relay (MPR) based packet forwarding, publisher MPR (PMPR) based proactive content discovery, and flooding range control algorithms based on 1-hop and 2-hop neighbor information. In TOP-CCN algorithm, every meter device periodically elects its MPR, and PMPR through 1-hop broadcast content announcement message. Multi-hop content announcement message from the selected PMPR is broadcasted through MPR to reduce the number of flooding messages of the LMN. Based on a simulation study using ns-3 [5], we show the improved performances of our proposal in terms of communication reliability, network overhead, content downloading time compared to related work.
# Optimization of Redundant Degrees of Freedom in Robotic Flat-End Milling Based on Dynamic Response
## 4 Conclusions  
In contrast to CNC machines, industrial robots have inherent challenges in stiffness and positioning accuracy, leading to difficulties in achieving desired dimensional tolerances and surface quality during robotic milling. Previous research has predominantly focused on addressing these challenges by separately optimizing the redundant angles and predicting positioning errors. To address these issues, this article focuses on developing a dynamic response index model to optimize the redundant degrees of freedom, taking into consideration the impact of both tool vibrations and robot postures on the quality of machined surfaces.  
The proposed model incorporates the posture-dependent dynamic characteristics of the robotic milling system. By utilizing the posture-independent modal parameters and calibrated cutting forces, a dynamic model for robotic milling is established and solved through the semi-discretization method. Through the extraction of the vibration signal peaks, the dynamic response index is established to estimate the surface machining quality.  
The combination of simulation and experimental studies confirming the effectiveness of the proposed optimization method indicates a promising solution for optimizing stability and machining quality, with potential benefits for various practical applications.  
J.L.: conceptualization, methodology, investigation, writing--original draft preparation. Y.Z. ([PERSON]): validation, visualization, writing--review and editing. Y.N.: validation, visualization, writing--review and editing. J.C.: validation, visualization, writing--review and editing. L.Z.: supervision, conceptualization, resources, writing--review and editing. Y.Z. ([PERSON]): supervision, conceptualization, resources, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the China Postdoctoral Science Foundation, Grant No. 2023M742265, and the National Key Research and Development Program of China for Robotics Serialized Harmonic Reducer Fatigue Performance Analysis and Prediction and Life Enhancement Technology Research, Grant No. 2017 YFB1300603.  
All the data generated or analyzed during this study are included in this paper or are available from the corresponding authors on reasonable request. Some data are not publicly available due to intellectual property protection.  
The authors declare no conflicts of interest.
# Ultrathin and Electrically Tunable Metamaterial with Nearly Perfect Absorption in Mid-Infrared
## 3 Results and Discussion
Dirac point is achieved at around the bias of 17 V. The reflectance spectra of the absorbers with different cross-shaped metamaterials. **(c)** Modulation of reflectance achieved by back-gating of graphene with cross-shaped metamaterial of L = 1.25 \(\upmu\)m and W = 0.3 \(\upmu\)m. **(d)** The change in reflectance and frequencies at different back-gate bias. are inverted into electron type, and that the carrier density shows a non-monotonous dependency upon \(\mathrm{V_{G}}\). In the region of \(\mathrm{V_{G}}<17\) V, \(\mathrm{Q_{r}}\) appears to be almost independent of back-gating, while \(\mathrm{Q_{a}}\) can be considerably modulated by back-gate biasing. The \(\mathrm{Q_{a}}\)/\(\mathrm{Q_{r}}\) value extracted from experimental data is also displayed in Figure 2d, and a similar agreement can be clearly identified. 2D images in the inset of Figure 2d show the modular z-component of the electric field (\(\mathrm{|E_{Z}|}\)) of a unit cell at \(\mathrm{V_{G}}=-8\) V and \(\mathrm{V_{G}}=26\) V. As \(\mathrm{V_{G}}\) decreases, the difference between \(\mathrm{Q_{r}}\) and \(\mathrm{Q_{a}}\) also decreases. Accordingly, the electrical field around the cross increases, which leads to a condition closer to perfect absorption (\(\mathrm{Q_{a}}\)/\(\mathrm{Q_{r}}=1\)). In our present work, although the \(\mathrm{Q_{a}}\)/\(\mathrm{Q_{r}}\) change covered only a limited interval along the dashed line and perfect absorption (\(\mathrm{Q_{a}}\)/\(\mathrm{Q_{r}}=1\)) was not yet reached, it is evident that electrical tuning of graphene Fermi level by back-gating is effective for modulating the optical property of the hybrid metamaterial structure. In addition, it is expected that electrical tuning at a different geometrical parameter W (and therefore different \(\mathrm{Q_{r}}\)) will lead to a different tunable range, and the same electrical gating at a larger W will lead to possibly perfect absorption, as indicated by the phase diagram in Figure 2a.
