esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Reliability of Tactical MANETs by Improving Routing Decisions
###### Abstract  
Mobile ad-hoc networks (MANETs) have been primarily designed to enhance tactical communications in a battlefield. They provide dynamic connectivity without requiring any pre-existing infrastructure. Their multi-hop capabilities can improve radio coverage significantly. The nature of tactical MANET operations requires more specialized routing protocols compared to the ones which are used in commercial MANET. Routing decisions in MANETs are usually conditioned on signal-to-interference-plus-noise ratio (SINR) measurements. In order to improve routing decisions for use in highly dynamic tactical MANETs, this paper proposes to combine two different metrics to achieve reliable multicast in multi-hop ad hoc networks. The resulting protocol combining received signal strength (RSS) with SINR to make routing decisions is referred to as Link Quality Aware Ad-hoc On-Demand Distance Vector (LQA-AODV) routing. The proposed routing protocol can quickly adapt to dynamic changes in network topology and link quality variations often encountered in tactical field operations. Using computer simulations, the performance of proposed protocol is shown to outperform other widely used reactive routing protocols assuming several performance metrics.  
connectivity; link quality; network metric; routing decision; tactical MANET +
Footnote †: journal: Low Power Electronics and Applications
## 4 Exploring Vocabulary-Mismatched Dysarthric Speech Recognition
### Experiment Design  
#### 4.2.1 Model Initialization  
In previous experiments, we initialized the model with the base model (**E1**) and a newly initialized output layer, as the phone vocabulary in our training data was smaller than that of the base model. This approach discarded the mapping from speech features to phone tokens learned during base model training on rich-resource data. While this prevented leveraging the pretrained phone distribution knowledge, it ensured a stricter mismatch between the vocabularies of the training and testing datasets.  
However, given that the base model is publicly available and its phone vocabulary may align well with the test data, discarding this learned knowledge may not be necessary. To address this, we consider two different model initializations in the experiments. In the first one, we continue using the base model and the newly initialized output layer, maintaining strict vocabulary limitations. In the second one, we use model **E3** to simulate a new base model tailored to our phone inventory, referred to as a vocabulary-informed base model. This setup retains the pretrained wide vocabulary knowledge in both the output layer and the overall model.  
For the cross-lingual scenario, instead of using **E3**, we train the vocabulary-informed base models with the C_rich_extgt dataset, which excludes target language data from the  
**C_rich** dataset. This ensures that the vocabulary-informed base models draw knowledge exclusively from non-target languages.
# On the Local and String Stability Analysis of Traffic Collision Risk
## 2 Preliminary on Conventional Traffic Stability Analysis
### Definition of Traffic String Stability  
Unlike traffic local stability, string stability focuses on the evolution of disturbance of a vehicle platoon [31]. The traffic local stability and string stability should be distinguished. Local stability investigates whether the subject vehicle will return to the equilibrium state. In contrast, string stability focuses on the disturbance propagation along the vehicle platoon, i.e., if the disturbance will be amplified or attenuated along the vehicle platoon.  
A sketch of a vehicle platoon is demonstrated in Figure 1. After a small disturbance \(y_{n}(t)\) is introduced to \(x_{n}(t)\), vehicle \(n\) will respond to the disturbance, and the disturbance will propagate to vehicle \(n+1\) as \(y_{n+1}(t)\), where \(y_{n+1}(t)=x_{n+1}(t)-x_{n+1}^{eq}(t)\). If the disturbance decays over the following vehicles and the vehicle platoon recovers to its initial state, eventually, the vehicle platoon is considered traffic-string-stable. The criterion for string stability is formulated as  
\[\frac{||y_{n}(t)||_{p}}{||y_{n+1}(t)||_{p}}>1 \tag{6}\]  
where \(||\cdot||_{p}\) represents the \(p\)-norm (\(p=1,2,\ldots,\infty\)), and \(p=2\) is normally adopted to analyze the string stability [32], which is also known as \(L_{2}\) string stability. With \(p=2\), Equation (6) is reformulated as  
\[\frac{||Y_{n}(s)||_{2}}{||Y_{n+1}(s)||_{2}}>1 \tag{7}\]  
where \(Y_{n}(s)=\mathscr{L}(y_{n}(t))\) and \(Y_{n+1}(s)=\mathscr{L}(y_{n+1}(t))\) denote the Laplace transform of \(y_{n}(t)\) and \(y_{n+1}(t)\), respectively.  
