esa-sceva/satcom-chunk-collection huggingface dataset:
# Compact Photonic-Crystals Based Isolator Using Ni-Zn Gyromagnetic Ferrite Posts
## 2 Materials and Methods  
### Design of PCs Isolator  
As shown in Figure 1a, the designed PCs isolator includes two SLPCs (white), four ferrite posts (blue) and a built-in fan-shaped coupler (red). The top view of the isolator is described in Figure 1b. In Figure 1c, the SLPCs are formed by Al\({}_{2}\)O\({}_{3}\) ceramic rods arrays (white), while the radius and the relative dielectric constant of the rods are \(r_{0}\) and 9.2. The lattice constant of SLPCs is represented as \(A\). The distance between the upper and lower arrays is marked by the width \(w\). A list of rods at the central of the lower array is removed to form a T-shaped PCW, whose two collinear arms are expressed as W1 and W2.  
The third arm of the PCW is closed, in which a fan-shaped PC array structure is introduced, which is also formed by Al\({}_{2}\)O\({}_{3}\) ceramic rods with radius \(r_{2}\). The area of the fan-shaped PCs is determined by the angle \(\theta\) and the distance \(d\) of the two rods, as shown in Figure 1d. The fan-shaped coupler is exactly located in the middle of the lower arm of the T-shaped PCW. The total length of the fan-shaped coupler is 6 mm with \(d=0.5A\), where \(A\) is assumed 4 mm long. At the center of the PCW, four ferrite posts (blue) with the radius \(r_{1}\) and relative dielectric constant 13.5 are introduced, which are marked F1, F2, F3 and F4.  
In our scheme above, two standard WR34 flange interfaces are designed according to the two arms of the PCW, which are the input and output ports of the isolator. The size of the flange interfaces is \(w\times h\), where \(w\) is 8.64 mm and \(h\) is 4.32 mm. The structure has a compact size about of \(46.6\times 41.6\times 4.32\) mm\({}^{3}\) owed to the ingenious design of the built-in fan-shaped coupler. There is no need to use the WR34 matched load alone, which has a comparable size. In our current design analysis, we leave the fan-shape area as it is to obtain the s-parameters and test its bandwidth. For practical fabricated isolators, the bottom of the fan-shape area can be opened with a third connector window, to which a 50 Ohm load can be conveniently attached. In this aspect, our design is no different from existing isolator designs. In addition, some literature has employed wedge type styrofoam to absorb energy inside the device [24].
## 4 Results and Discussion
### Data Preprocessing Results
#### 4.1.2 Spectral Preprocessing Results  
After rejecting the outlier samples, the spectra are subjected to preprocessing operations. In general, the processing sequence begins with a derivative for baseline correction, followed by smoothing and denoising, and then scattering correction and normalization. This study establishes a full-wavelength PLSR model, where \(n\) components = 3 for PLSR. The commonly used sample selection methods include random selection, Kennard-Stone (K-S), and Sample set Partitioning Based on Joint X-Y Distances (SPXY). The SPXY is generally superior to K-S and random selection [31]. Therefore, the SPXY sample selection method is chosen to divide the training set and test set by 4:1. The remaining one is used to validate the model. In this study, a 5-fold cross-validation is employed, and the training set and validation set are divided in accordance with the 4K principle of the standard GB/T 29858-2013 [22], which states that the number of samples in the validation set must be at least four times the number of principal components. The window length of SG is seven, and the order of polynomial fitting is three. The Daubechies wavelet is chosen as the wavelet transform.  
Spectrum of three coniferous woods before and after preprocessing are shown in Figure 9. Using larch as an example, Table 4 compares the prediction results of a number of methods.  
Figure 8: Distribution of reference values of three coniferous wood samples after the rejection of outlier sample.  
Figure 9: WT + MSC for spectral preprocessing of three coniferous wood: (**a**) Raw spectrum for larch; (**b**) Raw spectrum for hemlock; (**c**) Raw spectrum for mongolica; (**d**) Preprocessed spectrum for larch; (**e**) Preprocessed spectrum for hemlock; (**f**) Preprocessed spectrum for mongolica.  
As shown in Table 4, the effect of direct modeling of the original spectrum is poor, and the correlation coefficient on the test set is 0.5157. Using smooth denoising methods (e.g., WT, SG, etc.) alone can improve the modeling effect to a certain extent. The smooth denoising method combined with scattering correction can eliminate the spectral background and noise to the greatest extent. For example, the WT + MSC combination method can obtain the most accurate prediction results, and the correlation coefficient on the test set is 0.7453.  
However, in this experiment, the derivative methods play a negative correction role and reduce the prediction result. At the same time, the normalization treatment is not helpful to improve the accuracy of the model. This shows that there is a litter baseline drift problem in this experiment. MSC and other scattering correction methods are suitable for diffuse reflection spectrum and can eliminate light scattering caused by uneven sample distribution [32]. WT can eliminate the spectral background and improve the stability of the model [33].
