esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Addons and Extensions
### Germicide Add-On  
COVID-19, the disease caused by the novel coronavirus, has created a global lockdown. People located in contaminated areas are putting themselves at considerable potential risk. To reduce human contact in these affected indoor and outdoor areas, disinfecting robots are being developed [27]. One of the ways to eliminate the virus without chemicals is to use UV-C light. Automatically controlled non-contact ultraviolet surface disinfection robots can be used because COVID-19 also spreads via contaminated surfaces [28]. This disinfection procedure will inactivate the coronavirus without any further modification.  
UV-based disinfection robots, such as UVD Robot [29], Violet [30], Connor [31], or Wellwit WDR01C [32], are usually made as single-purpose devices. Moreover, their simpler versions must be operated manually. In contrast, the disinfection ability of Phollower is purely designed as an optional add-on. We consider this add-on conception one of the most important features of Phollower.  
Phollower UV+ (see Figure 12d) is the special add-on with germicidal radiators designed to disinfect spaces using UV light in exposed environments such as hospitals. It consists of four 1500 mm long germicidal tubes with 75W power, placed vertically opposite each other to the central supporting structure. A warning beacon is placed at the top of the supporting structure. It lights-up 15 s before the radiator starts to emit light to warn the operator and surrounding humans to leave the place and not to enter during disinfection.  
As the primary objective, safety functions have also been implemented in this add-on. The four widescreen cameras with algorithms for human detection have been placed on the top of the central supporting structure (under the beacon). If a human body happens to be detected during disinfection,  
Figure 11: Freely available Phollower interfaces.  
Figure 12: Various add-ons for Phollower: (**a**) simple transport, (**b**) locked box transport, (**c**) towing add-on, and (**d**) Phollower UV+ germicide add-on.  
the germicidal radiators will turn-off to ensure safety and to prevent any human being exposed to UV radiation.  
There are two disinfection modes for the germicidal add-on:  
1. the UV-C radiator is activated at the stations only, during the transfer to the next station, light emission is deactivated;
2. the UV-C radiator is activated both at the stations and during movement, the user can adjust both the times spent on the station and the movement speed from 0.01 m/s to maximum speed.  
The operating period per one battery charge is around 4 h of disinfection time using a standard monitoring device [33]. It means that several rooms can be decontaminated. Note, that it heavily depends on the spatial arrangement of rooms and resulting movement trajectories.
# Location Analysis for Arabic COVID-19 Twitter Data Using Enhanced Dialect Identification Models
## 3 Arabic Dialect Identification Model
### AOC-Based Results
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Embeddings Level** & **Embeddings Type** & **Model** & **Precision** & **Recall** & **F1 Score** & **Accuracy** \\ \hline Word (unigrams, bigrams, trigrams) & TFIDF & Logistic Regression & 0.7647 & 0.6723 & 0.7019 & 0.7703 \\ Word (unigrams) & TFIDF & Logistic Regression & 0.7949 & 0.667 & 0.712 & 0.7829 \\ Word (1k unigrams) & TFIDF & Logistic Regression & 0.7785 & 0.6635 & 0.7036 & 0.7756 \\ Word (5k unigrams) & TFIDF & Logistic Regression & 0.7972 & 0.6766 & 0.7199 & 0.7879 \\ Word (10k unigrams) & TFIDF & Logistic Regression & 0.7992 & 0.6727 & 0.7175 & 0.7861 \\ Word (20k unigrams) & TFIDF & Logistic Regression & 0.7986 & 0.6693 & 0.7149 & 0.7847 \\ Word (unigrams) & Random Embeddings & BiLSTM & 0.7783 & 0.7179 & 0.7389 & 0.7952 \\ Word (unigrams) & AOC Embeddings & BiLSTM & 0.778 & 0.7038 & 0.7309 & 0.7947 \\ Word (unigrams) & Twitter Embeddings & BiLSTM & 0.8046 & 0.7632 & 0.7811 & 0.8344 \\ Word (unigrams) & Random Embeddings & Attention BiLSTM & 0.7814 & 0.7097 & 0.7344 & 0.7932 \\ Word (unigrams) & AOC Embeddings & Attention BiLSTM & 0.78 & 0.6842 & 0.7189 & 0.7871 \\ Word (unigrams) & Twitter Embeddings & Attention BiLSTM & 0.8001 & 0.7577 & 0.7748 & 0.8278 \\ Subword & AraBERT Embeddings & AraBERT & 0.8086 & 0.7564 & 0.7777 & 0.8364 \\ Subword & ArabicBERT Embeddings & ArabicBERT & 0.8132 & 0.7677 & 0.787 & 0.8396 \\ Subword & MARBERT Embeddings & MARBERT & 0.8402 & 0.8000 & 0.8157 & 0.8586 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The 4 classes dialect identification results.