By substituting the linearized car-following model (Equation (2)) into Equation (7), the string stable criterion is derived as follows. For a detailed derivation, refer to [28].  
\[f_{x}^{2}-2f_{\Delta x}-2f_{\Delta x}f_{x}>0 \tag{8}\]
# Pipe Routing with Topology Control for Decentralized and Autonomous UAV Networks +
## 8 Simulation Results and Discussion
### Performance Evaluation  
The performance of a routing scheme depends on the number and location of the targets (sources). In our simulations, we considered a number of different target counts and locations; however, for compactness, we detail the results for three cases of single-target and three-target settings, which show the general range of behaviors. For single-target settings, we examine three different locations, designated \(C_{1}\), \(C_{2}\), and \(C_{3}\), which are shown in Figure 7a-c. The locations represent a range of distances from the BS, from \(C_{1}\) (farthest) to \(C_{3}\) (closest). We see that the performance of the network for other target locations (such as \(C_{2}\)) generally tends to fall within the range of the two extreme settings (\(C_{1}\) and \(C_{3}\)).  
For multi-target settings, we examine three targets in three location scenarios \(C_{4}\), \(C_{5}\), \(C_{6}\), as depicted in Figure 7d-f. Again, these illustrate the range of outcomes, with \(C_{4}\) (all targets far) and \(C_{6}\) (all targets close) the two extremes, and other target locations (such as \(C_{5}\)) falling between the performance of the two extreme settings.
# Holographic Silicon Metasurfaces for Total Angular Momentum Demultiplexing Applications in Telecom
## 1 Introduction
If the transparency of silicon in the telecom infrared suggests the exploitation of this high-refractive index material in order to further reduce the optical thickness, then, on the other hand, the fabrication of 3D surface-relief patterns in silicon is still undoubtedly challenging. An alternative method for phase-fronts manipulation is provided by Pancharatnam-Berry optical elements (PBOE) acting on the geometric phase of light. Unlike refractive and diffractive optics, in PBOEs the phase change is not produced by means of an optical path difference, but is the result of a space-variant modification of the polarization state of light [30]. This is achieved by realizing an artificial material, i.e., a metasurface, which is both inhomogeneous and anisotropic, in order to create an effective anisotropic medium whose extraordinary axis orientation is spatially variant. The phase transferred to the input beam is equal to twice the value of the fast-axis orientation, therefore by properly engineering the anisotropy pattern it is possible to reshape the input phase-front in the desired way. With respect to conventional optics, the approach with metasurfaces can offer greater advantages owing to their digital profile and fixed thickness. In comparison with diffractive optics, metasurfaces show a broader band [31], since the wave-front is tailored by the geometric pattern of the optical element. In addition, since the optical response becomes inherently dependent on the input polarization [32], polarization-division multiplexing (PDM) can be easily implemented without the need of additional optics [33]. In this paper, we present the design and realization of sorting optics for OAM-MDM, in the form of Pancharatnam-Berry optical elements in silicon for the telecom wavelength of 1310 nm. Weconsidered the demultiplexing method based on OAM-mode projection and we computed and realized different metasurfaces in silicon, performing both OAM-MDM and PDM. Despite its lower efficiency with respect to other methods, this technique allows to customize the channel constellation and the sorting OAM range, depending on the desired application. Different OAM sets and far-field channel configurations have been selected and presented, in order to demonstrate the versatility offered by this demultiplexing method. The birefringence of the single PBOE subunit has been achieved artificially by structuring the silicon substrate with a digital subwavelength grating, which is experienced by the impinging wave as a uniaxial crystal whose fast axis is perpendicular to the grating ridges [34]. The resist mask fabricated on the silicon surface with high-resolution electron-beam lithography (EBL) was transferred to the substrate by a finely-tuned inductively coupled plasma--reactive plasma etching (ICP-RIE) process. The optical tests at the wavelength of 1310 nm, in the telecom O-band, confirm the expected capability of the designed optics to detect correctly input beams with different circular-polarization states and orbital angular momentum values.
## 5 Design Method
### Head Thickness  
Head thickness is also a concern in the design of CHBC. Heads with inadequate thickness can yield during loading, which may impair the bearing capacity of CHBC. So, the determination of the critical value of thickness is necessary.  