# Design Trends and Comparative Analysis of Lightweight Block Ciphers for IoTs
## 3 Classification of Lightweight Block Ciphers
These operations are inherently fast, data-independent, and highly parallelizable, making ARX ciphers attractive for software implementations on constrained processors. The absence of S-boxes eliminates the need for look-up tables, reducing memory usage and susceptibility to cache-based side-channel attacks. However, ARX ciphers typically require more rounds to achieve the same level of diffusion as SPN or Feistel-based designs. Well-known ARX-based LWBCs optimize their round constants, mixing layers, and key schedules to mitigate this and provide resistance against differential and rotational cryptanalysis [9]. Figure 3: Classification of lightweight cryptography. Finally, **hybrid structures** blend elements from multiple architectures, aiming to balance the strengths of each. Examples include FeW and SFN, which combine features of SPN, ARX, and Feistel networks. These designs often yield more adaptable and well-rounded performance profiles but introduce design and verification complexity. Their multi-layered nature can complicate implementation and make formal security analysis more challenging, especially in real-time or reactive systems [10]. A thorough understanding of these structural distinctions is essential for selecting the most suitable cipher for specific IoTs application requirements. Table 3 below categorizes all 58 surveyed lightweight block ciphers based on their structural design, providing a clear view of the algorithms belonging to each type. The number of ciphers in each category is as follows: SPN (19), FN (14), GFN (9), ARX (5), and hybrid (11). The 58 lightweight block ciphers selected in this survey were chosen based on their publication between 2018 and 2025, ensuring a focus on recent advancements in the field. These ciphers represent a diverse range of structural designs, including SPN, Feistel, generalized Feistel, ARX, and hybrid models, which allows for a comprehensive structural and performance-based comparison. Selection criteria also included the availability of implementation metrics such as gate equivalents, key/block sizes, and round counts, as well as documented limitations or attack surfaces in the literature. By focusing on both widely cited and novel proposals, this survey aims to provide a balanced evaluation of contemporary trends and challenges in lightweight cryptographic design for IoTs and constrained devices. The characteristics of the selected algorithms were identified according to the article selection criteria outlined in Table 1, which align with the security evaluation benchmarks summarized in.
## 5 Conclusions and Future Works  
Sometimes it may be necessary to reconstruct the normal anatomical and functional structures of the mandible as a consequence of trauma or oncological resection. In most surgical procedures, the reconstruction is performed using a free fibula flap. The bone segments must be taken with extreme accuracy and, for this reason, it is often performed using a cutting guide which allows the surgeon to easily identify the cutting point and angle. This paper focused on the development of a Mixed Reality application for HoloLens2 that helps the positioning of surgical guide on the fibula during surgery. The application was tested to estimate the accuracy of the system, evaluating the error on positioning in terms of both distance and rotation. Results show greater accuracy and stable performance in positioning the guide, with a distance error of 6 mm and a rotation error of 5.23 degrees. Furthermore, from the study conducted to assess the usability of the system, the application was judged by users to be innovative, helpful, and satisfying, despite they found some difficulties to learn to use it. From this preliminary study, the application showed good results and potential. The next steps include clinical trial and surgery validation to validate the application from a medical perspective.  
Furthermore, the main problem encountered during this study was related to the recognition of the fibula by Vuforia. This software kit is sensitive to variations in light and/or color of the object, which does not guarantee optimal performance, especially in terms of time. For this reason, future work will include the use of different technologies for object tracking and recognition to improve the performance and the efficiency of the system, and also during real surgery procedures where different obstacles, such as blood or tissues, may influence fibula visibility.  
Conceptualization, L.U. and P.P.; Methodology, L.U. and P.P.; Software, C.P.; Supervision, P.P. and E.V.; Validation, L.U.; Writing--original draft, C.P. and P.P. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 5 New Trends and Future Research Directions in Enterprise Networking
### Network Automation  
Since the emergence of SDN and NFV, the traditional static network has witnessed a significant transformation, opening the door for new network technologies. Another remarkable advancement is the automation and effective management of complicated networks through software for achieving optimal performance [83; 98]. Large enterprises are using automated networks more frequently in an attempt to increase productivity and reduce operational expenses. Manual network management is becoming even less effective due to increasing global digitization, with challenges ranging from configuration or human error, to variations and IT outages. Thus, organizations will experience overall network efficiency with the use of AI and machine learning for network operations, providing the possibility of central or remote management [96; 97].  
