esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
The different chopper frequency and DEM frequency were used, and this may introduce performance degradation due to the IDM between the chopper and DEM frequencies. In Figure 14d, the same frequency for the chopper and the DEM is used, thus IMD between the chopper and DEM can be minimized. Table 2 summarizes the performances of the proposed potentiostat readout circuit with those of previous studies [4; 16; 17; 22; 23; 24]. The simulation results of this circuit achieve the wide input range, low noise, and mismatch-tolerant characteristics. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Transistor**} & \multirow{2}{*}{**Noise Source**} & **Total Noise** & **Total Noise** \\  & & **Chopper Disable (\%)** & **Chopper Enable (\%)** \\ \hline \(M_{1,2}\) (current mirror) & Flicker & 71.52 & 12.44 \\ \hline \(M_{1,2}\) (current mirror) & Thermal & 8.03 & 31.02 \\ \hline \(PM_{2,3}\) (TIA input pair) & Flicker & 4.06 & 1.40 \\ \hline \(NM_{2,3}\) (TIA input pair) & Flicker & 3.62 & 1.38 \\ \hline \(PM_{4,5}\) (TIA cascode load) & Flicker & 3.53 & 0.18 \\ \hline \(NM_{4,5}\) (TIA cascode load) & Flicker & 2.8 & 0.10 \\ \hline \(NM_{6,7}\) (TIA cascode) & Flicker & 0.18 & 2.78 \\ \hline \end{tabular}
\end{table} 
Table 1: Simulated noise contributors. Figure 14: Mismatch simulation results when the chopper and DEM were (**a**) disabled; used different frequency (**b**) \(f_{\text{ch}}\) = 125 kHz, DEM clock = 100 kHz; (**c**) \(f_{\text{ch}}\) = 125 kHz, DEM clock = 150 kHz; used same frequency (**d**) \(f_{\text{ch}}\) = DEM clock = 125 kHz.
# Data Augmentation Method Using Room Transfer Function for Monitoring of Domestic Activities
## 4 Simulation
### Evaluation Results  
We used the micro, macro, and weighted averages of the F1-Score as metrics for the performance comparison. Following the cross-validation setup of the SINS dataset [19], the models were trained for four folds. For the evaluation, the average of the probability outputs from these four models was used. We evaluated the performance using the test dataset from the development dataset and an evaluation dataset, which included data collected with an unknown microphone not present in the development dataset. This allowed us to compare the results for known and unknown microphone positions, each with different RTFs. For the experiment to determine the correct parameters for our proposed method, we evaluated the performance using the evaluation dataset only, dividing it into known and unknown microphone data. First, we compared the proposed method with existing data augmentation techniques, then examined whether the performance gap between the known and unknown microphones decreased, as per our proposal. Next, we compared the performance changes based on the parameters of the proposed method. Finally, we compared the performance of our proposed method with a technique that adds Gaussian noise, which is similar to our method and easily applicable.
# Compression-Assisted Adaptive ECC and RAID Scattering for NAND Flash Storage Devices
## 3 Compression-Assisted Adaptive ECC and RAID Scattering
### Metadata Structure and Parity Management
The updated metadata information, that is, the logical addresses for the written data in this stripe group, is just maintained as the StripeMapLogInfo structure information in memory until all pages of the stripe group are used for writes. Once it is filled with a certain amount of metadata updates, the stripeMapLogInfo is flushed to the last available page in the StripeMapLog block. When the StripeMapLog block is full of loaded pages, thus, no longer available for logging, the next available block area is needed. Before allocating a new StripeMapLog block, metadata information in the main memory is synchronized to the flash to be the latest snapshot, and then, a new StripeMapLog block is allocated. After this process, the logging mechanism described above is repeated. In addition to the metadata logging, full stripe parity update operation is designed and implemented. In our system, parity for the stripe is retained in a buffer memory until full stripe write is done. The full  
Figure 5: Software metadata structure for RAID and parity management. stripe write means that all of the data for the corresponding stripe is written. Since parity is updated in memory buffer with the incoming data of the stripe, it does not need to read old parity and old data to update parity. The parity is just written when a full stripe write is completed. Since partial parity is maintained in memory, it can be lost with power failure. The metadata log information is used during the recovery process of the partial parity from power failure. When power loss occurs during the parity is in partial state retained in buffer, that partial parity can be recovered by scanning the logged information.
