esa-sceva/satcom-chunk-collection huggingface dataset:
# An S/C/X-Band 4-Bit Digital Step Attenuator MMIC with 0.25 \(\upmu\)m GaN HEMT Technology
## 1 Introduction  
Recently, GaN has been receiving attention for its use in wideband and high-power radar applications. GaN-on-SiC HEMT technology is especially popular in radar applications due to its high breakdown voltage and good thermal properties. Despite considerable progress in GaN process technology, it is still challenging to implement microwave control circuits such as phase shifters, true time delay, and digital step attenuators [1]. The attenuator is one of the key components of wideband phased-array radar systems [2, 3, 4, 5]. The low insertion loss and the amplitude and phase errors are the important performance specifications in designing a wideband attenuator. When the attenuator has high insertion loss, an additional gain compensation amplifier is necessary, which results in increasing the DC power consumption and the size of the system. When it has high phase and amplitude errors depending on the attenuation states, additional phase and amplitude correction circuits should be included in the circuit. Figure 1 shows some popular digital step attenuator topologies. The switch-embedded T-type or \(\uppi\)-type resistive attenuators can be implemented with a small chip area and have low insertion loss, since the switch transistors are embedded with the resistive attenuation networks shown in Figure 1a [4, 5]. However, these topologies generally have high phase error and a limited operating bandwidth due to the variation of the parasitic elements in the switch transistors, depending on on- and off-states. Therefore, small transistors should be used to reduce the capacitance variation of the switch transistors, which results in attenuators with high attenuation coverage not being realizable without degrading bandwidth and phase error. In addition, because the size of the switch transistors is limited in GaN technology, designing the attenuator is difficult. The switched path attenuator in Figure 1b provides lower phase errors with a wide operating frequency bandwidth [6]. Because single-pole double-throw (SPDT) switches can be implemented in the resistive attenuation networks separately, an attenuator with low phase error and wide operating bandwidth can be achieved. However, when multiple bits are implemented in the switched path attenuator, it shows relatively high insertion loss, because the accumulated losses of the SPDT switches are high. A large chiparea is necessary in general since the number of the SPDT switches is twice the number of control bits.  
In this paper, an S/C/X 4-bit wideband passive digital step attenuator MMIC using 0.25 \(\upmu\)m GaN HEMT technology is presented for its low insertion loss and RMS phase and amplitude errors from DC to 12 GHz.
## 2 Ground Conditions  
The in-situ tests were carried out in Nanchang Aerospace Industry Town, in the Jiangxi province of China, with multiple thick sandy layers and abundant groundwater. The plant before treatment was flat terrain, including paddy fields, ponds, roads, and creeks. Figure 1 shows the schematic diagram of the area and the soil profiles. As shown in Figure 1, a soft substratum (\(\copyright\)) lies between coarse sand (\(\copyright\)) and medium sand (\(\copyright\) & \(\copyright\)). To obtain a secure and stable foundation, the PHC pipe pile foundation was penetrated through the soft substratum (\(\copyright\)) to the bearing stratum of medium sand (\(\copyright\) & \(\copyright\)) based on the geological exploration. However, it was difficult to penetrate the PHC pipe piles with a length of approximately 21 m down to the bearing layer (\(\copyright\) & \(\copyright\)) because of the existence of dense coarse sand (\(\copyright\)) with a variable thickness of 5-8 m. It is necessary to conduct in-situ tests to find the bearing capacity characteristics of a single PHC pipe pile embedded in the bearing layer at different depths, which provides a reliable basis for subsequent optimization of the design and construction of PHC pipe piles. As shown in Figure 1, the typical area between A-A' and B-B' was selected as the in-situ test area. In this study, the primary purposes were to obtain the vertical UBC of a single pile when the bearing stratums are coarse sand (4) and medium sand (5 & 7), respectively, and to acquire the ultimate skin friction of single PHC pipe piles embedded in medium sand (6 & 7).