# Radiometric Method for Determining Canopy Stomatal Conductance in Controlled Environments
## 3 Results
### Control of Canopy Transpiration by CO\({}_{2}\) Concentration  
The effect of CO\({}_{2}\) concentration on canopy transpiration and \(\Omega\) was explored using a wheat canopy (27 to 34 day old; PPF = 1600 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\); T\({}_{\rm air}\) = 21 \({}^{\circ}\)C; RH = 68%) exposed to varying chamber CO\({}_{2}\) concentrations ranging between 400 and 1200 \(\upmu\)mol mol\({}^{-1}\) (Figure 10; Table 3).  
The CO\({}_{2}\) concentration was raised in steps from 400, to 700, to 950, and to 1200 \(\upmu\)mol mol\({}^{-1}\) and allowing a 48 h acclimation period at each CO\({}_{2}\) concentration. Simulated decoupling coefficients were calculated for increasing values of \(g_{\rm A}\) (Figure 10; 2 mol m\({}^{-2}\) s\({}^{-1}\) (dotted line), 4 mol m\({}^{-2}\) s\({}^{-1}\) (dashed line), and 8 mol m\({}^{-2}\) s\({}^{-1}\) (solid line)). The decoupling coefficient increased as canopy \(G_{\rm S}\) increased, reaching an average \(\Omega\) of 0.65 with a canopy \(G_{\rm S}\) of 2.1 mol m\({}^{-2}\) s\({}^{-1}\) at 400 \(\upmu\)mol mol\({}^{-1}\) CO\({}_{2}\) (Figure 10; Table 3). Increasing CO\({}_{2}\) concentration from 390 to 690 \(\upmu\)mol mol\({}^{-1}\) led to 1.77 X CO\({}_{2}\) or nearly a doubling in ambient CO\({}_{2}\). This change decreased mean daily \(G_{\rm S}\) by \(-\)35%, resulting in a \(-\)23% lower transpiration rate and a -23% reduction in latent heat (Table 3). Since \(g_{\rm A}\) remained constant, this decrease in \(G_{\rm S}\) caused a \(-\)26% decrease in \(\Omega\). These results indicate that \(E_{\rm can}\) is less sensitive to changes in stomatal conductance due to the decoupling of \(D_{\rm S}\) from \(D_{\rm Bulk}\). In addition, an increase in \(P_{\rm net}\) of 15% and a \(-\)23% decrease in \(E_{\rm can}\) resulted in a 150% increase in WUE (Table 3).  
Figure 9: Diurnal changes in \(\Omega\) of (**A**) wheat and (**B**) soybean at two chamber CO\({}_{2}\) concentrations.  
The relative changes in \(G_{\rm S}\), \(E_{\rm can}\), and \(\Omega\) of wheat as CO\({}_{2}\) concentration increased, from Table 3, are shown in Figure 11. Canopy \(G_{\rm S}\) decreased by 52%, but \(E_{\rm can}\) only decreased by 37% when CO\({}_{2}\) concentration was raised from 390 to 1230 \(\mu\)mol mol\({}^{-1}\) because of the feedback between \(E_{\rm can}\) and D\({}_{\rm S}\). In these chamber settings, \(E_{\rm can}\) is much less sensitive to a proportional change in \(G_{\rm S}\) and the reduction in \(E_{\rm can}\) is largely explained by \(\Omega\).  
Figure 10: The decoupling coefficient, \(\Omega\), expressed as a function of canopy \(G_{\rm S}\) in wheat measured at CO\({}_{2}\) concentrations ranging between 400 and 1200 \(\mu\)mol mol\({}^{-1}\).
## 1 Introduction  
Fiber optic sensors can be used to monitor large structures such as bridges, dams, pipelines, _etc_. They can also provide long distance sensing solutions with many measurement points through one optical fiber line in a technique known as distributed sensing, which is very unique for fiber optic sensing technologies [1]. Fully distributed sensing techniques are usually implemented by a sensing optical fiber which can sense some external disturbance influencing any location on the fiber. In general, the fully distributed sensing fiber cables are usually expensive because the fibers should be specially fabricated to sense external disturbances [2; 3; 4]. However, the quasi-distributed sensing techniques can be economically implemented with point sensing probes installed on conventional optical fibers even though the number of sensing points on each probe are limited.  