Providing the rigid connection between head and steel bar, the head can be modelled as a cantilever beam fixed at the steel bar. Uniform compression stress over the head is assumed and the force integrated by all the compression stresses over the head-bearing surface is equal to the design tensile strength of headed bar in this paper. It should be noted that in CHBC, the tension force is not only borne by the head, so it is for conservative and convenient purposes that the force at the head is equal to the design tensile strength of the headed bar. Actually, the force at the head is equal to \(F_{h,u}\) mentioned above. The analytical model of the cantilever beam can be seen in Figure 39. Based on the bending theory of beam, the stress in the head can be determined by Equation (10).  
\[\begin{array}{l}q_{P}=\frac{P}{a_{h}^{2}-A_{s}}a_{h}=\frac{f_{s}A_{a}a_{h}}{ a_{h}^{2}-\frac{u_{d}^{2}}{a_{h}^{2}}}=\frac{f_{s}\pi d^{2}a_{h}}{4a_{h}^{2}-\pi d ^{2}}\\ \sigma_{h}=\frac{M_{P}}{5}=\frac{q_{P}a_{h}^{2}/8}{a_{h}h^{2}/6}=\frac{3}{4} \frac{f_{s}\pi d^{2}a_{h}^{2}}{f_{s}^{2}(4a_{h}^{2}-\pi d^{2})}\\ \tau_{h}=\frac{3}{2}\times\frac{V_{P}}{\mathcal{A}}=\frac{3}{2}\times\frac{q \rho a_{h}/2}{a_{h}^{2}t_{h}}=\frac{3}{4}\frac{f_{s}\pi d^{2}a_{h}}{t_{h}(4a_{ h}^{2}-\pi d^{2})}\end{array} \tag{10}\]  
where \(\sigma_{h}\) and \(\tau_{h}\) are the bending stress and shear stress of head, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & **Specimen ID** & **\(F_{u,exp}\) (kN)** & **\(F_{u,pre}\) (kN)** & **\(F_{u,pre}\)/\(F_{u,exp}\)** \\ \hline
1 & & 53.6 & 52.1 & 0.972 \\
2 & M4-D12-6d & 53.9 & 52.1 & 0.967 \\
3 & & 56.2 & 52.1 & 0.9  
So, the critical value of head thickness for yielding can be determined by Equation (11):  
\[t_{h}=\max\left\{\begin{array}{l}\frac{\sqrt{3}d_{h_{y}}}{2}\sqrt{\frac{f_{y} \pi}{(4{{u_{h}}}^{2}-\pi d^{2})f_{h,y}}}\\ \frac{3\sqrt{3}}{4}\frac{f_{y}\pi d^{2}a_{h}}{(4{{u_{h}}}^{2}-\pi d^{2})f_{h,y} }\end{array}\right. \tag{11}\]  
where \(f_{h,y}\) is the yielding strength of head material.  
Generally, welding or thread connection technique can be used to achieve the rigid connection between the head and steel bar in CHBC. The head thickness should be also in accordance with the requirements of these connection techniques.
## 3 Methodology  
### Overview of Proposed Machine Learning Approach  
This study adopts a long short-term memory (LSTM) neural network, a special class of recurrent neural network (RNN) designed to handle time-series data by capturing both near-term and long-term dependencies. Such adaptability is crucial for modeling dynamic restroom usage, where facility demand can shift throughout the day.  
The methodological workflow encompasses five primary stages:  
1. Data Collection and Preprocessing--Compiling both empirical and synthetic datasets to reflect a range of usage scenarios.
2. Feature Engineering--Identifying and extracting key variables, including occupancy levels, gender distribution, and temporal factors.
3. Model Development and Training--Designing and refining the LSTM architecture using the preprocessed dataset.
4. Hyperparameter Tuning--Systematically optimizing the model configuration to elevate predictive accuracy.
5. Performance Evaluation--Measuring the final model's performance against standard methods like queueing theory.  
The model's input features capture occupancy, time of day, and gender proportions, while its output focuses on estimating the required counts of toilets, urinals, and washbasins for each gender.
# Dynamic Graph Neural Network for Garbage Classification Based on Multimodal Feature Fusion
## 3 Graph Construction and Dynamic Graph Neural Network  
### Dynamic KNN Graph Construction  
Each image not only possesses a 1024-dimensional feature vector \(x_{cnn,i}\) extracted by the CNN but also includes a 512-dimensional color histogram feature \(x_{hist,i}\). These two sets of features are concatenated to form the node feature \(x_{fused,i}\) representation:  
\[x_{fused,i}=[x_{cnn,i},x_{hist,i}]\in\mathbb{R}^{1536} \tag{11}\]  
Figure 5: Original images and their corresponding 3D histograms in LAB color space.  
