esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Power Rectennas in Microwave Wireless Power Transmission
## 2 Low-Power Rectifiers
### Matching Network Enhancement
#### 2.2.2 Dynamic DC-Load Matching  
When the input power is relatively low, the optimal load resistance for a rectifier is generally the sum of the diode's junction resistance \(R_{j}\) and series resistance \(R_{s}\)[14]. To address variations in junction resistance \(R_{j}\), wide-load techniques are considered to achieve dynamic dc-load matching. Maximum power point tracking (MPPT) technology is widely used for optimal load matching [66; 67; 68; 69]. In [67], a fractional open-circuit voltage approximation method was proposed to determine the optimal load. The reconfigurable rectifier designed with this method operates over an input power range from \(-22\) dBm to \(-2\) dBm, maintains an efficiency above 20% from \(-16\) dBm to \(-2\) dBm, and achieves MPPT accuracy exceeding 86%. Reference [68] reported a novel feedback-free fast (\(\Omega\)) MPPT module based on input power detection and direct parameter adjustment. The block diagram of the controller is shown in Figure 8, which includes an RF power detector, flash ADC, timing controller, REG (register), and logic circuits. The fabricated rectifier, implemented in \(0.18\)\(\upmu\)m CMOS technology, operates over an input power range from \(-23\) dBm to \(1\) dBm, maintains efficiency above 20% from \(-17\) dBm to \(1\) dBm, and achieves MPPT accuracy exceeding 96%. Another approach is dynamic self-bias impedance compensation between two sub-rectifiers operating in low-load and high-load regions. The layout of the circuit is shown in Figure 9. Experimental results show that circuits based on this technique achieve efficiency greater than 50% under \(0\) dBm input power with an output load between \(0.4\) and \(9\) k\(\Omega\), and can maintain operation over an input power range from \(-15\) dBm to \(10\) dBm [70].
## 3 Influence of Different Simulation Methods for Internal Thermal Boundary Conditions
### Box Girder
#### 3.1.3 Influence on the Average Effective Temperatures and Vertical Temperature Gradients  
The hourly average effective temperature (\(T_{\text{AVG}}\)) of the box girder cross-sections can be estimated using the following rules. The temperature of each element in the FEM is multiplied by its respective element area, and the results for all elements in the cross-section are added together. Then, \(T_{\text{AVG}}\) can be obtained by dividing the sum by the total area of the cross-section [13; 22]. The highest \(T_{\text{AVG}}\) values of the box girder cross-sections for 13 August 2017, obtained from the field test and the FEMs with different simulation methods for the internal thermal boundary conditions, are listed in Table 1. It can be observed that the influence of different simulation methods for the internal thermal boundary conditions on the highest \(T_{\text{AVG}}\) was small, with the maximum difference of 0.7 \({}^{\circ}\)C.  
The numerical maximum vertical temperature gradients of the box girder cross-section on 13 August 2017, which were obtained from the FEMs with different simulation methods for the internal thermal boundary conditions, are compared in Figure 13. It can be observed that the influence of different simulation methods for the internal thermal boundary conditions on the trends of the vertical temperature gradients for the box girder cross-section was small, with a maximum difference of 0.4 \({}^{\circ}\)C. The maximum vertical temperature differences on the top flanges and bottom flanges calculated by the Ambient Temperature Method and the Mean Temperature Method were slightly larger than those calculated by the Measured Temperature Method and the Air Element Method. The maximum vertical temperature differences on the webs calculated by the Ambient Temperature Method and the Mean Temperature Method were slightly smaller than those calculated by the Measured Temperature Method and the Air Element Method. The numerical maximum vertical temperature gradient of the box girder cross-section calculated by the Air Element Method was close to that calculated by the Measured Temperature Method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & **Measured** & **Ambient** & **Mean** & **Air Element** \\  & **Temperature** & **Temperature** & **Temperature** & **Method** \\  & **Method** & & **Method** & **Method** \\ \hline Measured value & & & 34.9 & \\ Numerical value & 35.0 & 35.0 & 34.6 & 35.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons of the highest \(T_{\text{AVG}}\) of the box girder cross-sections obtained from the field test and the FEMs with different simulation methods for the internal thermal boundary conditions (unit: \({}^{\circ}\)C).  
Figure 12: Temperature contour plots of the box girder cross-sections at 15:00 on 13 August 2017: (**a**) Measured Temperature Method; (**b**) Ambient Temperature Method; (**c**) Mean Temperature Method; (**d**) Air Element Method.