For detecting water leakage, various fiber optic sensors have been developed by utilizing hydrogels [5], polyacrylic acid polymers [6], and the mechanical macro-bending technique [7]. These can be implemented by a configuration of quasi-distributed sensing techniques for multi-sensor systems. In the water leak detection methods, swellable material techniques have been employed due to their simple detection scheme since they absorb water and result in a bending loss due to their expansion [8; 9]. For detecting sensing locations and levels of events, an optical time domain reflection (OTDR) technique has been widely employed [10; 11; 12; 13]. OTDR measurements allow locating and detecting water leak events from the sharp drops of backscattered light [14]. However, these light scattered techniques based on OTDR should usually detect small and short backscattered lights which limit the dynamic ranges of sensors [15]. The small dynamic range causes a reduction of the number and sensitivity of installable sensors. Therefore, it is necessary to develop new sensing techniques with large dynamic ranges to detect the location and severity of water leakages.  
In this paper, a novel fiber optic sensor probe based on an OTDR measurement is devised using a pair of highly reflected connectors on an optical fiber in order to enhance reflected signals and a vessel of water absorption material in order to detect water leakage. The bending loss of a sensor head is studied according to the amount of water leakage. Three types of sensor probes, having different vessel sizes, are fabricated to study the responsivity to water absorption. Two sensor probes of the same type are connected through one optical fiber line to investigate the characteristics of multiplexing operation. Moreover, a link budget is simply analyzed to investigate how an entire sensor system can be designed to satisfy the demands of users.
# A Reconfigurable Framework for Hybrid Quantum-Classical Computing
## 4 Experimental Results
### Complexity Considerations and Algorithm Classes  
In addition to the empirical regression, we provide a theoretical perspective on the observed acceleration. For classical computations involved in hybrid quantum-classical loops--such as gradient computation, loss evaluation, and parameter updates--the complexity often scales polynomially with the number of qubits, typically as \(\mathcal{O}(n^{2})\) or \(\mathcal{O}(n^{3})\) depending on the encoding and circuit design [39]. FPGA architectures, with their pipelined and parallelizable data paths, can exploit this structure by reducing the effective time complexity for such operations [40; 41]. In contrast, CPU-bound implementations are constrained by sequential bottlenecks and memory latency. As a result, tasks that map well to fixed-point arithmetic, linear algebra primitives or element-wise matrix operations (e.g., parameter-shift gradient evaluations) benefit substantially from FPGA acceleration [33; 42]. A general class of algorithms--including variational quantum classifiers, variational quantum eigensolvers (VQEs), and QAOA and QGAN loss modules [43]--fall into this category and represent promising targets for our proposed pipeline.
## 3 In Situ Measurement and Analysis on Deformation Pattern
### Bedrock Deformation Results  
As shown in Table 2, a strongly positive correlation between reservoir water level and groundwater pressure in limestone is observed while the valley narrowing process has not totally converged yet. Meanwhile, the temperature beneath the dam site decreases to 17.5 \({}^{\circ}\)C and is still in progress. Based on the comprehensive consideration of these tendencies, bedrock deformation is derived from the deformation monitoring system and closely examined.  
Seven plumb lines and several external deformation supervision points are installed inside the dam to monitor the deformation of the dam. As shown in Figure 8, the red vertical lines represent the plumb lines implanted in the dam, while the blue circles represent external deformation supervision points.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**ID** & **Correlation Coefficients** \\ \hline ZK01 & 0.9995 \\ ZK02 & 0.9864 \\ ZK03 & 0.9893 \\ ZK04 & 0.9970 \\ ZK05 & 0.9976 \\ ZK06 & 0.9920 \\ ZK07 & 0.9900 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Correlation coefficients between reservoir water level and groundwater level in limestone.  
Figure 7: The process at the bottom of the dam site.  
As plumb lines can monitor the relative deformation between anchoring joints and monitoring points, it is possible to derivate the deformation pattern of bedrock by combining the plumb line with external deformation supervision measurements. More details about the calculation method have been discussed by [30].  
The result of bedrock deformation is shown in Figure 9. Two deformation characteristics shall be highlighted. Firstly, the transverse deformation along the vertical direction remains constant, which excludes the possibility of interlayer-gliding. Secondly, the deformation of the riverbed is larger than the dam abutment, which indicates that the limestone underneath the riverbed is the main deform zone, rather than the slope.  
Figure 8: Layout of the plumb line system and external deformation supervision system.  
