esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Investigation in Drag Coefficient of Cubes
## 2 Experimental Facility and Procedure
 & **Initial Released Angle** \\ \hline
1 & 16.10 & 16.26 & 16.08 & & & aluminum & 1912.33 & \\
2 & 16.08 & 15.98 & 15.90 & 0.5 & & brass & 4624 & 0\({}^{\circ}\)\(\pm\) 1\({}^{\circ}\) \\
3 & 16.08 & 16.28 & 16.10 & & & steel & 4394.15 & \\
4 & 16.24 & 16.24 & 16.20 & & & aluminum & 1894.18 & \\
5 & 16.04 & 16.26 & 15.92 & 0.67 & PVC & brass & 4543.91 & 20\({}^{\circ}\)\(\pm\) 1\({}^{\circ}\) \\
6 & 16.10 & 16.34 & 16.20 & & & steel & 4370.69 & \\
7 & 16.40 & 16.38 & 16.20 & & & aluminum & 1877.60 & \\
8 & 16.30 & 16.36 & 16.00 & 1.0 & & brass & 4534.20 & 45\({}^{\circ}\)\(\pm\) 1\({}^{\circ}\) \\
9 & 16.50 & 16.60 & 16.30 & & & steel & 4199.51 & \\
10 & 19.92 & 19.96 & 20.10 & & & aluminum & 1887.18 & \\
11 & 20.00 & 19.96 & 20.10 & 0.5 & & brass & 4684.99 & 0\({}^{\circ}\)\(\pm\) 1\({}^{\circ}\) \\
12 & 20.10 & 19.92 & 20.30 & & & steel & 4453.89 & \\
13 & 19.96 & 20.40 & 19.9 Equivalent sphere diameter (\(d_{n}\)), particle Reynolds number (\(Re_{n}\)) are calculated by using Equations (2) and (3), respectively. \[d_{n}=\left(\frac{6V_{p}}{\pi}\right)^{\frac{1}{3}}, \tag{2}\]  
\[Re=\frac{\rho_{f}v_{t}d_{n}}{\mu}, \tag{3}\]  
where \(V_{p}\) is the volume of particle, \(d_{n}\) is the volume-equivalent sphere diameter. \(v_{t}\) is the terminal velocity  
When the particle reaches its terminal velocity, the gravity, buoyancy, and drag are balanced, and the drag can be expressed as follows:  
\[F_{D}=\frac{\pi}{6}{d_{n}}^{3}g(\rho_{P}-\rho_{f}). \tag{4}\]  
In addition, the drag can also be expressed by using Equation (5):  
\[F_{D}=\frac{\pi}{8}{d_{n}}^{2}C_{D}\rho_{f}{v_{t}}^{2}. \tag{5}\]  
Through Equations (4) and (5), the drag coefficient can be obtained by the following expression:  
\[C_{D}=\frac{4}{3}\frac{gd_{n}}{v_{t}}^{2}(\frac{\rho_{p}-\rho_{f}}{\rho_{f}}), \tag{6}\]  
where \(\rho_{p}\) is the density of particle, \(\rho_{f}\) is the density of experimental liquids, and \(v_{t}\) is the terminal velocity.
## 3 Results  
### Question 1: What Are IEQ Models?  
An IEQ model is an evaluation tool used to assess the environmental quality of an indoor space by considering all the aspects of IEQ [9]. It combines the values of several indicators corresponding to different IEQ aspects into an overall index, \(\theta_{IEQ}\), which can be a serial number (such as a score from 0 to 100) [10] or a binary number (to indicate accepted/not accepted) [11]. The goal is to provide quantitative insights for the use of building owners, designers, and managers to evaluate IEQ and to indicate both the weakest IEQ aspect and the most important aspect, in order to optimize overall IEQ satisfaction.  