# A Hybrid Bald Eagle Search Algorithm for Time Difference of Arrival Localization
## 4 Experimental Design and Analysis
### Performance Evaluation
#### 4.2.5 Comparison of 3D Scene Positioning  
The above experiments are the application of the swarm intelligence algorithm in the two-dimensional scene, and three-dimensional space positioning is also one of the important applications of the sensor network. Therefore, TDOA localization based on HBES algorithm is applied to 3D scene. The experimental scene of this experiment is set in a three-dimensional space of 50 m \(\times\) 50 m \(\times\) 50 m. When the quantity of base stations was 5, 6, 7, and 8, the algorithm was independently run 50 times, and the average value and standard deviation of positioning errors of each algorithm were recorded.  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline
**Location Accuracy** & \(N\) & **PSO** & **GWO** & **COOT** & **CPSO** & **SSA** & **BES** & **HBES** & **CRLB** \\ \hline \multirow{8}{*}{RMSE/m} & 4 & 3.9277 & 3.7517 & 4.0423 & 3.8522 & 3.8588 & 4.0328 & 3.9384 & 1.0084 \\  & 5 & 2.0468 & 1.9646 & 1.9649 & 1.8189 & 2.1965 & 1.9989 & 1.9539 & 0.9451 \\ \cline{1-1}  & 6 & 1.7577 & 1.5178 & 1.7455 & 1.7175 & 1.8377 & 1.5985 & 1.4638 & 0.8799 \\ \cline{1-1}  & 7 & 1.0027 & 1.0496 & 1.2084 & 1.1186 & 1.1047 & 0.9055 & 0.8767 & 0.8187 \\ \cline{1-1}  & 8 & 1.0532 & 1.0297 & 0.9966 & 1.0111 & 1.0742 & 0.8515 & 0.8396 & 0.7149 \\ \hline \end{tabular}
\end{table}
Table 5: Localization accuracy comparison of a different number of base stations.  
The positioning data and effects of each algorithm in 3D scenes are shown in Figures 8 and 9. Figure 8 imagines the unknown and anchor nodes as points in 3D space, and the estimated locations are the locations found by the TDOA localization method based on the swarm intelligence algorithm. The closer the points of the triangle and the points of the square in the figure indicate, the better the accuracy of the localization. Figure 9 shows the histogram of the average error of the experimental simulation, which shows that the average error of all seven algorithms decreases as the number of base stations increases in the 3D scenario. The average errors of CPSO, BES, and HBES remained consistently low as the number of base stations changed. The HBES improved over the BES algorithm by 1.4%, 4.6%, 5.4%, and 7.1%, respectively, and over CPSO by 1.1%, 3.6%, 1.4%, and 11.9%, respectively, as the number of base stations increased. The standard deviation reflects the stability of the algorithm in solving the positioning coordinates. The HBES algorithm is less stable when the number of base stations is small, and the standard deviation becomes smaller as the number of base stations increases, remaining at the top of the seven algorithms while the quantity of base stations is 8. It can be seen that the TDOA positioning algorithm based on the HBES algorithm for 3D scenes has good positioning accuracy and stability compared with other algorithms, and the method is simple in model and is easy to implement.  
Figure 6: Relationship between RMSE and population size.  
Figure 7: Relationship between RMSE and iteration times.
###### Abstract  
The surgical treatment of isolated medial orbital wall fractures is still a much-debated topic in the literature due to the choice of many surgical accesses. The main options are represented by transcutaneous versus endonasal endoscopic approaches. Our study aims to clarify the role of ethmoidectomy in the pure endoscopic endosomal reduction of medial orbital wall fractures, evaluating the immediate postoperative outcome and its long-term stability. A total of 31 patients affected by isolated medial orbital wall fracture, treated only by endoscopic approach, were included in the study and divided in two groups: (A) 14 patients treated by endoscopic reduction and anterior ethmoidectomy; (B) 17 patients treated by endoscopic reduction and anteroposterior ethmoidectomy. Perioperative and 6-month postoperative follow-up CT scans were performed. With the use of 3D medical software, we evaluated the comparison between the treated orbit and the mirrored contralateral orbit in the two groups, in order to observe the reduction of the fracture. Furthermore, to check the stability of reduction and to evaluate any medial orbital wall changes, we provided a comparison between the 3D CT scan orbital images of immediate postoperative CT and 6-month follow-up. Data obtained showed that the intraoperative surgical reduction was successful in all 31 cases, but it was better in Group B. Stability of the reduction at 6 months was observed in both groups without significant discrepancies. In our opinion, the endonasal endoscopic approach with ethmoidectomy represents a valid and useful technique by which to treat medial orbital wall fractures. The anatomical detail of the buttressing structures of the medial orbital wall, as the second portion of the middle turbinate, grants long-term stability of the surgical outcome.  
medial orbital wall fracture; traumatology; endoscopic approach; ethmoidectomy; endoscopic fracture reduction; CAD/CAM maxillofacial surgery +
Footnote †: journal: Applied Sciences