esa-sceva/satcom-chunk-collection huggingface dataset:
# Distributed Inference Models and Algorithms for Heterogeneous Edge Systems Using Deep Learning
## 4 Convolution-Layer Partitioning Algorithm  
Previous studies [13; 14] have shown that convolutional layers account for most of the computation time and memory usage in DNN inference. In [12], the convolutional layers were divided into independently executable segments to speed up inference across multiple edge devices. To minimize inference time and energy consumption and find the optimal mapping between edge devices and workload partitions, we develop an enhanced version of the FTP algorithm [14]. First, layers are partitioned along the height of the input feature map, with each segment assigned to a worker device. A split point (\(p\)) divides each network layer into two parts, forming two fused blocks to reduce redundancy. Additionally, ResNet residual blocks (BasicBlocks) are converted into convolutional layers to simplify distributed inference. The resultant algorithm is the one-dimensional partitioning algorithm termed as OD-FTP.
## 4 Experimental Evaluation and Results
### Experimental Setup  
To evaluate the performance of the proposed framework, we conducted the simulation compromising of 4000 rounds of communication using a python environment using _PyTorch_ machine learning libraries on Intel (r) Core (tm) i7-8550 @ 4 GHz system and all the experimentation is performed in this simulated environment. Table 3 below describes the simulation parameters and settings utilized for the experiments.
## 4 Seismic Performance of a Masonry Structure with RC Frames on the First Story with CFST Dampers
### Result Analysis of Three Structures JG-1, JG-2, and JG-3
#### 4.2.4 Base Shear Analysis of Three Structures JG-1, JG-2, and JG-3  
The comparison of base shear time-history curves of three structures under the seismic action of the EL-Centro wave is illustrated in Figure 21.  
It can be seen from Figure 21 that the amplitudes of the time-history curves of the base shear of the three structures are quite different. Compared with JG-2 and JG-3, the maximum value of the base shear of JG-1 decreases by 36.3% and 48.2%, respectively. The reason for this is that the base shear force of the structure is related to the ductility of the first story. As the JG-1 is equipped with CFST dampers, the first story plays a role in seismic isolation when the structure is under the action of earthquake, which reduces the seismic response of the upper masonry.  
It can be seen from Table 4 that when the whole structure of JG-1 is in the elastic stage, most of the base shear force is borne by the CFST dampers, i.e., about 78%. The energy dissipation device is fully utilized to dissipate energy, thus protecting the frame structure of the first story. With the further increase in seismic action, the energy dissipation device yields when the structure enters the plastic deformation stage and cannot bear more base shear. At this time, part of the shear is borne by the frame column, but the base shear ratio borne by the CFST dampers is about 58%, and the effect is still good.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Peak** \\ **Acceleration** \\ **cm/s2** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Structure** \\ **Code Name** \\ \end{tabular} } & \begin{tabular}{c} **Shear Force** \\ **at Other** \\ **Parts** \\ **V\({}_{\text{w}}\)/kN** \\ \end{tabular} & \begin{tabular}{c} **Column** \\ **Shear** \\ **V\({}_{\text{c}}\)/kN** \\ \end{tabular} & \multirow{2}{*}{\
\begin{tabular}{c} **Total Shear** \\ **V\({}_{\text{w}}\)/V** \\ **V\({}_{\text{c}}\)/kN** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **V\({}_{\text{w}}\)/V** \\ **V\({}_{\text{w}}\)/kN** \\ \end{tabular} } \\ \hline \multirow{2}{*}{50} & JG-1 & \(-148.377\) & \(-40.880\) & \(-189.257\) & \(0.784\) \\  & JG-2 & \(-247.899\) & \(-37.370\) & \(-285.269\) & \(0.869\) \\  & JG-3 & \(-293.540\) & \(-34.805\) & \  
Compared with JG-2 and JG-3, the energy dissipation device in JG-1 bears most of the seismic action, reduces the seismic response of the upper masonry, and has good seismic performance in general.
