esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Approach to Identifying Crash Hotspot Intersections (CHIs) Using Spatial Weights Matrices
###### Abstract  
In this paper we develop a new approach to directly detect crash hotspot intersections (CHIs) using two customized spatial weights matrices, which are the inverse network distance-band spatial weights matrix of intersections (INDSWMI) and the k-nearest distance-band spatial weights matrix between crash and intersection (KDSWMCI). This new approach has three major steps. The first step is to build the INDSWMI by forming the road network, extracting the intersections from road junctions, and constructing the INDSWMI with road network constraints. The second step is to build the KDSWMCI by obtaining the adjacency crashes for each intersection. The third step is to perform intersection hotspot analysis (IHA) by using the Getis-Ord Gi* statistic with the INDSWMI and KDSWMCI to identify CHIs and test the Intersection Prediction Accuracy Index (IPAI). This approach is validated by comparison of the IPAI obtained using open street map (OSM) roads and intersection-related crashes (2008-2017) from Spencer city, Iowa, USA. The findings of the comparison show that higher prediction accuracy is achieved by using the proposed approach in identifying CHIs.  
cnnhtspot intersections (CHIs); road network; traffic crash; spatial weights matrix (SWM); Getis-Ord Gi*; hotspot analysis; Intersection Prediction Accuracy Index (IPAI)
# Predictive Optimization of the Heat Demand in Buildings at the City Level
## 4 Results  
The optimization was performed as illustrated in Figure 2. First the physical parameters \(C\) and \(U\) were calculated (Section 3.2.2). Then the model parameters for the indoor temperature and heat demand models (Equations (1) and (2)) were estimated. Next, the indoor temperature and heat demand were forecasted, and finally the predictive optimization was performed. In each optimization cycle, there were 48 real number variables (heat demand for each hour of the 48-h forecast horizon) for each building subject to hard bounding constraint in Equation (5). The hourly indoor temperature was subject to bounding constraint in Equation (6) inflicting penalty (\(T_{Penalty}\)) if breached. In addition, for minimum variance optimization, the total heat demand for the 48 h was subject to inequality constraint in Equation (4) inflicting penalty (\(P_{Penalty}\)) if breached. For peak load cutting the hourly heat demand was subject to the inequality constraint in Equation (8) inflicting a penalty (\(P_{changePenalty}\)) if breached. Also, for the peak load cutting period, the hourly heat demand was subject to the bounding constraint in Equation (9). The indoor temperature model parameters were estimated only once for each building at the beginning of a simulation run. For the 480-h minimum variance optimization (Section 3.3.1), the heat demand model parameters were updated in each iteration as presented in Figure 2.  
Figure 1: The optimization of the heat demand for 480 h by minimizing the variance of the heat demand.  
All the computations were performed in MATLAB(r) environment on a standard PC with Intel(r) Core i7 processor with 3.4 GHz speed and 8 GB of RAM. As the optimization was performed separately for each building, local parallel computing in MATLAB(r) was utilized. Calculation time for one optimization cycle including the estimation of parameters for the heat demand model was approximately four minutes.
# A Multi-Machine and Multi-Modal Drift Detection (M2D2) Framework for Semiconductor Manufacturing
## 5 Illustrative Example
### Phase 6: Drift-Aware Federated Learning  
In scenarios involving multiple machines or multiple plants, FL becomes crucial for safeguarding each machine's sensitive data while enabling shared detection knowledge. However, if the distributions or drift severity among machines differ substantially, a standard federated averaging approach (FedAvg) may allow a single outlier machine to \"drag down\" the global model. To address this issue, this study introduces drift-aware federated learning (drift-aware FL) in Phase 6, adjusting aggregation weights dynamically in each round based on the short- and long-term drift scores obtained in Phases 2 and 3. For instance, if Machine 3 experiences a surge in reconstruction error and distributional divergence from late June to early July 2024--perhaps due to component aging or overload--the system may reduce its parameter weights or invoke additional micro-round local updates, thus preventing undue disruption to the global model. After Machine 3 completes maintenance and logs \"maintenance done\", and its reconstruction error returns to a stable range, its parameters are reintegrated into the global aggregation. As illustrated in the lower portion of Figure 2, this approach ensures that the drift of Machine 3 exerts minimal impact on the detection quality of Machines 1 and 2, ultimately preserving both the stability and strong performance of the global model across multiple machines. Machine 4, with its dual-recipe operations, can sometimes display more frequent distribution shifts. By incorporating drift-aware weighting, M2D2 prevents these recipe-induced changes from disproportionately influencing the federated model, thus preserving its performance for other machines.