Figure 9: Horizontal deformation pattern of the rock base (upstream view, November 2012–November 2019).  
These are the most unique and important features that have rarely been observed in other similar projects. As many cases can be interpreted as landslides or local surficial movements due to changes in the seepage field. The main deform zone is always the river valley itself. Thus, a new physical model of the valley narrowing deformation shall be proposed to explain the deformation pattern. The effect of thermal field variation has been gradually recognized based on the comprehensive consideration of the abnormal geothermal field before the impoundment and the unique deformation features after the impoundment.
## 4 Results and Discussion
### Buffer Measure
#### 4.3.3 Effectiveness of the Buffer Structure
The vehicle multi-body dynamics model, flexible track, and bridge models established above are imported into the UM software and combined to form a dynamics model of the vehicle-track-bridge coupled system, as shown in Figure 16. The track irregularity spectrum is based on the Chinese high-speed railway slab track spectrum. The time-varying aerodynamic load of each carriage of the HST is recorded in a standardized format as.txt files, which are then imported into UM and applied to the center position of each car using T-force elements. In this study, the wheel load reduction rate (WLRR) and derailment coefficient (DC) are adopted to assess the running safety of the train passing through the wind barrier area in crosswind conditions. The safety limits of WLRR and DC are 0.6 and 0.8, respectively [27]. Figure 17 shows the variations in the WLRR and the DC of the head car as the HST enters the wind barrier area with and without a buffer structure under a crosswind speed of 15 m/s. The running speed of the HST is 250 km/h. It can be observed from Figure 16 that the WLRR and the DC of the head car significantly increase when the train enters the wind barrier without the presence of a buffer structure, with maximum values of 0.4484 and 0.3643, respectively. However, with a buffer structure of 90 m in length, the maximum values of the WLRR and DC of the head car decrease to 0.3843 and 0.3457, respectively, representing reductions of 14.30% and 5.11%. This indicates that the buffer structure installed at the end of the wind barrier of the bridge significantly enhances the safety of the HST operations. Figure 16: Schematic of multi-body system dynamics model. Figure 17: Analysis of safety index for the head car with and without buffer structure: (**a**) WLRR and (**b**) DC.
# Modeling Intelligent Control Switch IEC 61850 Based Substation Automation Communication
###### Abstract  
With the advent of the new information technologies, the power communication service object is continuously enhanced by the modernization level of the switches architecture designed in the substation automation environment. In the futuristic plan, the communication network not only provides a transmission line between devices, but also takes into account the reliability and efficiency of the power system communication network. Many protocols are used for the provision of faster and more robust communication among control centers, Remote Terminal Units (RTUs) and Intelligent Electronic Devices (IEDs). These protocols support the exchange of a wide range of possible common data that is organized by a DATA-SET, such as in synchronized circuit breaker switching, distance protection and overcurrent protection. However, these protocols do not give the expected performance with standard switches architectures due to the remarkable traffic in power system communication. To address this issue, this paper modelled a substation automation switch based on the packet forwarder technique based on Generic Object Oriented Substation Event (GOOSE) protocol with different architectures of substation automation switches based International Electrotechnical Commission (IEC) 61850 standard. Finally, the simulations results found that the architecture of the four-switch system showed a significant performance, as compared with other architectures.  
IEC 61850; GOOSE; alternative path; drop packet; communication; SimEvents +
Footnote †: journal:  
_Article_
###### Abstract  
In this contribution, a narrowband radio channel model is proposed for rural scenarios in which the radio link operates under near-ground conditions for application in wireless sensor networks dedicated to smart agriculture. The received power attenuation was measured for both transmitter and receiver antennas placed at two different heights above ground: 0.2 and 0.4 m. Three frequency ranges, proposed for future 5G-IoT use case in agriculture, were chosen: 868 MHz, 2.4 GHz and 5.8 GHz. Three ground coverings were tested in a rural scenario: soil, short and tall grass fields. The path loss was then estimated as dependent of the radio link range and a three-slope log-normal path loss model was tailored. Results are explained in terms of the first Fresnel zone obstruction. Commercial Zigbee sensor nodes operating at 2.4 GHz were used in a second experiment to estimate the link quality from the experimental Radio Signal Strength Indicator (RSSI) received values. Two sensor nodes were placed at the same elevation above ground as in the previous experiment, only for short grass field case. The Quality of Service performance was determined in terms of theoretical bit error rate achieved for different digital modulations--BPSK, 8 PSK and 16 QAM--concluding remarkable results for an obstructed radio link.  