Due to the manual nature of most network management tasks, performing regular maintenance on them has led to time consumption and wastage of resources. Network automation readily comes to the rescue, as these operations can be carried out more regularly and efficiently, thereby reducing the likelihood of network failure and downtime. Additionally, network automation provides easier ways to maintain network operations by applying configurations (in Figure 8) consistently with less effort across the infrastructure. However, with more connected devices and adoption of software-enabled management systems, the network is continuously exposed to a range of automation security threats. They include phishing attacks, insider threats, ransomware, and malware, among others. By identifying these vulnerabilities, more sophisticated approaches with threat hunting capabilities can be applied to complement other security interventions [99].
## 3 Experimental Outcomes
### The Influence of Variables on CV Adsorption on NIFGS  
#### 3.2.1 Solution pH  
The adsorption capacity of NIFGS depends on solution pH. The pH plays dual roles: first, to instigate surface characteristics of adsorbent and second, the chemical nature of solution [35]. The pH as a parameter, and assumes importance to substantiate the efficiency of the adsorbent under study. The knowledge of this parameter is significant when the process is scaled to commercial levels [36]. The NIFGS exhibits good capacity of adsorption with increased pH. This characteristic is dedicated to the repulsion between cations and H\({}^{+}\) ions with lower pH. Conversely, at higher pH the negatively charged OH\({}^{+}\) attracts the cation of the dye and the negatively charged species (Figure 2a).
## 3 Data and Methods
### Vegetation Indices  
Three satellite-based vegetation indices (VIs) were used to derive post-fire burn severity maps and pre-fire stand density and woody biomass maps for the study area at 3-m (Planet) and 10-m (Sentinel-2A) spatial resolutions. The three VIs are commonly used as proxies of green biomass and vegetation cover are the following:
## 4 Experiments
### Results and Discussion  
This subsection presents performance of the proposed method against baseline methods. The results are shown in Table 3, with the best overall performing method highlighted in bold. When the results given in Table 3 are examined, it is clearly shown that:The proposed SkipGT model: Achieves superior performance on all metrics compared to the baseline methods, thereby substantiating the efficacy of the proposed approach. Traditional baselines: Traditional methods generally exhibit the weakest performance among all approaches evaluated. Item-KNN, which neglects the potential transformations between items, demonstrates the lowest average performance. Similarly, FPMC's limitation to modeling only first-order item relationships hinders its effectiveness compared to neural network-based techniques. Overall, traditional methods suffer from a lack of expressiveness in capturing complex item relationships, ultimately leading to inferior performance compared to neural network methods. Consistent with prior findings [14, 16], FPMC exhibits high memory requirements for processing large-scale datasets. This memory limitation precluded the evaluation of FPMC's performance on the Yoochose 1/4 dataset. RNN-based baselines: Pioneering the application of deep neural networks (DNNs) to session-based recommendation, GRU4 Rec exhibits the lowest performance among DNN-based approaches. This limitation stems from its recurrent neural network (RNN) architecture, which can only capture one-directional transformations between adjacent items within a session. Overall, neural network-based methods like GRU4 Rec, NARM, and STAMP outperform traditional methods. Specifically, the effectiveness of session-based recommendation is attributed to DNN techniques, which include the recurrent structure of GRUs, attention mechanisms employed by NARM, and the prioritization of short-term memory in STAMP. Notably, STAMP outperforms both GRU4 Rec and NARM on the two datasets evaluated. By incorporating both global and local memory to represent the current prediction interest, STAMP can achieve a superior recommendation effect. SAN-based baseline: SASRec surpasses previous methods by effectively modeling users' sequential features with a self-attention network (SAN). The SAN offers a more robust fitting capability compared to recurrent neural networks (RNNs). However, SASRec is limited to users exhibiting sequential interest patterns and may struggle with non-sequential user behavior. The SR-IEM model, equipped with an improved self-attention module, outperforms the SR-GNN model. Notably, both SR-GNN and GC-SAN achieve
## 2 System Design and Method  
### Sensor Manufacturing  
The sensor fabrication and design processes followed the route proposed in our previous study [32]. Following the sensor characterization results based on its chemical, physical, mechanical, and electrical properties, it was realized that the sensor model is strongly dependent on the material preparation and fabrication process conditions. However, to turn the nanoparticles from highly resistive to highly conductive, we used only carbon black instead of a hybrid synergetic network. Herein, the microporous sponge skeleton was fabricated by immersing a commercially available sugar cube (\(15\times 15\times 10\)) in PDMS solution prepared by mixing Ecoflex prepolymer with a base curing agent in a weight ratio of 10:1. Then, the mixture was stored on a heating platform for 2 h at 80 \({}^{\circ}\)C to solidify the prepolymer solution around and inside the cube's structure. Next, the solid cubes were cut into slices with a thickness of 400 \(\upmu\)m and further soaked in a glass container full of hot water. Subsequently, we put the solution in an oven at 60 \({}^{\circ}\)C for 1 h to dissolve the sugar particles from the inner porous sponge. The flexible skeleton was prepared in an inner 3D porous sponge and treated with plasma. At the same time, the conductive CB-alcohol solution was prepared by homogeneously dispersing CB powder in alcohol solvent with a mass ratio 1:9 g/L\({}^{-1}\). The conductive solution was dispersed in the PDMS sponge by ultrasonication (in 45 min at the rate 30%) to firmly obtain conductive fillers on the inner holes of the sponge's walls. Finally, the conductive sponge was dried in the oven to evaporate the alcohol and extract the resulting tactile sensor.