## 2 Antenna Array and Design Method
### Operation Mechanism and Leaky-Wave Generation
The dispersion diagram clearly showed that the stop band corresponding to the n = \(-2\) space harmonic had been successfully suppressed as the propagation constant increased almost linearly and smoothly with the frequency around 190 GHz and the attenuation constant remained relatively stable. As the frequency approached 205 GHz, the reflection coefficient and attenuation constant increased again due to the stop band relating to \(\beta_{-1}p=2\pi\). The other higher spatial harmonics (i.e., n = \(-3\), n = \(-4\) and so on) did not enter the radiation region in the operating frequency range. These bounded modes (slow waves) will attenuate exponentially in the transverse direction and can only propagate along the structure. Consequently, the given frequency range is dominated by a single leaky mode (n = \(-1\)) scanning in the backward and the forward directions. For the broadside scanning at 172 GHz where \(\beta_{-1}\) = 0, the phase difference between unit-cells (\(\Delta\varphi\)) became 2\(\pi\). This implies that the reflected waves from each circular disc become equally in phase at the broadside direction and hence, the maximum power is reflected towards the source. Therefore, the wave is rapidly attenuated as it propagated along the structure and the attenuation constant \(\alpha\) becomes high, leading to the open-stop-band (OSB) phenomena [47]. By printing two circular metal discs a quarter wavelength apart, the phase difference between the reflected waves from the first and second discs becomes 180\({}^{\circ}\) and they nearly cancel out each other. This means that the \(\alpha\) becomes relatively small and the OSB is suppressed. The main beam direction of LWA is dependent on the rate of the propagation constant in guiding structure and the free space wavenumber \(k_{0}\) calculated by [48]  
\[\sin(\theta_{m})=\frac{\beta_{m}}{k_{0}} \tag{4}\]  
Figure 2: Dispersion curve of the optimized unit-cell with dimensions indicated in Figure 1.  
where \(\theta_{m}\) is the elevation angle measured from the z axis. The proposed antenna operates at the n = \(-1\) order mode nearby the broadside direction. The propagation constant \(\beta_{-1}\) of the n = \(-1\) space harmonic can be obtained by the propagation constant of the fundamental mode \(\beta_{0}\) and the periodicity \(p\) of the unit-cells using the Equation (2). Consequently, the main beam direction of the proposed antenna is defined by the phase constant \(\beta_{0}\) of the fundamental harmonics and the periodicity \(p\) as. \[\theta_{-1}=\arcsin(\frac{\beta_{0}-\frac{2\pi}{p}}{k_{0}}) \tag{5}\]  
To have broad-side radiation at a desired frequency of 172 GHz, the radiation elements should be fed equally in phase, namely, with an inter-element spacing of one guided wavelength (\(\lambda\)g) at 172 GHz in DIL. The spacing of \(p\) = 0.8 mm was adjusted by field simulation from an initial value of 1 mm relating to the guided wavelength of the loaded DIL.