Figure 1 illustrates the search result for the number of publications using the keywords \"(indoor environmental quality OR IEQ) AND (model\" OR weight\" OR index*)\" in Web of Science. As is shown, there is growing attention to the assessment of IEQ; the peak occurred in the past four years. According to the high-frequency items used in the abstracts of these publications (Figure 2), four research trends can be identified: (i) IAQ, thermal comfort, visual quality, and acoustic quality were the most commonly studied IEQ aspects, while IAQ and thermal comfort were mentioned more frequently than auditory and visual comfort; (ii) schools (or classrooms) and offices were the most popular of the investigated environments; (iii) occupants' comfort, productivity, performance, satisfaction, and health were the focus of the research; (iv) field studies and surveys were the two most commonly used research methods.  
Figure 1: The number of publications on indoor environmental quality assessments.  
A typical IEQ model consists of the individual IEQ indexes (\(\theta_{i}\)) and their corresponding weights (\(\beta_{i}\)):  
\[\theta_{IEQ}=\sum\beta_{i}\theta_{i}. \tag{1}\]  
It is worth noting that Equation (1) is just a single example of the IEQ models. There are diverse model forms that mainly vary according to the analysis methods employed. The individual IEQ indexes of the IEQ model are calculated using separate models that aim to predict the performance of each IEQ aspect (e.g., [13; 14; 15]). At the same time, the weighting schemes are determined through different analytical methods [16; 17]. Combining the individual IEQ indexes and the weighting schemes could help to identify the most crucial aspect that contributes to overall IEQ satisfaction, along with the weakest aspect that needs urgent improvement. Figure 3 shows the necessary procedures for developing an IEQ model, including the selection of investigated subjects (namely, the measurement parameters and the surveyed occupants), data collection, data analysis, and the interpretation of the results. Each step of this process is explained in the following sections.
# Wideband E\({}_{00}\)-E\({}_{10}\) Silicon Mode Converter Based on 180 nm CMOS Technology
## 4 Conclusions  
In this work, a wideband silicon mode converter based on E\({}_{00}\)-E\({}_{10}\) is theoretically proposed and experimentally demonstrated. With FD-BPM calculations, the simulated mode crosstalk is less than \(-29.2\) dB in 1540-1600 nm. The standard 180 nm CMOS technology is applied in the fabrication of designed device, instead of the E-beam lithography. The best mode conversion, with an efficiency of 91.5%, is observed at 1575.9 nm. The comparatively wide 3 dB bandwidth of over 60 nm offers the proposed great potential of on-chip multiplexing systems in future.  
Conceptualization and resources, D.Z.; formal analysis and investigation, Y.X.; validation, Y.G.; data curation, S.L. and T.L.; funding acquisition, B.T.; writing--review and editing, X.S.; project administration, P.Z.; All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Key Research and Development Program of China (2019 YFB2203003).  
Not applicable.  
The authors declare no conflict of interest.
# Mapping a Guided Image Filter on the HARP Reconfigurable Architecture Using OpenCL
## 2 FPGAs and the HARP Platform
### FPGA Cache  
The FIU core not only contains the CCI-P, it also provides an IP cache in the FPGA, for the QPI channel, see Figure 1. This cache has a total capacity of 64 KiB, and is direct-mapped with 64 B cache lines. As the FPGA cache is included in the cache coherence domain of the CPU, the data in the FPGA cache is coherent with the CPU cache and the DDR memory.  
Because of the expanded cache coherence domain, AFU memory requests can be serviced at three different memory levels. The lowest latency and highest bandwidth is obtained when reading from the FPGA cache itself. In this case, no read request to the CPU is required. If the FPGA cache does not contain the requested cache line, the next memory level to be addressed is the CPU Last Level Cache (LLC). This results in a higher latency and lower bandwidth than when reading from the FPGA cache. The highest latency and lowest bandwidth is obtained when both the FPGA cache and the CPU LLC read requests result in a miss. In this case, the read request is serviced by the DRAM. The FIU supports two programming languages: Hardware Description Language (HDL) and OpenCL. The OpenCL code is compiled into HDL by the Intel OpenCL compiler and the resulting HDL code is synthesized into a bitstream using Intel Quartus.  
Figure 1: HARPv2 heterogeneous architecture, consisting of an Intel Xeon CPU and Arria 10 FPGA with QPI and PCIe interconnects. The QPI and PCIe channels are respectively 16 and 8 bit wide.