# UAV IoT Framework Views and Challenges: Towards Protecting Drones as \"Things\"
## 2 Overview on UAVs as Members of IoT
### 5G and IoT Sensor Technologies for UAVs
#### 2.3.1 Flight Control Sensor for Internal State Evaluation  
Accelerometers are used to determine position and orientation of the drone in flight. One type of technology senses the micromovement of embedded structures in an integrated circuit. Thermal sensing is another technology used in accelerometers, which does not include any moving parts but instead senses changes in the movement of gas molecules passing over a small integrated circuit [2]. Drones and UAVs manage to maintain flight paths and directions using inertial measurement units combined with GPS. The Inertial Measurement Units utilize multiaxis magnetometers (available in one to three axes). A magnetometer is basically a magnetic compass that can measure the magnetic field of Earth. This mechanism helps in determining the direction of  
Figure 1: Unmanned aerial vehicle (UAV)-enhanced 5G-enabled Internet of Things (IoT) services.  
a compass and, consequently, of the drone, which is estimated with respect to the magnetic North. The flight-control system, in order to maintain level flight, obtains input from tilt sensors combined with accelerometers and gyroscopes. This is an essential element for UAVs, especially when the applications require high level of stability (e.g., surveillance, delivery, etc.). In certain drones we have Engine Intake Flow and current sensors [31]. These UAVs are powered with gas engines to effectively monitor the air flow and sensors to estimate the proper fuel-to-air ratio at a specified engine speed aiming to reduce emissions and the overall consumption. Current sensors are available in drones to monitor and optimize power consumption and detect faults with motors or other areas of the system.
## 2 Materials and Methods  
### Experiments  
The experiments were conducted in Agroscope's field station greenhouses in Conthey (Switzerland) on commercial tomato plants (var. Admiro) grown using soilless methods in coconut fiber substrate. Each of the experiments assessed the effect of different common stressors on the plant's electrophysiological response. More precisely, on different sets of tomato plants, three types of stressors were analyzed:  
* _Drought_. Normal irrigation was cut for three days. The drought period was considered to take place nine hours after irrigation removal.
* _Nutrient deficit_. Four different nutrients were considered: manganese (Mn\({}^{2+}\)), iron (Fe\({}^{2+}\)), nitrogen (N), and calcium (Ca\({}^{2+}\)). Deficits of nitrogen and calcium, both of them being macronutrients, often lead to a blossom-end rot [15] of the tomato fruit which consequently reduces the yield. Manganese and iron, as micronutrients, are crucial for photosynthetic machinery and therefore, their deficit triggers a major nutritional disorder that reduces the growth and causes marked losses in both yield and quality [16; 17]. For different sets of tomato plants, each of these nutrients was specifically removed from the full nutrient irrigation, and each experiment was continued for several days after the visual symptoms of the related deficit appeared.
* _Infestation with spider mites_. This represents one of the major pest problems of tomato cultivated in greenhouse since the highly controlled environment of greenhouses favors low-humidity diseases. Plants were infested with spider mites (_T. urticae_) during two weeks as previously described [14], leading to a significant infestations.  
The visual symptoms in the stressed state of the plants after application of each type of stressor are shown in Figure 1. For instance, the drought is manifested by a strong wilt in the plants (Figure 1c), whereas the nutrient deficiency causes the appearance of yellow leaves (Figure 1d). A leaf highly infested with spider mites is shown in Figure 1e.  
Recordings of the electrophysiological signal of each plant were made with multichannel PhytlSigns devices, which use pairs of electrodes: an active one placed in a higher part of the plant stem and a reference (ground) electrode in the lower part [13; 14]. The  
Figure 1: Illustration of the electrode (**a**) and device (**b**) placement during the acquisition of the signal as well as the visual symptoms indicating the stressed state of the plants after application of each type of stressor: (**c**) drought; (**d**) deficit of Ca\({}^{2+}\); (**e**) infestation with spider mites.  
placement of the active electrode and the device during acquisition is shown in Figure 1a,b, respectively. The input impedance of the device is of order of many M\(\Omega\) and the sampling rate was 500 Hz.
## 4 Results
### Results for Research Objective One: Design of Open-Text Items for Assessing \"Selecting dLMs\"
#### 4.2.2 RQ 2.2: What Is the Relationship Between the Designed Items?  
We use Spearman rank correlation to determine whether the scores for content knowledge (items 1-3) and TCK-x, TPK-x, and TPACK reasoning (item 4) support the notion in the literature that higher content knowledge parallels better reasoning [11; 33; 41].  
The results reveal a positive but small correlation between pre-service teachers' content knowledge (items 1-3) and their TCK-x, TPK-x, and TPACK reasoning (item 4) (\(r_{s}\) = 0.17, \(p\) = 0.001).