# A Survey: Future Smart Cities Based on Advance Control of Unmanned Aerial Vehicles (UAVs)
## 3 Types and Characteristics of UAVs
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **UAV Type** & \multicolumn{2}{c}{**Applications**} & \multicolumn{2}{c}{**Advantages**} & \multicolumn{2}{c}{**Drawbacks**} \\ \hline \multirow{2}{*}{\(\bullet\)} & Rotary Wing & \(\bullet\) & photography & \(\bullet\) & hovering & \(\bullet\) & short flight time \\  & (multicopter) (a) & \(\bullet\) & filmography & \(\bullet\) & availability & \(\bullet\) & small payload \\  & & \(\bullet\) & inspection & \(\bullet\) & low price & \(\bullet\) & \\ \hline \multirow{6}{*}{\(\bullet\)} & Fixed-Wing & Hybrid & & & \(\bullet\) & large area coverage & \multirow{6}{*}{} & \multirow{6}{*}{} \\  & & & & \(\bullet\) & long endurance & & \\ \cline{1-1}  & & & & \(\bullet\) & hovering & & \\ \cline{1-1}  & Fixed-Wing & Hybrid & \(\bullet\) & structural inspection & \(\bullet\) & large payload & \\ \cline{1-1}  & VTOL (b) & \(\bullet\) & area survey & \(\bullet\) & great maneuverability & \(\bullet\) & stability issues in wind \\ \cline{1-1}  & & & \(\bullet\) & supply drops & \(\bullet\) & ease of use & \\ \cline{1-1}  & & & & \(\bullet\) & efficient camera control & \\ \cline{1-1}  & & & & \(\bullet\) & vertical takeoff and & \\ \cline{1-1}  & & & & & landing (VTOL) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of UAV with types [29]. Figure 5: Types of UAV rotors: (**a**) Rotary Wing (Multicopter), (**b**) Fixed-Wing, (**c**) Rotary Wing, (**d**) Fixed-Wing [29]. Controlling UAVs using nonlinear control strategies can be highly important and beneficial in certain situations. Nonlinear control approaches offer advantages over traditional linear control methods by accounting for the nonlinear dynamics and uncertainties present in UAV systems.
## 4 Results and Discussion
### Biological validation of extracted Key Genes
\begin{table}
\begin{tabular}{c c c c c} \hline
**AFFY ID** & **Gene Symbol** & **ENTREZ Gene ID** & **Onology** & **-Gene Pathway** \\ \hline
36711\_at & MAFF & 23764 & leukemia/lymphoma (BCR-ABL1) & REACT\_24970. Megakaryocyte and platelet \\  & & & & construction. \\ \hline
205749\_at & CYP1A1 & 1543. & hepatocellular carcinoma, NOS, unstated behavior & KEGG: hsa: 1543. \\  & & & & & \\ \hline
209775\_x\_at & SLC19A1 & 6573. & anaplastic large cell lymphoma & KEGG: hsa 6573. \\  & & & & & \\ \hline
205767\_at & EREG & 2069. & chronic myelogenous leukemia (BCR/ABL-positive) & KEGG: hsa 2009. \\  & & & (BCR/ABL-positive) & REACT\_147727. Signaling by PIX in Cancer. \\ \hline
21799\_at & PHLDA1 & 22822 & gastrointestinal stromal sarcoma & KEGG: hsa: 22822. \\ \hline
226515\_at & CCDC127 & 133957 & renal cell carcinoma & KEGG: hsa:133957 \\ \hline
206085\_s\_at, & CTH & 1491 & Bladder Cancer & REACT\_115589. \\  & & & & Cysteine omation from homocysteine \\ \hline
225285\_at & BCAT1 & 586 & HCC & REACT\_197. Branched-chain amino acid \\  & & & & catabolism. \\ \hline
202847\_at & PCK2 & 5106 & HCC & KEGG: hsa0020 Citrate cycle (TCA cycle) \\ \hline
209173\_at & AGR2 & 10551 & Breast Cancer & KEGG: hsa: 10551. \\ \hline \end{tabular}
\end{table} 
Table 3: Functional annotation of key genes detected using Euclidean distance after different periods post-infection with C virus. \begin{table}
\begin{tabular}{c c c c c} \hline
**AFFY ID** & **Gene Symbol** & **ENTREZ Gene ID** & **Onocology** & **Gene Pathway** \\ \hline
204892\_x\_at & EEF1A1 & 1915 & HCC & REACT\_1404. Peptide chain elongation \\ \hline
1553567\_s\_at & ATP6 & 4508. & HCC, adenoma & REACT\_6759. Development of ATP. \\ \hline
200801\_x\_at & ACTP & 948575. & hematopoietic & KEGG: ecoc. b4067. \\ \hline
212788\_x\_at & FTL & 2512. & HCC & REACT\_163699. Scavenging by Class A \\ \hline
200801\_x\_at & ACTB & 60 & Langerhans-cell histiocytosis & REACT\_20649. Cell-extracellular matrix \\ \hline
201596\_x\_at & KRT18 & 3875. & HCV, adenocarcinoma & KEGG: hsa: 3875. \\ \hline
1553570\_x\_at & COX2 & 5743 & Adencarcinoma, Mutation in HCC & REACT\_11213. Nicotinamideed salvaging. \\ \hline
224372\_at & MTND4 & 4538. & Adencarcinoma, Mutation in HCC & REACT\_22393. Respiratory electron \\ \hline
212661\_x\_at & PPTA & 5478. & Burkitt lymphoma & REACT\_9406. HIV-1 infection. \\ \hline