# Influence of Synthesis Parameters in Obtaining KIT-6 Mesoporous Material
## 2 Materials and Methods  
### Synthesis of Standard KIT-6  
The standard KIT-6 sample was obtained following the typical method described by [PERSON] et al. [9], using the following molar ratio: 1.000 TEOS; 0.017 P123; 1.83 HCl; 195 H\({}_{2}\)O; 1.31 butanol. For 0.05 kg of synthesis gel, 1.23 g of triblock copolymer pluronic (P123, EO\({}_{20}\)-PO\({}_{70}\)-EO\({}_{20}\), Sigma Aldrich, St. Louis, MI, USA) was dissolved in 2.25 g of hydrochloric acid (37% Vetec) and 44 g of distilled water, this solution was subjected to vigorous stirring at 308 K for 6 h. After complete dissolution, 1.21 g butanol (99.4% Vetec) was added. The mixture remained under stirring at 308 K for 1 h. Thereafter, the tetraethylorthosilicate (TEOS, 98% Sigma Aldrich, St. Louis, MI, USA) was added to the homogeneous solution. Then, this mixture was kept under stirring at 308 K for 24 h, and in sequence was transferred to an autoclave and heated at 373 K for 24 h under static conditions. The solid product finally obtained was separated by vacuum filtration, washed with 2% HCl/ethanol solution, and dried overnight at room temperature. Subsequently, the material was calcined at 823 K for 6 h in synthetic air atmosphere to remove the organic template. The sample obtained through the standard synthesis was coded as KIT-6.
# Radiator Enablers for Wireless Communication Evolution
## 2 Challenges, Enabling Technologies, and Antenna Developments
### Challenges and Enabling Antenna Technologies
#### 2.1.1 The eMBB Challenge
In the stated analysis, it is understandable that an increase in frequency bandwidth calls for the need for previously unlicensed/unused and higher-frequency bands to be utilized. In turn, increased bandwidth calls for broadband rf, microwave and (sub) THz sources, beamforming networks, and antennas. Thus, given the previous understanding, we deduce that enabling antenna technologies should facilitate wideband antenna systems to harvest large-frequency bands and focus on radiator arrays with an increased number of antenna ports.
# Energy-Efficient Amplifiers Based on Quasi-Floating Gate Techniques
## 3 Energy-Efficient Amplifiers Based on QFG Techniques
### Multistage Class AB/AB Amplifiers  
A drawback of class A/AB amplifiers is that the input stage can limit slew rate if it is not able to drive the compensation capacitor fast enough. To solve this drawback, class AB operation can also be included at the input stage, leading to class AB/AB topologies. Any of the class AB circuits of Figure 6 can be used at the first stage to this aim. A slightly different approach is shown in Figure 7b [28], where a scaled replica of the current in the output transistor M\({}_{5}\) is fed back to the differential pair. Another example of class AB/AB QFG implementation can be found in [29].
# Detection of Marine Oil Spill from PlanetScope Images Using CNN and Transformer Models
## 3 Method
### Model Optimization  
The DeepLabV3+, Swin-UPerNet, and Mask2 Former models were trained on the cross-validation datasets, resulting in the creation of five models for each approach. All models were configured with commonly used backbone networks, loss functions, and optimizers. The DeepLabV3+ model employed ResNet-50 and ResNet-101 as backbones, used the Binary Cross Entropy (BCE) loss function, and was optimized with the Adaptive Moment Estimation (Adam) optimizer. In the Swin-UPerNet architecture, the Swin Trans  
Figure 4: Examples of image data augmentation using the Albumentations library. The example images include random 90-degree rotation, horizontal flip, vertical flip, optical distortion, grid distortion, RGB shift, and random brightness/contrast adjustment.  