## 3 Results and Discussion  
### FE Simulation for Edgewise Compression Behavior  
Figure 8 shows the load vs. displacement plots predicted using the FE model by flute types and standards. For all standards, the double-wall (DW) and single-wall (SW) corrugated paperboard had significantly different gradient and peak points of the load-deflection plots. For the FE model built based on the KS M 7063-1 method A [13], which is an edge clamping method, the peak points of AB/F and A/F were greater than those of BB/F and B/F, respectively. However, for the models built based on the FEFCO No.8 method [15], which is a rectangular test specimen method with a small slenderness ratio, there was an opposite trend compared to the KS M 7063-1. The trend change was the consequence of the relatively small slenderness ratio of the FEFCO No.8 test specimen (Table 15 in Appendix A), resulting in a reduced effect of the corrugated paperboard thickness on the edgewise compression behavior.  
Both SW and DW corrugated paperboard had similar gradients in the elastic region. The stiffness of the DW corrugated paperboard was higher than the stiffness of the SW corrugated paperboard.  
The failure mechanism for each test method and flute type was analyzed using the distribution of stresses and deformation at the peak point of the load vs. displacement plot (Figures 9 and 10).  
For the KS M 7063-1 method A and the TAPPI T 838 FE models, where test specimens were neck-down at the middle, local buckling of the liners around the neck-down section was observed. In particular, the stress concentration was observed close to the edge of the neck-down section. For the FEFCO No.8 FE models, the stress was distributed evenly to the entire surface, and therefore local buckling of the liners was uniformly observed over the entire surface of the specimen. The stress distribution and the local buckling shape for the rectangular test specimen of the FEFCO No.8 was similar to the results observed by [PERSON] [2] for different rectangular test specimens (\(5.08\times 5.08\) cm (TAPPI T 839) and \(3.81\times 5.08\) cm (non-standard ECT geometry)).  
Figure 7: Static-frictional coefficients between corrugated paperboard components.  
Unlike A/F, B/F, and BB/F, whose neutral axis was placed at the middle of the corrugated paperboard thickness (\(z\)-direction) (Table A1 in the Appendix A), AB/F had bulging toward the B/F direction due to the higher compression stress generated at the inner liner connecting the A/F to the B/F. Once bulging had occurred, the inner liner became a compression state and the outer liner became a tension state. The bulging of AB/F was shown for TAPPI T 839; however, it was not observed for the FEFCO No.8 specimens due to their small slenderness ratio.
## 7 Discussion
### Indoor Geofencing Design  
The significance of geofencing design in indoor AR navigation systems is often underestimated. Although geofencing is a well-established technique in LBS and may seem straightforward, its effective implementation is crucial for enhancing user experiences. Properly designed geofencing, in conjunction with accurate tracking, can significantly enhance the user experience by providing timely and location-specific information or navigation cues, especially in complex indoor environments such as museums or shopping malls. Implementing geofencing indoors presents unique challenges not encountered in outdoor settings or beacon-based solutions. The precision required is considerably higher due to the complexity of the indoor spaces and the need for centimeter-level alignments in AR views. Consequently, elements including human dimensions, building structures, positioning errors, walking speed, and methods of displaying navigation cues must be considered.  
Our current geofencing design is derived from empirical experiments, and its applicability to different indoor settings remains unclear. For instance, if improved indoor positioning accuracy can be achieved, then the buffer adjustment for the geofence radius needs to be modified accordingly. The principle of indoor geofencing design and the systematic determination of the placement and size of geofences remain important areas for future research. It is also necessary to incorporate user feedback into the design of indoor geofencing.