221798\_x\_at & RPS2 & & anaplastic large cell lymphoma & \\ \hline
1553538\_s\_at & COX1 & 5742. & Mutation gene in HCC & REACT\_1396. COX reactions. \\ \hline
211296\_x\_at & UBC & 7316. & leukemia/lymphoma & REACT\_115852. Signaling by EGFR Cancer Variants. \\ \hline \end{tabular}
\end{table} 
Table 4: Functional annotation of detected key genes using PCA after different periods of post-infection with C virus.
# A Machine Learning Approach to Predict Fluid Viscosity Based on Droplet Dynamics Features
###### Abstract  
In recent years, machine learning has made significant progress in the field of micro-fluids, and viscosity prediction has become one of the hotspots of research. Due to the specificity of the application direction, the input datasets required for machine learning models are diverse, which limits the generalisation ability of the models. This paper starts by analysing the most obvious kinetic feature induced by viscosity during flow--the variation in droplet neck contraction with time (\(h_{min}/R\sim\tau\)). The kinetic processes of aqueous glycerol solutions of different viscosities when dropped in air were investigated by high-speed camera experiments, and the kinetic characteristics of the contraction of the liquid neck during droplet falling were extracted, using the Ohnesorge number (\(Oh=\mu/(\rho R\sigma)^{1/2}\)) to represent the change in viscosity. Subsequently, the liquid neck contraction data were used as the original dataset, and three models, namely, random forest, multiple linear regression, and neural network, were used for training. The final results showed superior results for all three models, with the multivariate linear regression model having the best predictive ability with a correlation coefficient \(R^{2}\) of 0.98.  
d pacs: 0
## 4 Results and Discussion
### Phase 1--Analysis of the Reference Context  
#### 4.1.1 Definition of the Most Common Building Typology  
The investigation carried out in the present research has concerned all the provincial capitals located in the Italian context and has focused on the prevailing building typology, i.e., the multi-family and multi-story residential building in reinforced concrete and composed by four floors. With reference to the national context and by considering the data published by ISTAT collected for the 15 th General Census of the population and housing in 2011, the number of existing buildings classified by type of intended use has been analyzed for each region. According to the data detected, the Italian building stock is mainly characterized by the residential typology, that is, 84.2% of the total built, i.e., 12.18 million of buildings compared to total 14.4 million. Moreover, by taking into account a study developed by CRESME in 2017 [67], with reference to the 15 million of buildings located on the Italian territory, 11.9 million buildings consist of residential ones, intended exclusively for housing or hosting a mix of residential and economic activities; 1.6 million are buildings entirely used for the performance of non-residential, primary, secondary, or tertiary activities; 1.5 million buildings are unused; abandoned; or intended for other uses, such as electrical substations, toll or gasoline stations, towers, fortifications, and lighthouses.  
Therefore, with reference to the criteria adopted for the selection of building typology, the identification and the architectural and constructive characterization of the existing buildings have been carried out. In fact, by considering the categories detected by ISTAT (residential, commercial, office, etc.) and the available data on the housing stock in terms of structural, morphological, and functional type, the factors of the building typology to be considered in the analysis have been defined.  
It should be added that the application of the proposed evaluation methodology to the case study selected represents a first implementation of the assessment tool and, in this sense, the choice of structural type has been also ruled by the available information data in terms of energy retrofit interventions defined in the Italian cities. Other practical developments of the methodology may concern different building types, such as masonry buildings, single and two-family buildings, and three and four- families buildings, as villas.