## 1 Introduction  
The laser wakefield acceleration (LWFA) of high-quality electron bunches is of high interest for several applications, such as the generation of secondary radiation, material science, imaging, or radiotherapy [1, 2, 3, 4]. Very High Energy Electrons (VHEE) with energy >150 MeV and sufficient charge are required for the dose rate of the FLASH radiotherapy [5, 6]. Emerging kHz-class lasers offer an attractive opportunity to develop reliable and stable VHEE sources; however, the pulse energy of kHz-class lasers is limited to approximately one hundred mJ [7]. One of the ways to increase the energy of accelerated electrons is to implement the focusing of the laser beam using new optic called an axiparabola (AXP), as reported by different research groups [8, 9, 10]. The AXP forms a Bessel-like laser beam with a prolonged focal area, extending the electron acceleration distance. In addition, applying spatio-temporal coupling (STC) could compensate for the dephasing of accelerating plasma waves [11]. In a recent paper, the LWFA simulation using an AXP to focus the laser pulse with the energy of 6.2 Joules demonstrated the feasibility of accelerating electrons over 20 dephasing lengths (1.3 cm) to a maximum energy of 2.1 GeV [12]. To ensure the required amount of accelerated charge, better control of ionisation injection, and the low energy dispersion of the VHEE source, a double nozzle with separate sections comprising a mixture of He and N\({}_{2}\) gases for injection and pure He for acceleration was proposed [13]. In this short note, three cases of a Particle-in-Cell (PIC) simulation of electrons, accelerated using a 10 fs Top-hat beam (TH) with a limited pulse energy of 85 mJ, and focused on a double nozzle target using an off-axis parabola (OAP), an axiparabola (AXP), and an axiparabola with spatial-temporal coupling (AXP+STC), are discussed.
# Application of Single-Electron Transistor to Biomolecule and Ion Sensors
## 5 Experimental Demonstrations of Biomolecule Sensing Based on a Single-Electron Transistor
### Detection of Streptavidin  
For the detection of streptavidin, a well-known ligand-receptor binding of biotin-streptavidin was used. Therefore, before the introduction of the target biomolecule (streptavidin), the corresponding receptor (biotin) had to be coupled to the surface of the gate insulator of the SET sensor. Then, the target biomolecules of streptavidin were introduced. After the ligand-receptor binding, the charge on the surface of gate insulator changed the electrical characteristics of the sensor. Figure 9 shows the surface modification of the gate insulator for the detection of streptavidin. After the silicon nitride surface was cleaned and amino-silanized, biotin was reacted with the surface. Then, the biotinylated surface was reacted with streptavidin. Negative \(V_{g}\) shift (\(\Delta V_{g}\)) of the \(l_{d}\)-\(V_{g}\) curve of Coulomb oscillations was observed after the attachment of biotin to the surface of the gate insulator [21]. The successive addition of streptavidin resulted in a positive \(\Delta V_{g}\), which was consistent with the negative charge of streptavidin in solution at pH 8.0-8.2. Therefore, biomolecule detection using an SET sensor was achieved for the first time [21]. The concentration of streptavidin was 10 \(\mu\)g/mL in the above measurement. Later, the detection of streptavidin binding down to 1 ng/mL (16 pM) was achieved (Figure 10) using an SET sensor [22].  
Figure 8: Drain current (\(I_{d}\))-gate voltage (\(V_{g}\)) characteristics of an SET sensor for three different pH values at room temperature. Reproduced with permission from [20], Copyright 2011, AIP Publishing LLC.  
Figure 9: Surface modification of the gate insulator for the detection of streptavidin.
## 5 Discussion
### Advantages and Limitations of the Proposed Method  
The proposed plastic waste classification method offers several advantages over traditional sorting approaches. Firstly, it eliminates the reliance on manual labor, reducing human error and increasing sorting accuracy. The automated nature of the system ensures consistent results, regardless of the operator's expertise or subjective judgment.  