## Abbreviations  
The main abbreviations used in this paper are illustrated following:  
\begin{tabular}{l l}
**Abbreviations** & **Full Name** \\ CSSA & Circular Space Situational Awareness \\ ISL & Inter-Satellite Link \\ CEI & Connected Element Interferometry \\ CRTBP & Circular Restricted Three-Body Problem \\ PNT & positioning, navigation, and timing \\ CVN & China VLBI Network \\ SOAS & Shanghai Observatory of the Chinese Academy of Sciences \\ GNSS & Global Navigation Satellite System \\ BDS & BeiDou Navigation Satellite System \\ LEO & Low Earth Orbit \\ GEO & Geostationary Earth Orbit \\ IGSO & Inclined Geosynchronous Orbit \\ MEO & Medium Earth Orbit \\ BDT & BDS system time \\ GPS & Global Positioning System \\ GLONASS & Global Navigation Satellite System \\ QZSS & Quasi-Zenith Satellite System \\ IRNSS & Indian Regional Navigational Satellite System \\ NBI & Narrow-Band Interference \\ SS & Spread Spectrum \\ FIR & Finite-Impulse Response \\ IIR & Infinite Impulse Response \\ DSSS & Direct Sequence Spread Spectrum \\ CNR & Carrier-to-Noise Ratio \\ RCS & Radar Cross Section \\ LiAISON & Linked Autonomous Interplanetary Satellite Orbit Navigation \\ SST & satellite-to-satellite tracking \\ DRO & Distant retrograde orbits \\ \end{tabular}
# Testing Multi-Frequency Low-Cost GNSS Receivers for Geodetic Monitoring Purposes
## 2 Material and Methods  
### Low-Cost GNSS Receiver ZED-F9P  
Nowadays, different types of multi-frequency GNSS receivers that are cheap, light, and able to work in static and dynamic applications are available. At the beginning of 2019, the u-blox company launched the ZED-F9P chip that can receive satellite signals in the lower and upper-L band (L1C/A, L1 OF, E1, B11, L2C, L2 OF, E5b, B2I) from all available satellite constellations (GPS, GLONASS, Galileo, BeiDou) [21]. The u-blox integrated the ZED-F9P module in C099-F9P application board that can work in RTK mode and transmit RTCM corrections via cable, Wi-Fi, or Bluetooth and is easily configurable through the open-source evaluation software u-center [21; 22]. Another board that houses the ZED-F9P chip is simpleRTK2B offered by Ardusimple. The board and u-blox multiband antenna ANN-MB-00 can be purchased with low cost (270 EUR). SimpleRTK2B can be also configured by u-center and has the same GNSS chip with an integrated RTK engine, which enables coordinate estimation in real-time. Both boards that own a ZED-F9P chip may be a proper solution for geodetic monitoring of natural hazards, however, they should first be tested in an optimal environment to analyze their performance.
### Clustering Strategies in Vehicular Ad Hoc Networks  
Within the VANETs framework, clustering assumes pivotal importance, facilitating the assembly of a group of vehicles with similar attributes. In the realm of VANET clustering, the mobility-based approach stands as the most prevalent method. However, several clustering techniques in VANETs have harnessed sophisticated algorithms such as fuzzy logic and machine learning to enhance the stability and efficiency of these clusters [17]. VANET clustering deploys a multi-hop-oriented approach and leverages network mobility (NEMO) techniques. Depending on the specific algorithms in use, the clustering technique in VANETs may opt for either the single-hop or multi-hop approach. Single-hop techniques, in turn, can be further categorized into intelligence-driven and mobility-centric methods [18].  
Furthermore, VANET clustering is enhanced by integrating fuzzy logic into the fray. In the comprehensive VANET approach, the fuzzy logic algorithm is artfully amalgamated with supervised learning algorithms, including Q-learning and machine-learning clustering techniques. This union develops a versatile and efficient clustering mechanism within the context of VANETs. Figure 2 shows the taxonomy of VANET clustering algorithms, which includes all the clustering algorithms' techniques.  
Figure 2: VANET clustering strategies for cluster head selection, neighbor nodes selection, and cluster formation strategy [19].  
The preeminent clustering methodology employed within VANETs is the mobility-oriented clustering technique. This technique pivots around essential metrics rooted in the dynamics of vehicular movement, encompassing parameters such as acceleration, relative speed, positional data, and the direction of vehicular motion, among others. A key challenge in the VANET context is the imperative need to optimize the cluster count. Several clustering techniques predicated on multi-hop packet transmission have been documented in the scientific literature to address this concern, all aimed at curtailing the number of clusters. This approach is pivotal as it allows a cluster head (CH) to extend its coverage over a more expansive geographical area, engendering heightened network stability [20].  
Within the domain of multi-hop strategies, two distinct categories emerge. In the context of two-hop communication, the cluster head is empowered to oversee vehicular communication spanning up to two hops, encompassing a segment of vehicles within its purview. The formation of clusters hinges upon a routing technique that considers vehicle positions and their respective traveling directions. Conversely, in the ambit of 2+ hop algorithms, the cluster head can expand its reach across two or more hops, a scenario witnessed in various multi-hop-based algorithms, such as those catering to three-hop, four-hop, or even five-hop coverage scenarios.
## 2 Methods
### Sound Level Monitoring of the Living Rooms  
One of the main aims of this work was gathering information about the overall acoustic environment of the living rooms of nursing homes. For this purpose, cost-effective sensor nodes were installed in the nine living rooms. The nodes were installed far from specific noise sources which could exaggerate the sound levels, but close enough to where most activities took place in such spaces. Figure 1 shows an example of installation in one of the living rooms.  