# Task Offloading Scheme Based on Proximal Policy Optimization Algorithm
## 4 Model System
### Problem Solving
#### 4.7.2 State Space  
During the time period \(T\) for task offloading on edge devices, before a certain time segment, \(t\), starts, the system's state space can be represented as \(S_{t}=[j_{n}(t),x_{n}(t),c_{m}(t),B_{m}(t)]\), where \(j_{n}(t)\) indicates the current task generation and upload status of the terminal device, \(x_{n}(t)\) represents the current user's location information, \(c_{m}(t)\) represents the service cache information in the edge computing device, and \(B_{m}(t)\) represents the current bandwidth situation of the edge computing device.
# Design and Optimization of Piezoelectric-Powered Portable UV-LED Water Disinfection System
## 4 Optimization
### Feasible Objective Functions (OFs)  
#### 4.1.1 Shake Duration  
From the user's point of view, the first objective function (OF) could be the shake duration (\(T_{S}\)). This helps to estimate the optimal shake duration for typical values of all design parameters. The OF can be derived based on the mechanical aspects as well as the energy harvesting and the targeted disinfection performance of the system. Firstly, since identical PZT ceramics are used in the system, the harvested energy can be written as  
\[E_{h}=P\ N\ T_{S} \tag{9}\]  
where \(N\) is the number of \(PZT\) ceramics and \(T_{S}\) is the shake duration.  
Next, the dissipated energy can be written as  
\[E_{d}=\ P_{d}\ T_{d} \tag{10}\]  
where \(P_{d}\) is the average power of UV-LED, \(T_{d}\) is the duration of the disinfection process, which also corresponds to Exposure Time in Equation (8). Thus, based on Equation (8), \(T_{d}\) can be calculated in terms of the predefined UV dosage along with UV-LEDs flux power (\(\phi\)) via  
\[T_{d}=\ \pi\ r_{3}^{2}\ D_{\text{UV}}/\phi \tag{11}\]  
where \(D_{\text{UV}}\) is the UV dose given in Table 3. Therefore, Equation (10) can be re-written as  
\[E_{d}=\ \frac{2.25\ P_{d}}{\phi}. \tag{12}\]  
Using Equations (9) and (12), and inserting the power in Equation (5), the shake duration can be calculated as follows  
\[T_{S}=\frac{2.25\ P_{d}}{P\ N\ \phi}. \tag{13}\]  
In the system, the amount of the harvested energy is directly proportional to the shake duration. Therefore, it is expected to have equal amounts of the harvested and dissipated energy in ideal conditions. Then, by expanding the terms using Equations (1)-(7), the power, \(P\), can be obtained as  
\[P=\ \frac{1.61\times 10^{6}\ h_{3}^{3}\ \big{(}1.296\times 10^{-3}\ h_{1}-h_{1}r_{2}^{2 }+3.36\times 10^{-2}\ r_{2}^{2}-r_{2}^{3}\big{)}^{3}f_{s}^{4}\ a^{2}}{R_{PZT}^{4}}. \tag{14}\]  
The shake duration can be then written as  
\[T_{S}=\frac{2.25\ P_{d}R_{PZT}^{4}}{1.61\times 10^{6}\ N\ \phi\ \ h_{3}^{3} \big{(}1.296\times 10^{-3}h_{1}-h_{1}r_{2}^{2}+3.36\times 10^{-2}\ r_{2}^{2}-r_{2}^{3}\big{)}^{3 }f_{s}^{4}a^{2}}. \tag{15}\]
# Filament X-ray Tube Current Control Method Using Indirect Filament Temperature Estimation
## 3 Indirect Filament Temperature Estimation Algorithm  
### Conventional Method  
Filament heating uses a filament driver circuit, as illustrated in Figure 4, to authorize the current command in a step or gradient method to heat the filament [6; 7; 8; 9]. However, as indicated in Equations (4), (5) and (8)-(11), the filament properties based on the temperature and variations in temperature exhibit a non-linear relationship with the input current. Therefore, the current and voltage command values are tuned according to the desired dose rate based on the experience of the medical personnel in terms of the conventional method. Additionally, the sublimation of tungsten reduces its cross-sectional area with the continuous use of the filament tube [9]. This changes the resistance value, causing it to converge to a higher temperature at the same current input. In such cases, it is difficult to control the filament temperature precisely and match the desired tube current.  
Figure 3: The thermal properties of filament considering the temperature: (**a**) filament heat capacity, (**b**) filament thermal conductivity, and (**c**) filament emissivity.