# Bearing Fault Diagnostics Based on the Square of the Amplitude Gains Method
## 3 Experimental Test Stand  
The proposed damage detection method was experimentally verified on the stand operated at the Bialystok University of Technology (Figure 2 and Table 2). The main element of the test stand is the rotor mounted on two ball bearings (1a-on the brake side and 1b-on the drive side), driven by an electric motor (2) with adjustable speed (up to 2000 rpm). The rotor consists of three parts: the middle part is a replaceable shaft (3), and the outer parts are the drive shaft and the braking shaft supported on the bearings. The shafts are connected into one rotor using conical fits and flanges with bolts ensuring, on the one hand, the axial symmetry of the rotor and, on the other, quick and simple reconfiguration of the shafts. Mass discs (4) are attached to the outer shafts. At the shaft's end, an electromagnetic brake (7) enables the torsional load introduction. The radial positions of the shaft near both bearings are measured by eddy current sensors placed in the horizontal and vertical planes, two at each bearing. Variants of the bearing damage were implemented in the support closer to the drive (1b).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Description** & **Value [Unit]** \\ \hline Bearing type & SKF 1207K EKTN9 \\ Rotational speed & max 2 000 [rpm] \\ Shaft critical speed & 3200 [rpm] \\ Load torque & No external forces \\ Radial force & Sampling time & 60 [s] \\ Sampling rate & 8192 [Hz] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental test stand parameters.  
Figure 2: Measurement test rig: 1—ball bearings (a—on the brake side, b—on the drive side); 2—motor; 3—shaft; 4—mass disc; 5—accelerometers; 6—eddy current sensors; 7—electromagnetic brake; 8—control panel.  
The object of the tests was the SKF 1207K bearing (Figure 3) with the parameters listed in Table 3:  
The bearings were tested in five configurations: undamaged, with cut outer and inner race, with the cut ball at a depth of 1, 2, and 3 mm and width 0.3 mm. The registration of the diagnostic signals was implemented by eddy current sensors, positioned radially to the rotating shaft in the vertical Y and horizontal X axes (two sensors at each bearing).  
The tested bearing is shown in Figure 3a and the types of its damage are presented in Figure 3b-f.
## 5 Conclusions
### Nomenclature  
\begin{tabular}{l l} \(x_{i}^{t}x_{j}^{t}\) & Energy consumption quantity of customer \(i\) at time slot t \\  & Energy consumption quantity of prosumer \(j\) at time slot t \\ \(\omega_{i}^{t}\) & Utility parameter characterizing the electricity consumption of customer \(i\) at time slot t \\ \(R_{\text{customers},i}\) & Revenue of customer \(i\) in distribution network \\ \(P_{\text{retailer}}^{t}\) & Real-time retail price for purchasing electricity in distribution network (DN) at time slot \(t\) \\ \(\alpha_{1},\beta_{1}CS_{j}^{t}\) & Cost coefficients of storage of the prosumers \\  & Cost of storage in prosumer \(j\) \\ \(S_{j}^{t}\) & Charge/discharge capacity of the energy storage system of the prosumer \(j\) at time slot \(t\) \\ \(\alpha_{2},\beta_{2}\) & Cost coefficients of renewable energy resource (RES) of the prosumers \\ \(r_{j}^{t}\) & Feed-in power of RES of prosumer \(j\) to grid at time slot \(t\) \\ \(CR_{j}\) & Cost of RES in prosumer \(i\) \\ \(L_{j}\) & Utility of consumption of prosumer \(j\) \\ \(H_{\text{renewable},j}\) & Income from tariff-in RES to grid \\ \(E_{\text{renewable},j}^{t}\) & Energy amount of RES to grid at time slot \(t\) \\ \(P_{\text{tariff}}\) & Local tariff of RES to gird \\ \(B_{initial}\) & Initial energy in the storage of the prosumer \\ \(R_{\text{prosumer},\ i}\) & Revenue of MG \(i\) in wholesale market \\ \(C_{\text{wholesale}}\) & Purchasing cost from wholesale market \\ \(C_{\text{renewable}}\) a, b & Purchasing cost from RES of the prosumers \\  & Coefficients of the carbon income \\ \(H_{c}\) & Carbon trading income of distribution system operator (DSO) \\ \(R_{DSO}\) & Total revenue of DSO \\ \end{tabular}
# Analyzing GPU Performance in Virtualized Environments: A Case Study
## 4 Proposed Solution
### Data Analysis
#### 4.2.1 Data Model  
As it is designed for offline analysis, our framework requires a robust data model to organize the data collected from the traced kernel subsystems efficiently. An efficient model is indeed essential for generating metrics and graphical representations effectively. Thus, it is necessary to have a thorough understanding of the GPU request's lifecycle--from when a VM process initiates a request to the point where the hardware executes it and returns a response. Such a comprehensive insight is paramount for diagnosing intricate performance anomalies effectively.  