former serves as the backbone, available in small (S), base (B), and large (L) versions, and is combined with the UPerNet model, using the BCE loss function and the Adam optimizer with weight decay (AdamW). Similarly, Mask2 Former uses the Swin Transformer as its backbone, along with the BCE loss function and the AdamW optimizer. The remaining hyperparameters were initially configured using a grid search technique and then consistently applied across the remaining four folds. The batch size was set to 8 for DeepLabV3+ and Swin-UPerNet and 4 for Mask2 Former. The training ran for 150 epochs for DeepLabV3+ and 100 epochs each for Swin-UPerNet and Mask2 Former. The learning rates were set at 6 \(\times\) 10\({}^{-7}\) for DeepLabV3+, 6 \(\times\) 10\({}^{-5}\) for Swin-UPerNet, and 1 \(\times\) 10\({}^{-5}\) for Mask2 Former. To effectively handle unusual cases, the dropout technique was employed in both models, with a dropout ratio of 0.3. Table 3 illustrates the hyperparameter settings.
# Evaluation of Multi-Sensor Fusion Methods for Ultrasonic Indoor Positioning
## 2 Global System Overview
### General Description of LOCATE-US  
LOCATE-US ULPS is a compact, light, and portable ultrasonic beacon architecture. A single ULPS is formed by five emitter beacons (\(B_{i}\), where \(I\) = 1,2,... 5) [26], placed at the centre and at the four corners to form a square with a side of \(1/\surd\)2 m, as can be observed in Figure 2. The five beacons have been placed in different planes: \(B_{2}\) and \(B_{4}\) are in the base plane, \(B_{3}\) and \(B_{5}\) are moved 10 cm apart, and \(B_{1}\) is moved 20 cm from the base plane [17]. When the ULPS is placed on the ceiling at a height \(H\) of 3.5 m emitting top-down, it approximately covers an area of 40 m\({}^{2}\) and a volume of 53 m\({}^{3}\), because the total aperture angle of each emitter is 120\({}^{\circ}\). To avoid audible artefacts, the ULPS operates at roughly 41 kHz [26]. Some ULPSs can be easily deployed to cover wide indoor spaces, where the receiver to be positioned is moving around. Each emitter of the ULPS may use two protocols, code division multiple access (CDMA) and time division multiple access (TDMA), to generate the corresponding emissions, encoded with a different code. These codes present suitable auto-correlation features and low mutual interference with the others. The emitters for a single ULPS can be controlled to configure the ultrasonic transmission in terms of modulation schemes, sampling frequency, and code patterns to be transmitted. Upon reception, a non-limited number of receivers can compute and estimate their own position by measuring TDOAs from the incoming ultrasonic signals inside the coverage volume in an independent and autonomous way. That is why it is not necessary to synchronize the beacons and the receivers [18; 27].  
It is worth mentioning that, hereinafter, every transducer from the existing ULPSs transmits its own 1023-bit Kasami code so that it can be identified upon reception by matched filtering. These codes are BPSK-modulated at a carrier frequency of 41.67 kHz to adjust the emissions to the available bandwidth.
## 4 Experimental Studies of Microstructural Defect Detection Using Multimode Ultrasonic Signals  
### Experimental Setup and 1 st Test  
In this section, the proposed multi-mode nonlinear guided wave excitation and detection method is experimentally demonstrated on the beam that was modeled and numerically simulated in the previous section. The beam assembly sample under test is fabricated with induced micro-cracks as described in Figure 1 and Section 3. Micro-racks are concentrated near T-joints.  
Figure 6 shows the first experimental setup for the measurements. The beam assembly used for the first test has the cross-sectional dimensions shown in Figure 2. The width of the beam is 0.3 m. For this experiment, three half-inch diameter broadband piezoelectric contact transducers (V103, \(\mathrm{Panametrics}^{\mathrm{TM}}\)) are attached, as shown in Figure 2. A function generator within an oscilloscope (Handyscope HS5, \(\mathrm{TiePie}\)) excites a compressional wave signal for 0.5 ms using the Transmitter transducer placed in the middle. The compressional wave signal is a 20-110 kHz linear chirp with a varying peak-to-peak voltage of 0.75, 1.5, 3.0, 6, and 12 V, respectively.  