## 2 Background
### Health Level Seven (HL7)  
Standards are commonly needed, and HL7 is a well-known organization that creates standards. The exchange of clinical data is only possible between EHR systems if the systems are built on common standards during development; an example is the identification of the mandatory fields of patient information or tests. The global use of HL7 in clinical environments has streamlined the healthcare practice. According to its official website, HL7 has more than 1600 members in more than 50 countries, including corporate members, stakeholders, medicine companies, drug vendors, and suppliers  
Health care is also a major initiative of some of the biggest global industries. In recent years, the health care industry has improved significantly, further advanced by inventions in the IoT. However, the communication of IoT and medical data is contentious issue. Although electronic health record (EHR) systems are able to manage the global needs of health-related industries, the security of medical data is a matter of concern. A lightweight and efficient mechanism is needed in the EHR system for the secure transfer of medical data on the basis of standards. This study follows the standards of HL7, one of the leading organizations in the creation of standards in the medical field, to create a robust, expandable, and reliable system for medical data communication.
# Reflective Inverse Diffusion
## 4 Results  
### Simulation Validation  
The diffusely-reflecting sample was replaced by a mirror to validate the propagation-based simulation. In the simulation, the mirror is considered a perfect reflector, which eliminates the \(e^{j(\alpha,y)}\) term from Equation (15). Qualitatively, with a mirror positioned at the focus of the positive lens, the creation of a focused spot on the CCD simply requires shifting the focus of the positive lens the distance from the mirror to the CCD. This is accomplished by applying a negative lens phase screen to the SLM. The phase screen for a lens is given by:  
\[t_{A}(x,y)=\exp\left[-j\frac{k}{2f}(x^{2}+y^{2})\right]. \tag{16}\]  
Using geometric optics, the focus of the negative lens is given by,  
\[f_{\text{SLM}}=\frac{1}{\frac{1}{R_{1}}-\frac{1}{R_{1}+R_{2}}}-R_{0}, \tag{17}\]  
where \(R_{0}\) is the distance from the SLM to the positive lens, \(R_{1}\) is the distance from the lens to the mirror and also equal to the focal length of the positive lens and \(R_{2}\) is the distance from the mirror to the CCD.  
The spot produced by the mirror test case was captured by the CCD and compared with the spot simulated using Equation (15). Figure 2 shows both the measured and simulated spots and includes the center horizontal cross-sections of each. There was a 20-\(\upmu\)m difference between the FWHM diameter of the measured and simulated spots. The difference is attributed to uncertainties in the experimental distances between the SLM, lens, reflector and CCD that result in a small defocus error in the measured spot.
# A Systematic Review of Virtual Reality Interfaces for Controlling and Interacting with Robots
## 5 Future Directions
### Interaction  
**Identifying best practices in interacting in VR.** As discussed in Section 3.3, there has been some research into looking at the best practices for interacting in VR, however, it is still limited. There is a large body of knowledge in best practices for designing user interfaces, but the focus is on interfaces for 2D devices, such as, computers or mobile devices. There are some for VR as well, but most of these are focused towards gaming. It is important to understand best interaction practices in VR with focus on interacting with robots in order to create understandable and enjoyable VR robot interfaces.  
**Investigation using different VR input devices.** So far there has been limited investigation in using other types of input devices for VR robot interfaces, such as VR gloves or omni-directional treadmills. These devices are used to create a more immersive experience in VR gaming and therefore could be useful as well in VR interfaces.
## 4 Discussion  
From the simulation results above, it is evident that the queue optimization mechanism proposed in this paper can adjust the lengths and numbers of queues used for forwarding time-sensitive traffic, thereby adapting to the actual traffic conditions in the network. Through the application of this mechanism to large-scale LEO satellite communication networks, the packet loss rate and transmission delay for time-sensitive traffic can be significantly reduced. Moreover, this mechanism can also enhance the network's throughput and better cope with microburst phenomena and has lower computational costs. Future research will further explore traffic scheduling and forwarding mechanisms based on synchronous and asynchronous dynamic adjustment, aiming to reduce delay jitter in the transmission of time-sensitive traffic.