The nodes continuously measured 1/3-octave band levels, with a temporal resolution of 125 ms, from 07:00 a.m. Monday to 07:00 a.m. Friday, during a typical week of activity of the nursing homes, between December 2016 and February 2017. During the observation period, data were sent over the internet to the Ghent University server infrastructure located in Ghent, Belgium. Data were subsequently further processed through an agent-based approach and stored in a warehouse database. The A-weighted sound equivalent levels were then calculated on a 15-min basis (L\({}_{\text{Aeq-15 min}}\)) for all data available, for each sensor node. Figure 2 shows a general overview of the system, and more details on the architecture of the sensor network can be retrieved in refs. [10; 11]. Figure 3 shows the daily patterns of the sound equivalent levels in the living rooms during the corresp  
Figure 1: An example of installation of a node in one of the living rooms of the study. The right figure shows the overall context, the left figure shows a close-up of the red box in the right figure.  
To gain further insights into the acoustical characteristics of the living rooms, volumetric data and reverberation times were measured; these are reported in Table 1. The reverberation time (T20) was measured in accordance with the standard ISO 3382-2:2008 (Acoustics--Measurement of room acoustic parameters--Part 2: Reverberation time in ordinary rooms). It was decided to opt for T20 instead of T30 for practical reasons. Due to the potentially sensitive receivers in the proximity of where the measurements were being performed, it was not suitable to use sounds that were too loud. So, to avoid potentially poor signal-to-noise ratio values, it was decided to extrapolate the reverberation time from the 20-dB decay.  
\begin{table}
\begin{tabular}{c c c} \hline
**Living Room** & **\(\approx\)Volume (m3)** & **T20 1 (s)** \\ \hline VH1 & 615 & 0.91 \\ VH2 & 217 & 1.11 \\ SJ & 264 & 0.84 \\ LH1 & 206 & 0.80 \\ LH2 & 222 & 1.30 \\ SV1 & 124 & 0.36 \\ SV2 & 121 & 0.40 \\ SP1 & 342 & 1.58 \\ SP2 & 499 & 1.23 \\ \hline \end{tabular} \({}^{1}\) The T20 reported in the table is an arithmetic average of the T20 values in the 500 Hz–2 kHz frequency range.  
\end{table}
Table 1: Volumetric data and reverberation time for the nine investigated living rooms.  
Figure 3: Equivalent sound level patterns in the living rooms during the monitoring week.  
Figure 2: General overview of the sound monitoring sensor network architecture.
## 3 Results
### Comprehensive Evaluation of Drought Resistance  
#### 3.4.1 Correlation Analysis of Different Indices  
As shown in Table 4, the correlation analysis among drought resistance indices illustrates significant (\(p\) < 0.05) and highly significant (\(p\) < 0.01) positive correlations between germination indices (GR, GP, and GI) and physiological and biochemical indices (MDA, SOD, POD, CAT, Pro) in _S. vicififolia_. This relationship suggests that both growth and physiological and biochemical indices serve as reliable measures for evaluating the drought resistance capabilities of _S. vicififolia_.  
4.2 Comprehensive Evaluation of Seed Germination and Physiological Indices in _Sophora vicifolia_ Treated with Exogenous ABA  
To analyze the results and explore the differences between the seed germination and physiological indices in _S. vicififolia_ treated with exogenous ABA, we conducted principal component analysis (PCA) on 11 physiological indexes. As shown in Table 5, PCA related to _S. vicififolia_ indicated that the combined contribution rate of the first two principal components amounted to 89.19%. In statistical terms, a cumulative contribution rate exceeding 85% for the first two components is sufficient for encapsulating the response of various indices of _S. vicififolia_ under different ABA treatment concentrations. Thus, the indices associated with these principal components serve as comprehensive evaluators for the impact of ABA treatment. The first principal component (PC1) had an eigenvalue of 4.804, accounting for 60.05% of the variance, while the second principal component (PC2) had an eigenvalue of 2.331, contributing 29.14% to the variance (Table 5).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Index** & **GP** & **GR** & **GI** & **MDA** & **SOD** & **Pro** & **CAT** & **POD** \\ \hline GP & 1 & & & & & & \\ GR & 0.587 ** & 1 & & & & & \\ GI & 0.752 ** & 0.750 ** & 1 & & & & \\ MDA & \(-\)0.199 & 0.280 * & 0.197 & 1 & & & \\ SOD & \(-\)0.110 & 0.412 ** & 0.289 * & 0.913 ** & 1 & & \\ Pro & \(-\)0.229 & 0.349 ** & 0.156 & 0.837 ** & 0.896 ** & 1 & & \\ CAT & \(-\)0.189 & 0.384 ** & 0.238 & 0.889 ** & 0.950 ** & 0.910 ** & 1 & \\ POD & \(-\)0.236 & 0.277 * & 0.111 & 0.832 ** & 0.864 ** & 0.881 ** & 0.890 ** & 1 \\ \hline \hline \end{tabular} Note: * represents \(p\) < 0.05; ** represents \(p\) < 0.01. GR, germination rate; GP, germination potential; GI, germination index; POD, peroxidase; SOD, superoxide dismutase; CAT, catalase; MDA, malondialdehydedehyde; Pro, proline.  