Furthermore, the integration of depth cameras and the YOLO-based object detection algorithm enables real-time classification. This real-time capability enhances the efficiency of waste-sorting operations, minimizing delays and bottlenecks in the recycling process.  
Figure 14: Proposed system confusion matrix.  
Additionally, the combination of compositional analysis and shape-based classification addresses the challenges posed by plastics with similar compositions but different shapes. By incorporating both compositional and shape information, the system improves classification accuracy, reducing misclassifications and enhancing overall sorting effectiveness.  
However, the proposed method also has certain limitations. The reliance on depth cameras may introduce challenges in certain scenarios, such as low lighting conditions or occlusions. Ensuring proper lighting and minimizing occlusions can help mitigate these limitations and maintain accurate classification results.  
Furthermore, the system's performance may be affected by variations in plastic waste samples, such as different colors, textures, or surface conditions. Ensuring a diverse and representative training dataset can help improve the system's ability to handle such variations and enhance classification accuracy.
## 4 Results and Discussion  
According to the application analysis at the city-scale, the C/D ratios of buildings show significant variation in terms of the different land use types; residential, retail and office buildings are the three major types in London. In addition, a size of 50 m \(\times\) 50 m is more cost-effective for sharing than other larger grid sizes from the city-scale point of view [16; 25]. Therefore, to compare the influences of these two district sharing strategies on the application capacities of the GSHP system, three districts in London (Figure 3) were selected as case studies. District 1 was a group of residential, retail and office buildings, District 2 was a group of retail, office and mixed-use buildings and District 3 had little heterogeneity as it was mainly composed of office buildings. All the GSHP systems in Figure 1 (individual system, individual system with borehole sharing and central district system) were used for these districts to perform the comparison analysis.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**System**} & \multirow{2}{*}{**CO\({}_{2}\) Emission Factor**} & \multirow{2}{*}{**System Efficiency**} & \multicolumn{2}{c}{**Equivalent CO\({}_{2}\) Emission per**} \\  & & & **kWh Heating Demand** \\ \cline{2-4}  & **kgCO\({}_{2}\) e/kWh** & **-** & **kgCO\({}_{2}\) e/kWh Heating Demand** \\ \hline Electricity heating & 0.533 & 1 & 0.533 \\ GSHP heating & 0.533 & 3.3 & 0.162 \\ Air conditioner cooling & 0.533 & 3.0 & 0.178 \\ GSHP cooling & 0.533 & 4.2 & 0.127 \\ \hline \hline \end{tabular}
\end{table}
Table 2: CO\({}_{2}\) emissions factors for heating and cooling systems.  
Figure 3: Building types in the three selected districts in London.
## 2 Materials and Methods
### Monitoring Campaign  
#### 2.3.1 The Weather Data  
For the purpose of the annual analysis of the case study buildings, the external hourly values of dry-bulb air temperature and relative humidity were monitored. For the monitoring campaign in Vasari Corridor, the data were provided for the whole year 2017 by the ancient Ximeniano astronomical observatory weather station located inside the city center of Florence [37] in close proximity of the corridor, while, for the campaign in La Specola museum, the data were recorded for 1 year between May 2012 and April 2013 with sensors (Tinytag Plus 2-TGP-4500) installed specifically in the courtyard of the building in a position shielded from direct solar radiation [33].  
Figure 1 shows the positions of the two buildings within the city center of Florence; the maximum distance as the crow flies is about 300 m.  
Figure 5 reports the monthly average values of dry-bulb air temperature (T) and relative humidity (RH) monitored in the two periods. The graphs show how the trend of T and RH values in the two periods, despite the different year and the origin of the data, are definitely comparable, especially after March. The small differences are partly attributable to the position of the sensor in the inner courtyard of La Specola less exposed to natural ventilation, where the RH values tend to remain constantly higher, while T values are slightly higher. In both cases, the temperatures reach the maximum values in the months of July-August and the minimum values in January. Although the variation of RH is flatter, the maximum and minimum values are inversely proportional to the trend of temperatures.  