For a batch of N images, we obtain N node features, denoted as \(\left\{x_{fused,i}\right\}_{i-1}^{N}\). Then, using the Euclidean distance, the distance \(dist(i,j)\) between each node \(i\) and every other node \(j\) (\(i\
eq j\)) is computed as follows:  
\[dist(i,j)=\left\|x_{fused,i}-x_{fused,j}\right\|_{2} \tag{12}\]  
Next, all distance values are sorted, and the top k closest nodes are dynamically selected as neighbors for node \(i\). When the total number of samples is limited, the actual number of neighbors is dynamically adjusted to \(min(k,N-1)\) to ensure the soundness of the graph structure. Finally, a directed edge marked as \((i,j)\) is recorded in the adjacency matrix, and a two-dimensional tensor is constructed to describe the index relationships between the starting and ending nodes of each edge in the graph, thus enabling adaptive graph construction. Assuming that k is set to 3--allowing up to three nodes to be adaptively chosen as neighbors--the detailed process of dynamic KNN graph construction is illustrated in Figure 6.  
As illustrated in the dynamic example in Figure 6, for the red node, within the predefined neighborhood, the distance between the red node and its surrounding nodes is first computed \(dist(i,j)\). Based on these distances, up to three of the closest nodes are selected to form the initial candidate neighborhood set. Within this candidate set, a directed edge is established between the red node and each candidate, with the edge orientation indicating that it points from the red node toward the nearest target node. Moreover, when the red node and a candidate node mutually fall within each other's neighborhoods, it indicates that these two nodes exhibit a high degree of similarity in the feature space, resulting in the formation of a bidirectional connection.
# The Security Perspectives of Vehicular Networks: A Taxonomical Analysis of Attacks and Solutions
## 3 Classification of Attacks in VANETs  
Many informational attacks are launched against the vehicles and smart grids [37]. These networks are less secure because of no or minimum-security standards maintained for them. Various types of attacks are launched on the different layers of the network. These attacks may be categorized based on the security service which targets integrity, confidentiality, or availability [38]. It is necessary to understand the nature of the attack and the attacker with the motive behind the attacks. We have classified the VANET security attacks based on security services, attacker types, VANET layers, and VANET components.
## 4 Result and Discussion
### Dynamic Multi-Stage Path Planning Experiments
#### 4.3.2 Response Time  
Figure 9 shows the average response time of the algorithm in different environments. The response time of ID A* and CBS increases exponentially as the number of AGVs grows. Both PRIMAL2 and PER-D3 QN-EDAgger deliver a fast path planning times. PER-D3 QN-EDAgger maintains near-instantaneous response times (50 ms), making it highly suitable for real-time planning in dynamic environments.
## 6 Conclusions  
In this paper, the error equation of an inertial/GNSS/odometer integrated navigation system was derived, and the error models of the strapdown inertial navigation system and inertial/odometer dead reckoning were established. The federated Kalman filter architecture was established to estimate the system error, and the system error was then corrected using feedback. A data-smoothing algorithm based on forward filtering and reverse smoothing was used to improve detection accuracy. A novel inertial track geometry state detector was developed, and the feasibility and detection accuracy of the integrated navigation scheme were verified through on-track experiments. The main conclusions of this study are as follows:  
1. The information distribution coefficient of the state vector fusion algorithm is determined based on the orthogonality of innovation and the DOP value of the satellite signal, which improves the detection accuracy of the inertial track detector.
2. The data-smoothing algorithm based on forward filtering and reverse smoothing improved the accuracy of integrated navigation and improved the detection accuracy of the inertial trajectory geometry state detector.  
Figure 8: Absolute deviation comparison of three replicates of test data for track geometry parameters of test lines: (**a**) track direction; (**b**) high and low; (**c**) horizontal (ultra-high).  
3. A high-speed railway track geometry parameter tester system was constructed and developed. The measurement accuracy of the track geometry parameters was better than 0.2 mm, and the detection speed was about 3 km/h, which meets the requirements of high accuracy and high efficiency of high-speed railway track geometry detection. In engineering practice, using the inertial track geometric state detection method proposed by this paper can realize the high-accuracy and high-efficiency geometry detection of an operating high-speed railway. This approach can also be extended to the maintenance of railways of varying speeds and long-track fine adjustment measurement of newly-built railways.  
