esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Cross-Domain Knowledge Transfer  
To evaluate the generalization capability of the GLR framework across different knowledge graph domains, we design a cross-domain transfer experiment. Specifically, models  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Set** & **MRR** & **Hits@1** & **Hits@3** & **Hits@10** \\ \hline \multirow{2}{*}{UMLS} & Valid & 0.781 & 0.688 & 0.861 & 0.941 \\  & Test & **0.804** & **0.715** & **0.893** & **0.962** \\ \hline \multirow{2}{*}{FB15K-237} & Valid & 0.497 & 0.375 & 0.488 & 0.621 \\  & Test & **0.507** & **0.402** & **0.511** & **0.643** \\ \hline \multirow{2}{*}{WN18 RR} & Valid & 0.653 & 0.598 & 0.684 & 0.777 \\  & Test & **0.679** & **0.621** & **0.705** & **0.796** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evaluation of overfitting risk during LoRA fine-tuning. The Bold indicates the best performance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**UMLS**} \\ \cline{2-4}  & **MRR** & **Hits@1** & **Times (s)** \\ \hline Monte Carlo Dropout & 0.816 & 0.724 & 5.08 \\ Ensemble & 0.831 & 0.736 & 6.35 \\ P(True) & 0.804 & 0.715 & 1.17 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance and efficiency of different confidence estimation methods on the UMLS dataset. The best scores for each metric are shown in underline.  
are independently trained on each of the three datasets and subsequently tested on the remaining two knowledge graphs that were not seen during training. This setup simulates the practical challenges of knowledge reasoning under conditions of substantial domain divergence.  
The results are presented in Figure 8. It can be observed that the GLR framework achieves the best performance when the training and testing domains are aligned. However, when transferred to other datasets, the model exhibits varying degrees of performance degradation, particularly in transfer scenarios characterized by substantial structural and semantic divergence. These findings indicate that cross-domain knowledge transfer remains a significant challenge. The generalization ability of GLR across heterogeneous knowledge graphs is still limited, with noticeable performance bottlenecks under conditions of entity semantic drift and imbalanced relation distributions.
# Tuning Guidelines for an Adaptive-Gain Parabolic Sliding Mode Filter
## 3 Performance Evaluation of AG-PSMF  
This section numerically evaluates the performance of AG-PSMF under different values of parameters and noise intensities. It should mentioned here that, in the upcoming analysis, parameter values are changed in ranges that capture the response tendencies of AG-PSMF, without loss of generality. In addition, \(F=1000\) and \(T=0.0001\) s are used for the discrete-time implementation of AG-PSMF.
# Bone Conduction Auditory Navigation Device for Blind People
## 5 Evaluation of the Proposed Navigation System
### Determination of Self-Location of Subjects  
The self-location of the subjects was estimated based on the GPS. A GPS receiver kit adopting a small high-sensitivity GPS receiver kit GYSFDMAXB (TAYO YUDEN), shown in Figure 1e, was used. This GPS receiver kit was connected to the Arduino Leonard on the Lattepanda alpha 864 s via GPIO code and a breadboard, and the latitude and longitude data were obtained from the GPS receiver kit via serial communication. The data transfer between Arduino and Lattepanda was performed every 1 s using the Arduino IDE and the Pyserial library installed in the Python program. After receiving the data, the distances between the present position and each of the positions from C1 to C8 and G are calculated and used for the navigation. If the distance between the present location and one of the points became shorter than 4 m, then the voice information of Table 9 was reproduced.
# On Global Quantum Communication Networking
###### Abstract  
Research in quantum communications networks (QCNs), where multiple users desire to generate or transmit common quantum-secured information, is still in its beginning stage. To solve for the problems of both discrete variable- and continuous variable-quantum key distribution (QKD) schemes in a simultaneous manner as well as to enable the next generation of quantum communication networking, in this Special Issue paper we describe a scenario where disconnected terrestrial QCNs are coupled through low Earth orbit (LEO) satellite quantum network forming heterogeneous satellite-terrestrial QCN. The proposed heterogeneous QCN is based on the cluster state approach and can be used for numerous applications, including: (i) to teleport arbitrary quantum states between any two nodes in the QCN; (ii) to enable the next generation of cyber security systems; (iii) to enable distributed quantum computing; and (iv) to enable the next generation of quantum sensing networks. The proposed QCNs will be robust against various channel impairments over heterogeneous links. Moreover, the proposed QCNs will provide an unprecedented security level for 5G+/6G wireless networks, Internet of Things (IoT), optical networks, and autonomous vehicles, to mention a few.  