In non-air-conditioned buildings, the external hygrothermal conditions significantly affect the indoor microclimate, defined by average levels and variability of T and RH. The more the building envelope components are characterized by high thermal transmittance and low thermal inertia and transparent surfaces exposed to solar radiation, the higher the variability of indoor microclimate parameters is, as analyzed in  
Figure 5: Vasari Corridor and La Specola: trend of monthly average outdoor air temperature (T) and relative humidity (RH).
## 5 Conclusions  
We have demonstrated the feasibility of developing an intraocular pressure (IOP) sensor based on the well-known laser-induced cavitation phenomenon and taking advantage of the STM technique developed by our group for the purpose of monitoring a cavitation bubble event on a single shot. Our results show that the Rayleigh cavitation model, which relates the cavitation bubble collapse time to the fluid pressure, allows for a rapid and precise estimation of the IOP. Although the pressure resolution we have achieved in the present study is still not within the clinical required value, we believe it can be improved by using laser pulses of shorter than a nanosecond, since the optical breakdown of the aqueous humor becomes much more deterministic with shorter durations of the laser pulses. An IOP sensor based on the cavitation phenomenon presents many advantages over traditional methods for IOP sensing; to mention a few: It is a noninvasive technique, a precise measurement can be obtained either on a single (ideally) or with a few shots, which allows a very fast measurement, and the measurement does not depend on the physical features of the eyeball. Our preliminary damage assessment confirms that corneal tissue is not compromised for up to tens of delivered laser pulses. There is still more work to do for improving IOP measurement resolution and for completing damage assessment in the whole eye structure. This pending work is currently underway.  
S.C.-L.: Conceptualization, Methodology, and Funding acquisition. C.A.Z.-R.: Data curation and Writing-Original draft preparation, Formal analysis. L.F.D.-C.: Conceptualization, Investigation, and Writing-Review & Editing. C.A.-D.: Methodology and Validation. M.A.P.-S.: Resources and Supervision: L.M.-M.: Data curation and Formal analysis. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by Clinica de Ojos de Tijuana, CONACYT grant number CB-2015-01-254304 and FONCICYT/01/2016 C0013-2014-01-246648.  
The authors would like to acknowledge to the Slaughterhouse of Ensenada, BC, and the Pathology and Clinical Analysis Laboratory at Ensenada, BC.  
The authors declare no conflict of interest.
# Machine Learning-Based Methods for Enhancement of UAV-NOMA and D2D Cooperative Networks
## 3 UFBS Placement Procedure
### 2D UFBS Placement
#### 3.1.2 k-Medoids Analysis and Setup  
In this sub-subsection, the basic principles of the k-medoids algorithm are presented. The k-medoids method can be used for the 2D placement of the UFBS in \(A\) in the same fashion as k-means. However, the way that the UFBS placement point \(p_{1}\) is selected differs between the two approaches. As previously stated, in the k-means UFBS placement scheme, the centroid point \(p_{1}^{e}\) is the empirical mean of the coordinates \(U\) of the GMTs in \(A\). However, in k-medoids, it is one of the actual GMT\({}_{l}\) (\(1\leq l\leq N\)), and it is called medoid point \(p_{1}^{m}\). Specifically, in k-means, the point-to-group-centroid distance is assessed concerning a virtual point \(p_{1}^{e}\in A\), while in k-medoids, it is measured concerning one of the actual data points \(u_{l}\in A\) (\(p_{1}^{m}=u_{l}\)) where \((1\leq l\leq N)\), i.e., actual GMTs location. Similarly to the k-means algorithm, the goal of the k-medoid method is to minimize the medoid-point to group distances metric, expressed as \(\sum_{u_{l}\in U}\|u_{l}-p_{1}^{m}\|^{2}\) by solving the following minimization problem:  
\[\operatorname*{arg\,min}_{p_{1}^{m}\in A}\sum_{u_{l}\in U}\|u_{l}-p_{1}^{m}\|^ {2}. \tag{29}\]  
The operation of the 2D UFBS placement process using the k-medoids algorithm is summarized in Algorithm 2.  