Figure 4: Spectrograms of difference signal \(y(t)\) between a baseline with 0.15 \(\mathrm{\SIUnitSymbolMicro m}\) input amplitude and monitoring signals with different input amplitudes: (a) 0.3 \(\mathrm{\SIUnitSymbolMicro m}\), (b) 0.6 \(\mathrm{\SIUnitSymbolMicro m}\), and (c) 1.2 \(\mathrm{\SIUnitSymbolMicro m}\) input amplitudes.  
Figure 5: Simulation results of actionable output (\(AO\)) vs. different \(\beta\) through FEM simulations where \(\beta<\beta_{1}<\beta_{2}\).  
The other two transducers are used to receive the out-of-plane displacement of the transmitted ultrasonic signals that has traveled through welds at T-joints. Such displacement signals are recorded for 0.5 ms. The received ultrasonic signal is then amplified by a low noise preamplifier (SR560, Stanford Research Systems).  
As summarized in Section 2, the base measurement is conducted for both measurement zones containing the T-joints, and its phase inverted signal is consecutively measured. Then, steps (3) through (6) are performed to estimate \(AO\) for both sides. It should be noted that the beam has concentrated micro-cracks near the welds at T-joints with a higher density of micro-cracks at T-joint #2, on the right-hand side of the beam as shown in Figure 6.
## 3 Methodology  
In the last section, we briefly review the current imaging framework for HRWS imaging. In this section, the methodology of the proposed target-oriented imaging concept and its realization framework are presented in detail.  
From the current imaging framework in Figure 3, two core modules of receiving and processing form it. While it is a general framework for all kinds of imaging scenes, it may not be the most suitable one for applications such as maritime surveillance, especially for online surveillance on a resource-limited platform. We take an imaging result of a typical maritime scene, for example, as shown in Figure 4. It is from the LS-SSDD-v1. (Large-Scale SAR Ship Detection Dataset-v1.0, LS-SSDD-v1) dataset we released before [25].  
From the figure, we can clearly see that most of the scene is water surface, and the interested targets of ships only occupy a relatively small part, revealing the strong sparsity of the scene. However, as shown in Section 2, echoes of the whole scene need to be received  
Figure 4: Typical scene for maritime surveillance.  
Figure 3: Current imaging framework for HRWS imaging.  
and processed. Then, for further surveillance, ship target detection is performed on the entire imaging result. As we explain in Section 1, this framework increases the burden of receiving resources and computation time, which is not favorable for online maritime surveillance.  
With this observation, utilizing the strong sparsity of the scene, we propose a new target-oriented imaging concept and its realization framework, as shown in Figure 5.  
To serve the need for online maritime surveillance, one key issue is the appropriate management of the resource budget. The first principle is to minimize unnecessary costs. Therefore, compared with the general imaging concept that is whole-scene-oriented, we propose this target-oriented one that guides the resource allocation to the targets and avoids the unnecessary resource allocation to the pure-water area. The second principle is to maximize necessary allocation. Therefore, compared with the general imaging framework with just two main modules (receiving and processing) as shown in Figure 3, we propose an adaptive feedback framework with an additional decision module and the feedback connection to dynamically allocate the resource.  
As seen in Figure 5, the proposed framework consists of three basic modules, namely receiving, processing, and decision modules. They are connected in a closed-loop way to enable feedback. This idea is inspired by biological behavior, where animals perceive the surrounding environment and then adjust their behavior according to its information. Thus, from the perspective of perception, the first two modules collect and process to obtain information from the imaging scene, respectively. From the perspective of action, the last module and the feedback connection process the obtained information and then decide the system's following response. Their details are presented in the following three subsections.