Figure 2 illustrates the various states that a GPU request undergoes throughout its lifecycle in the system. Initially, a process within a GPU-accelerated VM generates a request for GPGPU or graphics processing operations. This request is first received by the VM's GPU driver, which places it in a waiting queue (1). As GPU requests often depend on others, these dependencies must be resolved before the request attains the \"Submitted\" state (2). Once all preceding requests in the queue have been executed, the guest OS's GPU driver removes the request from its waiting queue and forwards it to the vGPU for execution (3). Subsequently, GVT-g intercepts the request and queues it in the vGPU's waiting queue associated with the VM (4). When the vGPU's scheduler allocates time for this vGPU, the request is passed to the host GPU driver (5), which then places it in another waiting queue (6). The request awaits its turn for execution, pending the resolution of its dependencies (7) and completing all prior queued requests. Once these conditions are met, the host GPU driver dispatches the request to the hardware for execution (8). Upon completing the request, GVT-g receives a notification (9) and informs the guest OS via a virtual interrupt.  
Modeling the collected performance data is mandatory for computing relevant performance metrics, such as the average wait and execution times of requests per vGPU. To this end, we used Trace Compass [15], a powerful trace analyzer that integrates several interesting pre-built performance analyses. These analyses save the data representing the states of the system to be monitored in a disk-based data structure called a state history tree (SHT) [42]. We store the states of our system in an attribute-tree data structure (Figure 3). This data structure exhibits sets of hierarchical attributes, which are updated each time one of the events described in Section 4.1 is handled. Thus, according to our model, one or many vGPUs can be attached to one pGPU. Both a pGPU and a vGPU each have their  
Figure 2: States of a GPU request along with the corresponding tracing events.  
own driver waiting queue. A GPU request is identified by its sequence number (_seqno_), its context (_ctx_), and the engine to which it should be sent for execution (_ring_).
# Assessment and Spatial Modelling of Agrochernozem Properties for Reclamation Measurements
## 2 Materials and Methods
### Random Forest  
We applied the RF algorithm for the spatial prediction of soil properties because it is the most popular in digital soil mapping studies [43]. Moreover, this method is superior to other machine learning methods, as shown in a variety of studies [28; 44; 45; 46; 47]. RF is an  
Figure 1: Location of the study site and distribution of samples (image source: Sentinel-2A—natural color composite).  
ensemble learning method for classification and regression tasks. In a model, each decision tree in the forest is built using a random subset of the input data and a random subset of the input features. Then, the results of all individual trees are aggregated to make a single prediction. We defined the default parameters required by the RF model: the number of trees to be built in the forest (ntree) and the number of predictors to be used in each tree-building process (mtry). We set the parameters as follows: \(\text{ntree}=500\) and \(\text{mtry}=4\).  
The recursive feature elimination (RFE) method was applied to select an optimal set of environmental auxiliary variables among all covariates [48]. The algorithm works by backward selection, where the least promising predictors are excluded from the model based on an initial predictor importance measure.
## 3 The Proposed GEER-DLAD Model  
The overall system architecture of the presented GEER-DLAD model is depicted in Figure 2. As shown in figure, the IoT devices continuously gather the data. Then, they are compressed by the use of ELC technique. Subsequently, the IoT devices executed the routing technique using the MSO algorithm and select the optimal route to the destination. Upon the election of routes, the compressed data will be forwarded towards the time. At the time of data transmission, the DLAD technique is applied to detect the occurrence of anomalies in IoT communication networks.