# USD-YOLO: An Enhanced YOLO Algorithm for Small Object Detection in Unmanned Systems Perception
###### Abstract  
In the perception of unmanned systems, small object detection faces numerous challenges, including small size, low resolution, dense distribution, and occlusion, leading to suboptimal perception performance. To address these issues, we propose a specialized algorithm named Unmanned-system Small-object Detection-You Only Look Once (USD-YOLO). First, we designed an innovative module called the Anchor-Free Precision Enhancer to achieve more accurate bounding box overlap measurements and provide a smarter processing mechanism, thereby improving the localization accuracy of candidate boxes for small and densely distributed objects. Second, we introduced the Spatial and Channel Reconstruction Convolution module to reduce redundancy in spatial and channel features while extracting key features of small objects. Additionally, we designed a novel C2f-Global Attention Mechanism module to expand the receptive field and capture more contextual information, optimizing the detection head's ability to handle small and low-resolution objects. We conducted extensive experimental comparisons with state-of-the-art models on three mainstream unmanned system datasets and a real unmanned ground vehicle. The experimental results demonstrate that USD-YOLO achieves higher detection precision and faster speed. On the Citypersons dataset, compared with the baseline, USD-YOLO improves mAP50-95, mAP50, and Recall by 8.5%, 5.9%, and 2.3%, respectively. Additionally, on the Flow-Img and DOTA-v1.0 datasets, USD-YOLO improves mAP50-95 by 2.5% and 2.5%, respectively.  
computer vision; object detection; you only look once; deep learning; unmanned system +
Footnote †: journal: Applied sciences
## 3 Experiment Results and Analysis
### Effects of Controlling Factors on Seepage-Erosion Process
#### 3.2.3 Effects of Confining Pressure on Seepage-Erosion Process
\begin{table}
\begin{tabular}{c c c c c} \hline
**Confining Pressure (MPa)** & **Ultimate Water-Outflow Rate (m3/h)** & **Soil Sample Mass (kg)** & **Total Mass Loss (kg)** & **Mass Loss Rate (\%)** \\ \hline
0.0 & 5.61 & 9.54 & 6.34 & 66.46 \\
0.2 & 7.62 & 9.54 & 6.83 & 71.59 \\
0.4 & 7.90 & 9.54 & 8.02 & 84.07 \\ \hline \end{tabular}
\end{table} 
Table 9: Variation of monitoring objects’ characteristic value with confining pressure (\(P_{c}\)).
# Imitating Human Go Players via Vision Transformer
## 4 Experiments and Results
### Performance Evaluation for Imitating Human Moves
#### 4.2.2 Extended Top-k Accuracy  
In most studies, such as [2; 12], only top-one accuracy is reported as a measure of a model's ability to mimic human experts. However, we argue that relying solely on top-\(k\) accuracy is insufficient for evaluating model performance, particularly in the context of Go.  
In the opening stage of a Go game, there are often multiple logically equivalent moves according to the rules of the game. However, the ground truth typically contains only one of these moves. For instance, as illustrated in Figure 9, moves A and B are equivalent in terms of their impact on the game. If the ground truth labels move A as correct and the model predicts move B, it would be unjust to consider the prediction a failure, since both moves achieve the same strategic outcome.  
An even more striking example is depicted in Figure 10, where eight moves are all logically equivalent. In this scenario, the model has only a one-eighth chance of being marked correct by top-one accuracy, despite all eight moves being equally valid. This limitation highlights the need for more nuanced evaluation metrics or alternative approaches that account for the equivalence of moves in certain game scenarios.  
More formally, the extended top-k accuracy can be expressed by  
\[\text{Extended Top-kAcc.}=\frac{1}{N}\sum_{i=1}^{N}\textit{II}(y_{i}\in\bigcup _{y^{\prime}\in\textit{Equiv}(y_{i})}\text{Top-k}(\hat{y}_{i},y^{\prime})) \tag{6}\]  
where the variables have the following meanings:  
* \(N\) is the total number of instances in the dataset;
* \(y_{i}\) is the true label for the \(i\)-th instance;
* \(y_{i}\) represents the predicted scores or probabilities for the \(i\)-th input;
* Top-k\((\hat{y}_{i},y^{\prime})\) represents the set of k labels with the highest predicted scores or probabilities for the \(i\)-th instance, considering label \(y^{\prime}\);
* \(\textit{Equiv}(y_{i})\) is the set of labels considered equivalent to the true label \(y_{i}\);
* \(\textit{II}(\cdot)\) is the indicator function, which returns 1 if the condition inside is true and 0 otherwise.  
Figure 9: Black to play. Moves A and B are considered equivalent according to the rules.  
The results regarding extended top-k accuracy are listed in Table 3. Performance evaluations based on top-k accuracy and extended top-k accuracy both showed that the ViT with eight encoder layers was sufficient for the task.