Internet of Things; 5G; wireless sensor networks; smart agriculture; near-ground; radio channel model; QoS; log-distance +
Footnote †: journal: Sensors
## 3 Numerical Modelling Approach
### Blast Wave Propagation and Structural Interaction (Stage 2)  
The corner structure was modelled within a 3D domain filled with air (as an ideal gas) using a series of cuboid obstacles with dimensions and arrangements consistent with the experiments (Figure 4). By default, obstacles specified in Viper::Blast have reflective boundary conditions, thus modelling a perfectly rigid structure with reflective surfaces. Pressure monitoring points were assigned at four locations (A, B1, B2, and B3) in the domain using 3D Cartesian coordinates corresponding to the experimental setups. To accurately model the scenario, a reflective boundary condition was assigned to the lower z-plane of the domain to model the ground surface. Additionally, transmissive boundary conditions were assigned to all other domain boundaries to model free-field (unobstructed) blast wave propagation. In the first stage, the 3D remap file was incorporated into the larger 3D domain (containing the structure) using coordinates for the detonation point corresponding to the experimental setup (Figure 4). The same multi-material solver was used in the second stage.  
The outer dimensions of the 3D domain were made sufficiently large to prevent any potential boundary perturbations from interfering with regions of interest, particularly in the vicinity of pressure gauge locations. During the development of the 3D model, an iterative approach was taken to optimise the domain size. The objective was to strike a balance between providing ample space to minimise unwanted boundary interference effects and reducing the computational cost associated with the modelling problem. The 3D modelling domains comprised exterior dimensions of 600 \(\times\) 1000 \(\times\) 900 mm for the 75% scale and 800 \(\times\) 1400 \(\times\) 1100 mm for the 100% experiments.  
Sensitivity studies were undertaken to optimise the mesh size and verify the accuracy of the 3D models. In Viper::Blast, a Cartesian mesh with uniformly sized cube-shaped cells was specified. Using a multi-material solver (method number 4), model sizes (total number of cells) were limited by the size of the graphics card. For this study, mesh convergence was observed for cell sizes of 1.5 mm and 2 mm for the 75% scale and 100% scale models, respectively. This led to a total of 160 million cells for the 75% scale model and 154 million cells for the 100% scale model within the respective modelling domains.  
Following sensitivity studies, each 3D model was run for 5.0 ms, which allowed sufficient time for the primary blast wavefront to propagate around the corner and pass the furthest measurement location (gauge B3). Pressure data were extracted at time increments of 0.1 ms; this provided adequate resolution of pressure histories while maintaining manageable data storage. Simulations were run on a Dell Alienware Aurora R11 workstation (Intel(R) Core(TM) i9-10900 KF @ 3.70 GHz CPU) with 64 GB RAM and NVIDIA GeForce RTX 3080 Ti 12,287 Mb graphics card.
## 4 Experiments
### Datasets and Evaluation Indicators  
VisDrone2019 is a conventional dataset widely used to evaluate algorithms of multi-category object detection based on UAV images. The dataset contains 6471 training images and 548 testing images with a resolution of 1360 \(\times\) 765 from 10 classes. In the VisDrone2019 dataset, the scales of objects are diverse, and the size of most objects is less than 32 \(\times\) 32.  
ARH2000 is established in this study for monitoring mouse pests. We collected aerial images of five sample plots with different densities of effective gerbil holes in April 2020 and April 2021. We used quadrotor M200 UAVs (DJI-Innovations, Shenzhen, China) as the data acquisition platform and selected clear and cloudless days from 12:00 to 14:00 to minimize the impact of shadows on the classification results. The altitude was set to 30 m, and the forward and side overlaps were set to 70% and 80%, respectively. A total of 445 images were collected, with each sample plot having 89 images of size 5280 pixels \(\times\) 3956 pixels. We used Pix4 Dmapper 4.5.6, a professional drone mapping software, to correct and mosaic the images and generate digital orthophoto maps (DOMs) with a resolution of 0.4 cm. The mouse holes (black patches with specific textures and shapes) in the UAV images are carefully labeled with a labeling tool and then cropped to 300 \(\times\) 300 to obtain the dataset. This dataset comprises 1728 images for training and 432 images for testing. A considerable amount of black patches in the data, which were formed by withered grass shadows, increases the difficulty of identifying mouse holes.  
Average precision (AP) and mean average precision (mAP) are used as evaluation indicators of accuracy, and FPS (frames per second) and model parameters are indicators of efficiency.