## 1 Introduction
During each data gathering epoch, we evaluate the current network sensed data at the sink node and adjust  
Figure 1: The sparsity varies in OTD datasets. (**a**) The Pacific Ocean temperature observed on 29 March 2014 (OTD A); (**b**) The corresponding coefficients of OTD A after six-layer Daubechies2 wavelet transformation; (**c**) The Pacific Ocean temperature observed on 2 April 2014 (OTD B); (**d**) The corresponding coefficients of OTD B after six-layer Daubechies2 wavelet transformation. the measurement-formation process according to this evaluation. By doing so, it forms a kind of feedback-control process, and the required number of measurements is tuned adaptively according to the real-time variation of data to be gathered. From another point of view, despite CDG's ability to reduce the global communication cost, multiple studies show that the effectiveness of CDG is still affected by the strategy of the measurements' formation process [9; 12; 14]. Several optimization schemes, such as Hybrid-CDG [9], are proposed to form all of the measurement results through a single routing tree in each data gathering epoch. To further reduce the energy consumption of such process, different from Hybrid-CDG, we supply a measurement-formation algorithm in this paper, where each measurement-formation path is treated individually in a data gathering epoch. Similar idea was also adopted by PB-CDG [14], yet the novelty of our approach lies in the path-generation procedure and the underlying method of measurement coefficients' distribution, which omits the massive coordination among sensor nodes in the network. The remainder of this paper is organized as follows: in Section 2 we first give a comprehensive overview on the typical CDG scheme mentioned above. Then, we propose our explicit motivation and main resolve method in Section 3. Next, we illustrate our data gathering approach in detail in Sections 4 and 5. Simulation and practical experiment results are presented in Section 6. At last, we address the conclusions in Section 7.
## 2 Thematic Review and Analysis
### Control Strategies  
This section examines various control mechanisms employed in DH systems, emphasizing network operations. Both physical and operational control measures are crucial for maintaining low return temperatures in DH systems. The chosen control strategy affects the design, functionality, and overall performance of the system [28; 56].  
[PERSON] [28] examined various control techniques focusing on indoor temperature maintenance, substation types (direct and indirect), and substation control (details given in Table 1). The study pertains to a DH system in Balcova, Izmir, Turkiye, with a supply temperature of 85 \({}^{\circ}\)C, a degree hour of 39,470 \({}^{\circ}\)C-h, and an overall peak heat demand of 73 MWh under the design boundary conditions of 20/0 \({}^{\circ}\)C (indoor/outdoor temperatures). The results, detailed in Figure 7, show that indirect substation configurations (Cases C and D) lead to higher return temperatures and geothermal fluid consumption--approximately 8 \({}^{\circ}\)C and 94 m\({}^{3}\)/h more during high heat load periods compared to direct substation configurations (Cases A and B). Furthermore, variable-flow control (Cases A and C) is shown to be more efficient than constant-flow control (Cases B and D) during low-load periods.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Cases**} & \multirow{2}{*}{**Substation Type**} & \multirow{2}{*}{**Control Type**} & \multicolumn{3}{c}{**Control**} \\ \cline{3-6}  & & & **Control at Indoor Heating System** & **Control at Substation** & **Radiator Inlet Temperature** \\ \hline Case A & Direct & Variable Flow & Thermostatic & - & Same as the DH network (85 \({}^{\circ}\)C) \\ Case B & Direct & Constant Flow & - & - & - \\ Case C & Indirect & Variable Flow & Thermostatic & - & Fixed at 80 \({}^{\circ}\)C \\ Case C & Indirect & Variable Flow & Radiator Valve & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Control strategies as appointed for the cases studied in [28].  
The following sub-sections address control tactics in DH systems, focusing on flow control strategies, supply temperature considerations, and low-flow operation. Multiple control strategies can be assessed concurrently in practice. [PERSON] [29] identifies three heat output control strategies: (i) flow regulation with a constant DH supply temperature, (ii) supply temperature adjusted via the weather compensation curve with a steady flow, and (iii) simultaneous flow regulation with supply temperature based on the weather compensation curve.
# Automatic Matching of Multi-Source Satellite Images: A Case Study on ZY-1-02C and ETM+
## 3 Experiments and Results
### Accuracy Analysis of Matching between ZY-1-02C Images and ETM+ Images
#### 3.3.2 Results of Image Matching  
After resampling, the size of the overlap area in the images of Datasets 2 and 3 was approximately 10,741 by 6202 pixels. Each image block (1-16) detected a pair of translation parameters. The data was then matched through NCC. The grid size used in our approach to extract the Harris feature points was 100 \(\times\) 100 pixels, the template window size was 13 \(\times\) 13 pixels, the search window size was 55 \(\times\) 55 pixels, and the threshold of the correlation coefficient was 0.9. Figure 6 shows some of the matched conjugate points.