Conceptualization, X.Z. and X.C.; data collection, X.Z. and B.H.; data analysis, X.Z. and X.C.; manuscript writing, X.Z.; manuscript review and editing, B.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 51474217.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
# Compact Sub 6 GHz Slot Multiantenna System for 5G Laptops
###### Abstract  
A sub 6 GHz dual-band closed-slot multiantenna system for 5G laptops is proposed in this paper. It was installed in a clearance space, with dimensions of 217 \(\times\) 3 mm\({}^{2}\) and 1 mm away from the upper edge of the screen ground plane. The dimensions of the clearance space were the same as those of a multisystem consisting of six antennas. The dimensions of the single closed-slot antenna were 32 \(\times\) 3 mm\({}^{2}\) (0.368 \(\lambda\)\(\times\) 0.034 \(\lambda\), where \(\lambda\) equals the free-space wavelength of 3450 MHz. The antenna was coupled to an asymmetric T-shaped feed-in section equipped with a chip capacitor for exciting one-half and full wavelength resonance modes of the closed-slot to encompass sub 6 GHz 3300-3600 MHz and 4800-5000 MHz dual-band operations. The design of the antenna features a long and straight slot to generate the high-order mode of the closed slot in the high-frequency (4800-5000 MHz) band (not the low-frequency (3300-3600 MHz) multiplier band). Its structure is simple, and the width of its slot is only 3 mm. The antennas were arranged to be 5 mm apart in the same direction and in parallel to form a six-antenna system in order to utilize the weak electric fields located at the two closed ends of the closed-slot structure when the closed slot was excited. It showed excellent envelope correlation coefficients (ECCs) and isolation performance without the installation of isolation elements. The measured fractional bandwidth of the antenna was 10.15% and 6.73% at the center frequencies of 3450 MHz and 4900 MHz, respectively. Its measured isolation was always over 10 dB, and the efficiency was between 46% and 76%. The ECCs of the system calculated from the measured complex E-field radiation pattern were all below 0.2, which means that it is ideal for use in laptop devices with a high screen-to-body ratio and a metal back cover.  
laptop antennas; multi-input multi-output (MIMO) antennas; slot antennas; 5G antennas 202314626[[https://doi.org/10.3390/mi14030626](https://doi.org/10.3390/mi14030626)]([https://doi.org/10.3390/mi14030626](https://doi.org/10.3390/mi14030626))
## 3 Background Theory
### Message Queuing Telemetry Transport (MQTT)  
Message Queuing Telemetry Transport (MQTT) is an Internet of Things (IoT) protocol standard issued by OASIS [26]. MQTT is designed by carrying a publish/subscribe protocol that is lightweight and requires small bandwidth [27]. MQTT has been widely implemented in various industrial sectors, such as manufacturing, automotive, sports, energy, military, telecommunications, and health [28].
# Numerical Study of Internal Flow Field in a Disc Stack Centrifuge Based on Mixture-PBM Model
## 3 Results and Discussion
### Coalescence and Breakup of Water Droplets  
For liquid-liquid separation of the disc stack centrifuge, the internal two-phase flow process, the dispersed phase droplets will occur in the coalescence and breakup phenomena. Figure 11 shows that at the inlet of the rising channel (a vertical channel consisting of neutral pores), the small-sized water droplets become fewer, and the large-sized water droplets increase in number compared to the inlet of the bowl component.  
After the oil-water mixture enters the distributor, water droplets with smaller particle sizes coalesce into water droplets with larger particle sizes because most of the water has settled to the lower surface of the disc stack shelf inside the distributor, and the distance between the water droplets decreases. When the distance between droplets is less than 10 nm, the droplets instantly undergo coalescence due to intermolecular force [31].  
Figure 12 shows the particle size distribution of water droplets at different cross-sectional locations. The particle size of the water droplets remains at a maximum in the bowl component heavy liquid region of the disc stack centrifuge. The heavy liquid zone should be in an oil-in-water (O/W) state, but since only the pre-set particle diameters of the dispersed phase can be modelled, it can be assumed that the water droplets in the heavy liquid zone, which maintain the largest particle diameters, are converted from the dispersed phase to the continuous phase.  
As can be seen from Figure 12b, the water droplets begin to coalescence already inside the distributor, sliding along the lower surface of the disc stack shelf and passing through the rising channel into each separation channel, where the coalescence process dominates, but outside the small end of the discs, the breakup process occurs, and the particle size of the droplets decreases.