quantum key distribution (QKD); discrete variable (DV)-QKD; continuous variable (CV)-QKD; postquantum cryptography (PQC); quantum communications networks (QCNs) +
Footnote †: journal:  
_Perspective_  
On Global Quantum Communication Networking
# A Review of Blast Loading in the Urban Environment
## 3 Numerical Methods
### Eagle-Blast  
[PERSON] et al. [102] utilised EAGLE-Blast to simulate geometrically complex blast scenarios. EAGLE-Blast is a blast-specific simulation software that acts as a module, which can be inserted into a larger software package named EAGLE. The addition of the EAGLE-Blast module allows for the inclusion of rigid elements contained in the flow field and is especially _\"tailored for blast simulations\"_. The paper contains multiple studies showcasing the modules' ability to perform simple blast simulations. It states that the results _\"offer confidence that EAGLE-Blast can capture shock propagation and interaction reasonably well\"_. However, due to the approximation nature of the software regarding the ignition and detonation of an explosive, only the relatively far-field behaviour blast effects can be captured with reasonable accuracy.  
The EAGLE-Blast module's suitability in conducting geometrically complex blast simulations is demonstrated through a comparison and validity study. Experimental modelling results from 1:50 scaled blasts were utilised as a basis for the comparison [103]. The simulated overpressures were recorded at the same locations that housed pressure sensors in the experimental model for a direct comparison. The paper does not present all the data, but the results from four pressure sensors for a given charge location are shown in Figure 7. A good correlation is present between the experimental and numerical data. Minor discrepancies were pointed out, but their cause is not certain. They are described as a potential consequence of the measurement fidelity or afterburning effects not being simulated within EAGLE-Blast at the time of writing.  
Figure 7: Pressure vs. time plots comparing experimental and simulated data for a set charge location at four different pressure sensors, adapted from [PERSON] et al. [102]. Note: The paper does not state which pressure sensor is associated with each plot.
## 2 Design of Integrated Passive Components
### Material Stackup  
A four-metal layer material stackup is selected for this demonstration, as shown in Figure 3. The core material is a 150 \(\times\) 150 mm\({}^{2}\), 100 \(\upmu\)m thick glass substrate (EN-A1) panel from Asahi Glass Company, Tokyo, Japan (AGC) and it has dielectric permittivity (\(\varepsilon_{r}\)) of 5.4 and a loss tangent (tan\(\delta\)) of 0.005 at 10 GHz. The polymer dielectric is a low-loss epoxy film from Taiyo Ink. Mfg. Co., Saitama, Japan (ZaristoTM) with 15 \(\upmu\)m and 71 \(\upmu\)m thickness. It has \(\varepsilon_{r}\) of 3.3 and tan \(\delta\) of 0.0025 at 10 GHz. The 15 \(\upmu\)m thick Taiyo Zaristo is laminated onto a bare glass panel on which metal layers M2 and M3 are patterned, followed by lamination of 71 \(\upmu\)m Zaristo onto which M1 and M4 are patterned. M2 and M3 act as ground planes to microstrip integrated passive components on M1 and M4. The stackup is double-sided to avoid panel warpage during the fabrication process. The thickness of copper is set to 8 \(\upmu\)m, which is sufficiently higher than five times the skin depth at an operating frequency of 40 GHz. The total thickness of this stackup is 202 \(\upmu\)m.  
Figure 3: Material stackup for demonstration of integrated passive components.
## 3 Results and Discussion
The experimental results show a similar Mean Squared Error (MSE) for the orientation control of the DC motors at no load and a similar output response between the orientation control of the heliostat and the final reference value for the FLC (Figure 23a) and the PID controller (Figure 23b), despite the load of the wind over the mechanical structure and the backlash in the axis mechanisms. However, for the orientation control of the heliostat, the FLC shows less dispersed error values (Figure 23c) and smaller final reference error values (Figure 23e) than the PID controller (Figure 23d,f).