## 2 Materials and Methods  
### Synthesis of Melanin-BSA Conjugates and PheoBSA-FITC Conjugates  
Dopamine hydrochloride, Bovine Serum Albumin (BSA), Potassium phosphate monobasic KH\({}_{2}\)PO4, Dibasic potassium phosphate K\({}_{2}\)HPO4, sodium hydroxide NaOH, Fluorescein isothiocyanate (FITC) were purchased by Sigma-Aldrich (Steinheim, Germany). The melanin-BSA conjugates were prepared by autoxidation of dopamine in the presence of the protein (Figure 1). To obtain water-soluble eumelanin-BSA conjugates (EuBSA), 450 mg of DA and BSA in the amounts necessary to give stoichiometric ratios of 1:2 and 2:1 (_w/w_) were allowed to react in air in 200 mL of 50 mM phosphate buffer, pH 7.4, at 37 \({}^{\circ}\)C while protected from light. The conjugates between pheomelanin and BSA (PheoBSA) were prepared similarly, through the autoxidation of 450 mg of DA in the presence of cysteine (DA:Cys molar ratio 5:1) and BSA in the same proportions as above, DA/BSA = 1:2 and 2:1 (_w/w_)and in the proportions 1:1 (_w/w_), in 200 mL of 50 mM phosphate buffer, pH 7.4. These solutions were allowed to react in the air at 37 \({}^{\circ}\)C while protected from light. The obtained conjugates were dialyzed for two days using tubing with a 12 kDa cutoff against Milli-Q water that was replaced three times to eliminate unreacted DA and small DAQ (dopaminoquinone) oligomers that were not bound to BSA. The dialyzed conjugates were then lyophilized to obtain brown-black solids. A summary of the various melanin-BSA conjugates that were prepared is given in Table S1 (Supplementary Material). They are named A, B, C, D, E according to the initial ratios of the reagents employed during their preparation and to the reaction time. The lyophilized samples were dissolved in Milli-Q water to make a stock solution of 10 mg/mL. To conjugate FITC to Pheo-BSA-D (PheoBSA-FITC), 8 mg of PheoBSA D was dissolved in a solution of sodium carbonate 0.1 M (pH = 9.4) and then fluorescein isothiamate in DMSO (1 umol, 1 mg/mL, 0.4 mL) was added. The reaction was stirred at room temperature in the dark for 8 h. The solution was dialyzed against water to eliminate unreacted FITC and then lyophilized and stored at 4 \({}^{\circ}\)C. The fluorescein bounded to Pheo-BSA D was quantified spectrophotometrically by measuring the absorbance intensity of the PheoBSA-FITC dissolved in water (0.2 mg/mL) at 495 nm against Pheo-BSA D as blank. For the calibration curve, standard solutions of FITC (1 um to 10 um) were prepared in water starting from 250 mg/L of fresh solution of FITC in sodium carbonate 0,1 M (Figure S2). To confirm that the labeling procedure did not affect the fluorescent properties of FITC, excitation and emission spectra of PheoBSA-FITC solution were measured by a high-resolution spectrofluorometer FluoroMax-40 (Horiba Jobin Yvon, New York, NY, USA). The fluorescence emission spectrum was obtained by setting 485 nm as the excitation wavelength and acquiring the fluorescent signal in the range between 495 and 600 nm. The fluorescent excitation spectrum was obtained exciting the sample in the range between 350 and 510 nm and setting 520 nm as emission wavelength (Figure S3).
## 10 Discussion
### Comparison with the Existing Studies  
Our work extends the current state of research on trie structures and blockchain data management in several key directions. Table 4 presents a comparison of our approach with relevant prior work.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Study** & **Main Focus** & **Key Contribution** & **Optimization Approach** & **Performance Results** & **Limitations** \\ \hline [PERSON] et al. [5] & Binary Patricia tries & Variance analysis of external path length & Mathematical analysis of path distribution & Variance \(\approx\) 0.37n + nP(log\({}_{2}\)n) & Limited to binary tries \\ [PERSON] et al. [9] & Height analysis of tries & Smoothed analysis of tries & Perturbation model & Height = \(\Theta\)(log n) under perturbation & Theoretical bounds only \\ Adaptive Merkle Trees [8] & Path length optimization & Dynamic tree restructuring & Statistical encoding principles & Up to 70\% path length reduction for hot addresses & Initial restructuring overhead \\ Current work & Probabilistic framework & Path length distribution model & Entropy-based optimization & 30–35\% average improvement & Requires periodic updates \\ [PERSON] et al. [15] & Storage efficiency & SolsDB optimization & Storage engine modifications & 30\% read performance improvement & System complexity increase \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of state-of-the-art research in trie structure optimization.  