In the same manner with k-means, it is assumed that the number of UFBS Y = 1. However, as can be shown in Algorithm 2, the k-medoids algorithm can be straightforwardly applied to scenarios with Y \(>\) 1. Additionally, Algorithm 3 is the modified version of Algorithm 2 for the special case where Y = 1.
## 4 Results
### Ablation Experiments  
To illustrate the impact of each component in SemiPolypSeg, we conducted four distinct experiments: training HTCSNet by adding only CPS, training by adding only contrastive learning, training without the transformer encoder branch (w/o Transformer), and training without the CNN encoder branch (w/o CNN). The CVC-ClinicDB dataset,  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **Labeled/Unlabeled** & **DSC (\%)** \\ \hline Fully supervised (HTCSNet) & 800/0 & 90.95 \\ Baseline (supervised-only) & 120/0 & 86.33 \\ MT (DeepLabv3+) [20] & 120/680 & 87.44 \\ CCT (DeepLabv3+) [21] & 120/680 & 81.14 \\ CMT (DeepLabv3+) [19] & 120/680 & 88.08 \\ UCMT (DeepLabv3+) [19] & 120/680 & 88.68 \\ SemiPolypSeg (DeepLabv3+) (ours) & 120/680 & 89.03 \\
**SemiPolypSeg (HtCSNet) (ours)** & **120/680** & **89.68** \\ Baseline (supervised-only) & 240/0 & 87.45 \\ MT (DeepLabv3+) [20] & 240/560 & 88.72 \\ CCT (DeepLabv3+) [21] & 240/560 & 84.67 \\ CMT (DeepLabv3+) [19] & 240/560 & 88.61 \\ UCMT (DeepLabv3+) [19] & 240/560 & 89.06 \\ SemiPolypSeg (DeepLabv3+) (ours) & 240/560 & 89.91 \\
**SemiPolypSeg (HTCSNet) (ours)** & **240/560** & **90.62** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantitative comparison of our method with other semi-supervised methods on Kvasir-SEG using 15% and 30% labeled data. Bold values highlight the top performance in comparison to other SOTA approaches.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **Labeled/Unlabeled** & **DSC (\%)** \\ \hline Fully supervised (HTCSNet) & 488/0 & 90.90 \\ Baseline (supervised-only) & 74/0 & 85.16 \\ MT (DeepLabv3+) [20] & 74/414 & 84.19 \\ CCT (DeepLabv3+) [21] & 74/414 & 74.20 \\ CMT (DeepLabv3+) [19] & 74/414 & 85.88 \\ UCMT (DeepLabv3+) [19] & 74/414 & 87.30 \\ SemiPolypSeg (DeepLabv3+) (ours) & 74/414 & 89.47 \\
**SemiPolypSeg (HTCSNet) (ours)** & **74/414** & **89.72** \\ Baseline(supervised-only) & 148/0 & 86.38 \\ MT (DeepLabv3+) [20] & 148/340 & 84.40 \\ CCT (DeepLabv3+) [21] & 148/340 & 78.46 \\ CMT (DeepLabv3+) [19] & 148/340 & 86.83 \\ UCMT (DeepLabv3+) [19] & 148/340 & 87.51 \\ SemiPolypSeg (DeepLabv3+) (ours) & 148/340 & 89.61 \\
**SemiPolypSeg (HTCSNet) (ours)** & **148/340** & **90.06** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Quantitative comparison of our method with other semi-supervised methods on CVC-ClinicDB using 15% and 30% labeled data. Bold values highlight the top performance in comparison to other SOTA approaches.  
with only 15% labeled data, was used to evaluate segmentation performance using the DSC metric. Table 6 presents the DSC findings from all experiments, indicating that SemiPolypSeg surpassed other frameworks, showing a mean enhancement of 1.62% over CPS alone and 4.43% over contrastive learning alone. Using HTCSNet with dual-encoder branches led to an average improvement of 3.99% and 5.48% compared to networks without the transformer encoder branch and without the CNN encoder branch, respectively, highlighting its effectiveness in enhancing segmentation performance.