# Towards a Single Integrative Metric on the Dynamics of Social-Environmental Systems
## 2 IMoSES of the Asian Drylands Belt  
To illustrate this new metric, we used information in a database from [PERSON] et al., (2020) [50] for the countries in the Asian Drylands Belt (ADB) to calculate IMoSES and its terms for two purposes. First, we examine if IMoSES provides new insights for the SESs over those obtained using traditional measures, such as GDP\({}_{\text{pc}}\) and HANPP, and other metrics used in the natural and social sciences (cf. Table 1). Second, we compare the magnitude and dynamics of IMoSES among the political entities (PEs). Here, we designate the ADB to include 18 PEs, covering 14,380,099 km\({}^{2}\) (approximately 30\({}^{\circ}\) N-55\({}^{\circ}\) N; 30\({}^{\circ}\) E-120\({}^{\circ}\) E): Afghanistan (AF), Gansu (GS), Inner Mongolia (IM), Iran (IR), Iraq (IQ), Jordan (JO), Kazakhstan (KZ), Kyrgyzstan (KG), Mongolia (MN), Ningxia (NX), Qinghai (QH), Syria (SY), Tajikistan (TJ), Tibet (TB), Turkey (TR), Turkmenistan (TM), Uzbekistan (UZ), and Xinjiang (XJ). In this database, six Chinese provinces are treated as separate PEs due to their large land areas, positions in arid and semiarid regions, and unique cultures and economic conditions compared to other provinces of China. The database spans a time period of 25 years (1992-2016) (Figure 3). To demonstrate the utility of IMoSES at finer administrative levels, four PEs with annual provincial statistics during 2000-2016 were used. (Note: Provinces are equivalent to oblasts in KZ, vizayets in UZ, aimags in MN, and prefectures in IM).  
Since IMoSES is a newly proposed metric for SES function and dynamics, we first compare the magnitude and variation of IMoSES with its major components in Equations (3)-(5) that reflect major economic, environmental, and social aspects. We then explore the variation of IMoSES with a few selected metrics that have been widely applied in studies of drylands SES: Palmer Drought Severity Index (PDSI), HDI, and SDI. HDI is among the few social indicators of social systems; whereas PDSI provides an integrative measure of drought severity--the most important forcing for the drylands regions.  
Water rather than energy is the most limiting resource in the ADB countries and is consequently used as the \"resource input\" in calculating IMoSES in this paper (Equations (3)-(5)). While the total precipitation of a country is typically used as a proxy for water supply, we argue that total water loss through evapotranspiration (ET, mm yr\({}^{-1}\)) in drylands better reflects the available water supporting the social, economic and ecological systems (i.e., the three pillars), particularly because the long-term changes in precipitation across the global terrestrial biosphere have been stable since 1880 [51; 52].
## 3 Background
### Recurrent Neural Network  
Recurrent neural networks (RNNs) excel in processing sequential data. They possess a memory feature, retaining information from previous steps in a sequence as shown in Figure 2. RNNs incorporate inputs (\"x\"), outputs (\"h\"), and hidden neurons (\"A\"). A self-loop on hidden neurons signifies input from the previous time step (\"t - 1\"). However, RNNs face challenges like the vanishing gradient problem, mitigated by techniques like long short-term memory (LSTM) units. For instance, if the input sequence comprises six days of stock opening price data, the network unfurls into six layers, each corresponding to the opening stock price of a single day. However, a significant challenge confronting RNNs is the vanishing gradient problem, which has been effectively addressed through various techniques, including the incorporation of long short-term memory (LSTM) units into the network.
## 4 Time Continuous Compactness Based _Rwta_  
In the short-distance QKD-ON scenario, any two nodes can directly establish a quantum channel and the maximum distance between them is less than the distance that can carry out point-to-point quantum key distribution process. Consequently, the establishment of the quantum channel and data transmission between the nodes which are not connected directly needs to meet wavelength consistency and time continuity. The operation of RWTA for QKCh and MBCh must consider the effect on connectivity of wavelength-timeslots on the path. Otherwise, it will cause wavelength-timeslot fragments, which is not conducive to the efficient utilization of resources. An example of wavelength-timeslot fragments in RWTA is illustrated in Figure 3. Suppose path 2 (5-6-7-8) is the selected route for the request. When performing wavelength-timeslot allocation with 2 timeslots requirements over this route, the available timeslots with the smallest index would be allocated by using first-fit (FF) policy. In this paper, we consider three possible solutions when time-slots #3 and #4 are allocated on wavelengths \(\lambda\)1, \(\lambda\)2, and \(\lambda\)3, which are shown by the dotted rectangles of different colors in Figure 3a. Different solutions will have different impacts on other paths that share links with path 2. If solution A is selected, as illustrated in Figure 3b, the connectivity of timeslots from #1 to #4 on \(\lambda\)1 of path 3 is broken due to link #8 is being shared by path 3 and path 2. If a new request that needs 3 timeslots and starts transmitting before #4 timeslot arrives on path 3, it will be blocked since no eligible timeslots can be found. A similar problem arises while choosing solution B, as illustrated in Figure 3c. However, solution C, as illustrated in Figure 3d, does not break the wavelength-timeslot connectivity of path 1 and path 2. In order to reduce the blocking probability of subsequently arriving requests, solution C should be chosen as the final wavelength-timeslot allocation.  