Our analysis demonstrates a clear evolution in the understanding and optimization of trie structures:  
1. Foundational theoretical work by [PERSON] et al. [5] established crucial properties of Patricia tries, proving that external path length asymptotically equals n\(\cdot\)log\({}_{2}\)n with probability one. This fundamental result suggested that Patricia tries maintain a natural balance without explicit restructuring;
2. [PERSON] et al. [9] extended this understanding through smoothed analysis, demonstrating that both tries and Patricia tries achieve logarithmic height bounds under perturbation, providing theoretical justification for their practical efficiency;
3. Building on these theoretical foundations, our previous work on adaptive Merkle trees [8] introduced dynamic restructuring based on access patterns, achieving significant path length reductions for frequently accessed data;
4. The current work synthesizes these approaches, providing a comprehensive probabilistic framework that:  
5. Recent practical implementations ([PERSON] et al. [15], [PERSON] et al. [14], [PERSON] et al. [13]) demonstrate various optimization approaches but focus primarily on specific aspects of tree performance rather than fundamental structural optimization.  
This progression shows how our work bridges the gap between classical theoretical analysis and modern blockchain requirements, providing both theoretical understanding and practical optimization guidelines.
# Performance Analysis of a WPCN-Based Underwater Acoustic Communication System
## 2 System Model
### Transmit Protocol  
The system adopts a harvest-then-transmit protocol as shown in Figure 3, where the transmission of each communication block is divided into two-time phases, i.e., phase I and phase II. In phase I, the H-AP transfers power to the terminal node in the downlink with a time duration of \(\tau_{0}T\), where \(T\) is the transmission time of each communication block and \(\tau_{0}(0\leq\tau_{0}\leq 1)\) is the time allocation factor. In phase II, the terminal node uses the harvested energy to transmit information data to the H-AP in the uplink with a time duration of \((1-\tau_{0})T\). To effectively counter the inter-symbol interference caused by multipath fading, the OFDM modulation scheme is applied to the system.
# MIMO LS-SVR-Based Multi-Point Vibration Response Prediction in the Frequency Domain
## 3 Experimental Verification  
### Acoustic and Vibration Experimental Devices on a Cylindrical Shell  
The experimental device was a cylindrical shell structure. The cylindrical shell structure was fixed on a vibration table using a fixture and hung using elastic rubber ropes as shown in Figure 2(a). There was an acoustic reverberation acoustic excitation (including an acoustic excitation sensor) in the interior of the cylindrical shell, as shown in Figure 2(b). Additionally, the vibration table (including a vibration excitation sensor) on the exterior was the source of the vibration excitation, as shown in Figure 2(c). Figure 2(d) shows the layout diagram for the measured vibration response. The position and direction of the vibration excitation of the vibration table and external acoustic reverberation of the acoustic excitation were fixed in each experiment, and the sensor response measuring point can reflect the main vibration of the system. In the experiments, a single spherical noise excitation source and a single suspended vibration table excitation were respectively applied to the device, and 18 measuring points were selected to record the response data.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Comparison Index** & **Transmissibility** & **Transfer Function** & **Multiple Linear** & **MIMO Neural** & **MIMO LS-SVR** \\ \hline Need transfer function or not & Yes & Yes & No & No & No \\ Robustness & Medium & Medium & Bad & Good & Good \\ Need to search for & No & No & No & Yes & Yes \\ hyperparameters or not & & & & & \\ Matrix inverse illnesses exist & Yes & Yes & Yes & No & No \\ or not & & & & & \\ Explanatory & Good & Good & Medium & Bad & Bad \\ Time complexity & Good & Good & Medium & Bad & Bad \\ Suitable for linear or & Linear only & Linear only & Linear only & & Both linear and \\ nonlinear systems & & & & & nonlinear \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the different methods.
## 2 Challenges, Trends and Constraints in WSAN Hardware Design
### Open-Source-Hardware Architectures versus Open-Architecture Systems
#### 2.3.1 Signals Management Constraints
For instance, when the MCU outputs a signal to a particular shield, then this signal is needlessly forced to be an input to the rest shields due to the common BECs signal pins. On the other hand, the existing BEC style of mechanical standardization limits the full exploitation of the overall system, since any single signal of the BECs, except from the supply voltages and ground pins as well as the data busses pins, can be used only from one shield and the main-board, so the functionality is sacrificed on the altar of the invaluable expandability and reusability.