# Image Dehazing and Enhancement Using Principal Component Analysis and Modified Haze Features
## 4 Experimental Results
### Application to Low-Light Image Enhancement
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{[26]} & \multicolumn{2}{c}{[27]} & \multicolumn{2}{c}{[25]} & \multicolumn{2}{c}{**Proposed**} \\ \cline{2-10}  & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** \\ \hline Figure 13a & 14.9340 & 0.6473 & 15.2250 & 0.6990 & 13.7155 & 0.7044 & 17.9683 & 0.8122 \\ Figure 13b & 8.9670 & 0.1310 & 12.7210 & 0.5678 & 15.0965 & 0.8063 & 18.1877 & 0.8205 \\ Figure 13c & 14.2907 & 0.4049 & 15.4260 & 0.5586 & 12.9453 & 0.6143 & 18.1130 & 0.5971 \\ Figure 13d & 13.7084 & 0.2131 & 15.7105 & 0.5963 & 14.2142 & 0.6855 & 16.7344 & 0.6968 \\ Figure 13e & 12.8507 & 0.6044 & 15.6484 & 0.6960 & 14.0895 & 0.6400 & 17.5864 & 0.6429 \\ Figure 13f & 14.4912 & 0.3775 & 14.8379 & 0.5650 & 12.6677 & 0.6375 & 19.3865 & 0.7534 \\ Figure 13g & 20.9754 & 0.8128 & 19.0706 & 0.7980 & 14.4191 & 0.7019 & 16.8107 & 0.7687 \\ Figure 13h & 15.6074 & 0.5902 & 15.8949 & 0.6559 & 12.0559 & 0.6022 & 14.5077 & 0.6213 \\ Figure 13i & 15.0136 & 0.3879 & 17.0851 & 0.5924 & 13.9160 & 0.6787 & 17.8701 & 0.7628 \\ Figure 13j & 15.5462 & 0.5564 & 16.7592 & 0.6522 & 12.7164 & 0.5974 & 18.2142 & 0.7238 \\ \hline
**Avg.** & 14.6384 & 0.4725 & 15.8379 & 0.6381 & 13.8536 & 0.6668 & 17.5379 & 0.7200 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The objective assessments of the low-light image enhancement using the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) [28]. Figure 14: Resulting enhanced images using existing and proposed methods taking synthesized low-light images as the input: (**a**) ideal image [29], (**b**) synthesized low-light image, (**c**) histogram equalization (HE) [26], (**d**) [PERSON]’s method [27], (**e**) [PERSON]’s method [25], and (**f**) the proposed method.
## 4 Results  
The automated target detection approach described in Section 3 with a water occurrence threshold of 50% identified 4535 water bodies with surface areas of 0.1 to 1291.04 km\({}^{2}\) and a total sum of 29,100.72 km\({}^{2}\). The mean area is 6.4 km\({}^{2}\). In total, 2061 lakes/reservoirs were found that are larger than 1 km\({}^{2}\), 429 that are larger than 10 km\({}^{2}\) and 45 that are larger than 100 km\({}^{2}\). While the number of detected water bodies was very close to the number given in GLWD (Level 1 and Level 2), which is 4647, the total area is underestimated by almost 4600 km\({}^{2}\) (the area from GLWD is 33,688 km\({}^{2}\)). This changes with the use of different water occurrence incidences (see Section 3.2 for details). The number of water bodies, as well as their surface area, increases if no limitation of water occurrence is used (>5%). In this case, 5708 lakes larger than 0.1 km\({}^{2}\) are identified with a total surface area of 35,397.54 km\({}^{2}\). Figure 3 shows the numbers for different thresholds of water occurrence probabilities. The blue line represents the results for all water bodies larger than 0.1 km\({}^{2}\). For the red and orange lines, larger lake size limits of 1 and 10 km\({}^{2}\) are applied. The number and size of detected lakes larger than 10 km\({}^{2}\) for higher water occurrences correspond well with those from GLWD Level 1 and WGHM.
# Experimental Study on Mechanical Behavior of Shear Connectors of Square Concrete Filled Steel Tube
###### Abstract  
In order to quantitatively evaluate the shear-bearing capacity of shear connectors of square concrete filled steel tube (CFST), push-out tests on 14 square CFSTs with shear connectors have been carried out. Among the 14 CFSTs, there are 13 specimens with steel plate connectors and one specimen with steel bar connectors. The following factors are investigated to figure out their influences on the performance of CFSTs, which are the width to thickness ratio of steel tube, thickness of steel plate, length of steel plate, strength of concrete, welding condition of steel plate, number of steel plate layer and interlayer spacing. The test results show that the ultimate bearing capacity and the elastic stiffness increase with decreasing width to thickness ratio of the steel tube, and increasing thickness and length of the steel plate. With increasing concrete strength, the ultimate bearing capacity also increases. However, the welding condition has no effect on the ultimate bearing capacity. The ultimate bearing capacity of the CFST with double-layer steel plate is greater than that with single-layer steel plate. The ultimate bearing capacity of steel bar type shear connector is 87% greater than that of the steel plate type shear connector, and the steel bar specimen shows good ductility. A formula for calculating the shear-bearing capacity of shear connectors has been developed, and the calculated shear-bearing capacities are in good agreement with the test data.  
Keywords:square CFSTs; shear connector; push-out test; shear-bearing capacity +
Footnote †: journal: Applied Sciences