Figure 3: An example of wavelength-timeslot fragments in RWTA: (**a**) Three possible solutions on wavelengths \(\lambda\)1, \(\lambda\)2, and \(\lambda\)3; (**b**) Resource status on \(\lambda\)1 after solution A; (**c**) Resource status on \(\lambda\)2 after solution B; (**d**) Resource status on \(\lambda\)3 after solution C.  
Therefore, a reasonable RWTA scheme should try its best to guarantee that the resource connectivity on each path is not damaged. In order to avoid the fragments of wavelength-timeslot resources, this paper introduces the concept of time continuous compactness (TCC), which is used to describe the connectivity of wavelength resources on the links of each path in a time dimension. Furthermore, a relative loss of time continuous compactness (ReLoss-TCC)-based RWTA strategy is proposed.
# Advanced Biological Imaging for Intracellular Micromanipulation: Methods and Applications
## 2 Image Sampling Methods
### State-of-the-Art Microscopes
#### 2.1.2 Two-Photon Fluorescence Microscope (TPFM)  
TPFM principle is based on two-photon absorption and excitation phenomena, that is, the combined energy of two quasi-simultaneously absorbed photons is sufficient to match the energy gap between the ground and excited states [56]. The typical structure of a TPFM is illustrated in Figure 1e. With the reduction of photo-bleaching effect, TPFM has become a powerful instrument for long-term measurement [57]. For example, TPFM can be used to monitor the movement of transferred T-cells in mice for 48 h [58], as shown in Figure 1f. TPFM is not susceptible with interferences to fluorescence concentration and light sources. TPFM can be used to accurately detect fluorescent probes to track dynamic changes of hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) and adenosine triphosphate (ATP) in neurons [59]. In addition, TPFM can perform tomography to measure specimens at different optical depths. A TPFM-based rapid bacteria localization platform was proposed. This technique produced z-stack images to distinguish the intracellular and extracellular bacteria [60]. However, TPFM is mainly limited by the low imaging speed (about 0.5 Hz), and it take tens of minutes to obtain a high-resolution image stack for the traditional TPEM [61]. Recently, an advanced TPFM with a high sampling speed of 2.7 kHz has been developed using a multifocus compressive sensing method and a digital micromirror device [62]. Besides, the spatiotemporal resolution of TPFM is relatively low, which is theoretically 500 nm laterally and 1 \(\upmu\)m axially; it also follows the diffraction limitation [63]. Other studies haveproposed a fast high-resolution miniaturized two-photon microscope (FHIRM-TPM) with a lateral resolution of 640 nm and an axial resolution of 3.35 \(\upmu\)m [64], and a photomultiplier TPFM with a lateral resolution of 550 nm [65]. The measured lateral resolutions of a TPFM with a double-wavelength meta-surface object lens at light wavelengths of 820 and 605 nm are 930 and 680 nm, respectively [66]. For TPFM, the visualization of biological objects smaller than hundreds of nanometers in size seems challenging. However, TPFM, which benefits from high-energy photons, can drive the electronic excitation of deep fluorescent molecules and is suitable for deep tissue imaging [67]. Specifically, TPFM has attracted extensive interest in studies of the structure and function of the mammalian brain in vivo [68]. Last but not least, research should note the high purchase costs of TPFM, which are normally more expensive than others [69].