# Laser Cooling and Trapping of Rare-Earth-Doped Particles
## 3 Optical Refrigeration of Rare-Earth-Doped Particles
### Laser Cooling of Rare-Earth-Doped Mie Resonant Samples
The silicon substrate was at room temperature. In 2022, 10%Yb\({}^{3+}\):LiLuF\({}_{4}\) (Yb\({}^{3+}\):LLF) crystals synthesized using a safe and scalable hydrothermal method were laser-cooled more than 15 K below room temperature in air and 5 K in deionized water. They were pumped with a 1020 nm diode laser [34].
# Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues
## 4 Localization Techniques
### Device-Free Localization
#### 4.2.5 Wi-Fi-Based Localization  
Wi-Fi-based localization estimates the object's location by using particular features of the signal distribution. However, it is a comparatively expensive setup. [PERSON] et al. [73] used the K-Nearest Neighborhood algorithm to develop wireless subarea localization (WiSal) positioning scheme to estimate the human location. It also uses signal modifications of distinct sub-regions through clustering. RSS on few antennas specifies the individual's accessibility to the receiver, which requires a massive difference in the signal magnitude.  
Consequently, it helps to differentiate simply the human location into sub-regions. Channel State Information scheme [74] is resilient to sequential alteration and susceptible to environmental changes through frequency divergence. The advantage of frequency divergence in CSI is that it considers diverse multichannel replications. In the experimental phase of CSI, inactive fingerprints for the location estimate of a solo object are generated. Wi-Fi-based localization is most suitable for indoor localization. Device-based and device-free localization techniques are summarized in Table 3 with reference to the parameters like; accuracy, energy efficiency and cost.
# Cutting Performance and Wear/Damage Characteristics of PCBN Tool in Hard Milling
## 3 Experimental Analysis
### Tool Damage  
There are three types of tool damage: the first type of damage is low-speed chipping; the second type of damage is crack-free fragmentation; the third type is high-speed damage [31]. Ten PCBN tools were used to study tool damage, five of which were used to study low-speed impact damage. The failure modes of PCBN tools at different cutting speeds are shown in Figure 16. The main failure modes of PCBN tools in the process of cutting hardened steel at low speed were tool micro-chipping, and low-rate impact damage of the rake face and the flank face. The main failure modes of PCBN tools in the process of cutting hardened steel at high speed were flank wear and high-rate fatigue damage.  
In the low-speed machining process of hardened steel, the rake face of the PCBN tool suffered micro-chipping, as shown in Figure 16a. When the tool rake face suffered micro-chipping, the extrusion effect of the tool on the machined surface increased, which led to a sharp increase in cutting temperature. Then, the chips were melted and adhered to the tool tip (as shown in Figure 17), which further led to the deterioration of the cutting position of the tool tip.  
Figure 16: Macroscopic damage morphology of PCBN tool intermittent cutting of hardened steel tool (Test 1): (**a**) micro-chipping (v = 50 m/min); (**b**) the rake face (v = 50 m/min); (**c**) the flank face (v = 50 m/min); (**d**) flank wear (v = 250 m/min); (**e**) high-rate fatigue damage (v = 350 m/min).  
Figure 15: Adhesive wear: (**a**) wear surface morphology of flank face; (**b**) elemental analysis at point A.  
There were two main forms of low-rate impact damage of PCBN tools in the intermittent machining of hardened steel: (1) the conchoidal damage of the rake face (as shown in Figure 16b); (2) the larger damaged area of the flank face (as shown in Figure 16c). The conchoidal damage of the rake face was mainly caused by the improper selection of feed and cutting depth at a small cutting speed. This form of damage was mainly caused by mechanical impact. The larger damaged area of the flank face was caused by the bending stress of the cutter, which was greater than the bending strength of the tool material itself [31].  
The wear mechanism of the flank was detailed in Section 3.5.  
PCBN tools can resist high-temperature wear at high speed. However, in intermittent machining, the tool was subjected to alternating thermal load and impact load, which led to fatigue crack of the tool after a certain period of stable cutting. The crack extension led to high-speed fatigue damage of the cutter, as shown in Figure 16e.