\end{table}
Table 4: Correlation coefficients between seed germination and physiological indices of _Sophora vicifolia_.
# Reshaping the Digital Twin Construct with Levels of Digital Twinning (LoDT)
## 4 Assessing the Level of Digital Twinning (LoDT)
### Digital Twinning Dimensions
#### 4.2.6 Intelligence  
The \"intelligence\" dimension reflects the complexity level of data analysis for each case study.  
Descriptive analysis has the lowest complexity level. This type of analysis seeks to answer questions about what happened, using techniques for summarising large amounts of data, such as arithmetic mean, frequency, range, etc.  
Diagnostic analyses, aimed at understanding why something happened, correspond to complexity level 2. In these analyses, there is a need to understand how data interact with each other, and data mining techniques like classification, pattern identification, and clustering are used.  
Level 3 of this dimension is related to predictive analysis. Predictive analyses seek to answer what is likely to happen in a future scenario. In this type of analysis, historical data are often used to identify trends and statistical regression analyses are used for such cases. In other cases, the use of physical-based simulation models (e.g., structural behaviour, energy performance) and different input data also allows for predicting the response of the asset or process in future scenarios.  
Prescriptive analysis is classified with level 4 of complexity. It aims at identifying what should be done based on multiple forecasts. A common application of this type of analysis is route recommendations in applications like Google Maps, Waze, etc.  
Finally, cognitive analysis represents the highest level of complexity. This type of analysis employs various techniques that simulate human reasoning (artificial intelligence) to solve highly complex problems. Cognitive analyses frequently involve vast amounts of data, significant computational capabilities, and sophisticated algorithms. A recent example of this type of application is ChatGPT.
# 40 W All-Fiber Er/Yb MOPA System Using Self-Fabricated High-Power Passive Fiber Components
###### Abstract  
In this work, we demonstrate a three-stage all-fiber master oscillator power amplifier (MOPA) system emitting over 40 W of continuous-wave (CW) output power at \(\sim\)1.5 \(\upmu\)m. The setup utilizes three self-fabricated high-power passive fiber components: a mode-field adaptor (MFA) and two types of pump and signal power combiners. Their development allowed us to become independent from commercially available components, which are often incompatible with fibers used in the experimental setups, resulting in additional losses. A power combiner with single-mode (SM) signal fibers in a configuration (\(5+1\)) \(\times\) 1 was used in the second stage of the MOPA system, which was based on an SM Er-Yb co-doped double-clad (DC) fiber. The fabricated MFA was used to connect the second amplifier stage based on SM fibers with the third amplifier stage based on large-mode-area (LMA) fibers. In the third stage of MOPA system, based on Er-Yb LMA DC fibers, we used the fabricated power combiner based on LMA-type signal fibers in a configuration (\(6+1\)) \(\times\) 1. The presented three-stage MOPA system, utilizing self-fabricated high-power passive fiber components, enables amplification of an input signal of 10 mW up to 44 W of the CW power at the wavelengths of 1555 nm and 1563 nm, corresponding to a gain level of approximately 36.4 dB.  
fiber amplifier; MOPA system; Er-Yb doped fiber amplifier; high-power amplifier; passive fiber components; power combiner; mode-field adaptor +
Footnote †: journal: Applied Physics Letters