## 6 Conclusions  
An experimental investigation has been successfully developed to analyse the effect of water depth on the energy, exergy, and water productivity of a single slope passive solar still located in the Baghdad centre. The decrease in depth of basin water has managed to increase the basin water temperature, thus increasing evaporation and convection heat transfer processes. The average rate of evaporation heat transfer coefficient of a still with 1 cm is found at 17.4%, which is higher than the evaporation heat transfer coefficient of a still with 2 and 3 cm by around 15.1-4.8%. This led to a decrease in water productivity and energy efficiency of stills with 2 and 3 cm in comparison to a still with 1 cm. The average energy efficiency of stills with 1 cm is 23.6%, which is higher than the energy efficiency of a still with 2 and 3 cm by around 9.3-1.1%. In terms of exergy efficiency, the still with 2 cm attained the most elevated exergy efficiency, followed by the still with 1 cm and then the still with 1 cm. Overall, it can be concluded that the still with 1 cm is the most acceptable option in terms of energy efficiency and water productivity as it is ranked highest in all obtained parameters. To further understand the performance behaviour, the basin water depth (below 1 cm) should be checked.  
Conceptualization, H.A.A.W.; Data curation, H.Q.A.K.; Formal analysis, H.Q.A.K. and W.A.K.A.-M.; Investigation, H.Q.A.K., H.A.A.W. and W.A.K.A.-M.; Methodology, H.Q.A.K. and W.A.K.A.-M.; Resources, H.Q.A.K.; Software, H.A.A.W.; Supervision, F.A. and B.E.; Visualization, F.A.; Writing--original draft, H.Q.A.K.; Writing--review & editing, W.A.K.A.-M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
We acknowledge support by the Deutsche Forschungsgemeinschaft (DFG) German Research Foundation and the Open Access Publishing Fund of the Technical University of Darmstadt. The authors would like to also thank the University of Technology-Iraq.  
The authors declare no conflict of interest.  
Figure 12: Accumulated water productivity with respect to time from the three considered cases of the still.
# From Stationary to Nonstationary UAVs: Deep-Learning-Based Method for Vehicle Speed Estimation
###### Abstract
This study aims to enhance the existing systems by providing more accurate speed measurements and trajectory estimations by utilizing AI and UAVs. This method offers a practical solution that effectively works with both stationary and nonstationary aerial footage, demonstrating remarkable flexibility. This method is capable of accurately mapping vehicle trajectories in real geographical space. Furthermore, it shows high precision in measuring velocity, with an error margin as low as 0.53 km/h. Implementing this solution can provide significant value for intelligent road traffic monitoring systems. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Drone Type** & \multicolumn{2}{c}{**Advantages**} & **Disadvantages** & \multicolumn{2}{c}{**Uses**} \\ \hline \multirow{4}{*}{Multirotor UAVs} & - & Vertical take-off and & \multirow{4}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{Aerial inspection, thermal reports, and 3D scans.} \\  & & landing (VTOL) & - & Shorter flight durations & & \\  & - & Hovering enabled & - & Smaller payload capacity & \\  & - & User-friendliness & & & \\ \hline \multirow{4}{*}{Fixed-Wing UAVs} & - & Increased coverage area & - & No hovering capability & - & Aerial mapping, \\  & - & Extended flight time & - & Difficult for novice pilots & & precision agriculture, \\  & - & Enhanced speed & - & Higher costs & & surveillance, and \\ \hline \multirow{4}{*}{Single-Rotor UAVs} & - & Hovering enabled & \multirow{4}{*}{-} & \multirow{4}{*}{-} & \multirow{4}{*}{-} & \multirow{4}{*}{-} \\  & - & Greater endurance & & & \\ \cline{1-1}  & - & VTOL & - & Difficult for novice pilots & - & Aerial LIDAR laser scan \\ \cline{1-1}  & - & Greater payload & - & Higher costs & & and drone surveying. \\ \hline \multirow{4}{*}{Fixed-Wing Hybrid UAVs} & - & Vertical take-off & - & Best of both worlds: with a & \multirow{4}{*}{-} & \multirow{4}{*}{-} \\  & and landing & & little trade-off in hovering & & \\ \cline{1-1}  & - & Long-endurance flight & \multirow{4}{*}{-} & \multirow{4}{*}{-} & \multirow{4}{*}{-} \\ \cline{1-1}  & & Long-endurance flight & & and forward flight. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Drone types, some of their civil applications (non-exhaustive), advantages, and disadvantages.