esa-sceva/satcom-chunk-collection huggingface dataset:
[16] applied PCA in order to summarise the relative differences amongst samples in relation to their overall fatty acid and volatile profiles in commercial lambs [16]. The first dimension of PCA explained 27.1% of the total variation in fatty acid and volatile composition, and the second dimension explained 18.2% of the total variation. On the other hand, [PERSON]. [17] investigated the overall aroma profile in grazing lambs. According to the PCA results, the first two principal components explained 94.1% of the variance in aroma. They demonstrated that the feeding system had an influence on the volatile compounds of lamb meat. PCA analysis has also been applied to accurately identify the volatile compounds in beef processed by different treatments [18]. In this study, PCA showed good discrimination between the ultrasound treatment group and the control group since PC1 and PC2 accounted for more than 87% of the total variance [18]. Moreover, PCA has been used to clearly distinguish pig breeds according to their fatty acids and reheating volatiles [19]. These authors reported that five pig breeds could be clearly distinguished by PCA analysis according to their fatty acids and reheating volatiles. They observed that the Chinese indigenous pork may produce the lowest levels of the WOF during reheating compared with the four other pig breeds [19]. The multivariate statistical analysis was applied in some other studies on animal food products. Ref. [20] differentiated dry-cured hams and showed that the first two principal components explained 81.3% of total variability. The samples were clearly separated according to the progressive ripening time. Moreover, they stated that the volatiles profile of Polish dry-cured ham had been intensively formed during all 30 weeks of ripening. Ref. [21] used PCA analysis in their studies on volatile compounds of Rhode Island Red (R-11) capons and cockerels. They observed that the proportion of the total variance explained by the first two principal components was 88.4%, and the classification accuracy, calculated using cross-validation, was 100%. This study aimed to comprehensively analyse and quantify the different aromatic and flavour compounds in cooked lamb meat released upon cooking with different methods.
## 3 Parameterization Schemes for Fiber Orientation
### Feature-Based Parameterization  
The parameterization schemes, as mentioned earlier, introduce low-level fiber material representations, such as pixel or voxel-based representations, thus representing the designs with variables proportional to the number of pixels or voxels in the design space. Moreover, these techniques render organic and free-form designs, which require sophisticated postprocessing to distinguish fiber paths for the use of CF4. Therefore, to avail manufacturable solutions with designs containing few variables, fiber material can be considered as a geometric feature with high-level parameters. High-level parameters refer to spatial dimensions associated with a feature's size, position, or orientation. Finally, feature-mapping techniques map these features onto a fixed mesh for analysis; an extensive review of feature-mapping methods by [PERSON] et al. [45] details the components of feature-mapping techniques and discusses their implementation in structural optimization. Geometry projection [120] is an explicit feature-mapping technique extended to represent the design via cylindrical bars reinforced with continuous fibers [121] and performs the analysis using a fixed finite element mesh. The interpolation of the material properties at the junction of multiple bars made of an anisotropic material is penalized as a convex combination of the penalized effective densities for each component. Furthermore, it demonstrates that the method can easily integrate shape constraints on the structural form offered by CF4 as shown in Figure 5. Thus, this work introduces the groundwork for using the geometry projection method for fiber-orientation optimization design problems. However, the fiber is restricted to being unidirectional along the bars, further constraining the possibility of exploiting CF4's design freedom. In the same spirit, the method can be used to accurately control the structure's size through the explicit representation of features. For example, [PERSON] et al. [122] proposed a trapezoidal component made of primary material and wound by the fiber layers. However, the framework results in small features size appearing in numerical problems. The Table 1 compares parametrization schemes, stating their allowed degree of freedom in the design space, advantages and disadvantages in finding an optimized design for continuous FRCs material, and applicability to CF4. Figure 4: (**Left**) Schematic diagram represents design variables of an element and parameterizing continuous orientation variable in the discrete-continuous setting. (**Right**) Implementation of the multiple print plane to design MBB [91].
## 3 Results
### Treatment Patterns/Sequencing  
The current study examined the sequence of treatments from 1L PBC through avelumab 1 LM and subsequent treatments. Among the 214 patients who received 1L PBC followed by avelumab 1 LM, 115 (53.7%) received cisplatin-based therapy and 99 (46.3%) received carboplatin-based therapy (Table 1). The most prevalent cisplatin-based regimen was cisplatin plus gemcitabine (n = 101, 87.8%), and the most prevalent carboplatin-based regimen was carboplatin plus gemcitabine (n = 98, 99.0%). Most patients had a best response to 1L PBC of rwPR (n = 159, 78.7%), with 14 patients (6.9%) having a rwCR and 29 patients (14.4%) having rwSD.  
Most patients (83.6%) either remained on avelumab 1 LM through the study period end (n = 83, 38.8%) or received 2L treatment (n = 96, 44.9%) (Figure 3). Of patients receiving 2L treatment, the most common was EV monotherapy (n = 53, 55.2%). Additional 2L treatments categories included carboplatin-based therapies (n = 11), IO monotherapy (n = 6), target-based therapies (n = 6), other ADC-based therapies (n = 4), IO plus other therapies (n = 3), IO plus targeted-based therapies (n = 1), cisplatin-based therapies (n = 1), IO plus ADC-based therapies (n = 1), and other therapies (docetaxel [n = 1]; docetaxel plus leuprolide [n = 1]; fluorouracil plus mitomycin [n = 1]; gemcitabine plus paclitaxel [n = 2]; letrozole [n = 1]; methotrexate [n = 1]; paclitaxel [n = 2]; topotecan [n = 1]).  
\begin{table}
\begin{tabular}{l c} \hline
**Characteristic** & **Avelumab 1 LM (n = 214)** \\ \hline
**Best response to 1L PBC *, n (\%)** & \\ \hline rwCR & 14 (6.9) \\ \hline rwPR & 159 (78.7) \\ \hline rwSD & 29 (14.4) \\ \hline \end{tabular} Due to rounding, percentages may not add up to 100%. Abbreviations: 1L, first line; 1 LM, first-line maintenance; ECOG PS, Eastern Cooperative Oncology Group performance status; G, grade; la/mUC, locally advanced/metastatic urothelial carcinoma; MVAC, methotrexate, vinblastine sulfate, doxorubicin, and cisplatin; PBC, platinum-based chemotherapy; QI, first quartile; Q3, third quartile; rwCR, real-world complete response; rwPR, real-world partial response; rwSD, real-world stable disease; Std Dev, standard deviation. * 202 patients had a recorded response to 1L PBC.  
\end{table}
Table 1: _Cont._  
Figure 2: Patient population. Abbreviations: 1L, first line; 1 LM, first-line maintenance; EHR, electronic healthcare record; IO, immuno-oncology; la/mUC, locally advanced/metastatic urothelial carcinoma; DCT, line of therapy; PBC, platinum-based chemotherapy; UC, urothelial carcinoma.  
Of 96 patients who received 2L treatment, 32 (33.3%) had ongoing 2L treatment at data cutoff, while 40 (41.7%) received 3L treatment. EV monotherapy (n = 10, 25.0%) was the most common 3L treatment, with half of these patients (n = 5) receiving 2L targeted therapy with erdafitinib. Other 3L therapies included other ADCs (n = 8), IO monotherapy (n = 5), carboplatin-based therapies (n = 3), targeted therapies (n = 3), cisplatin-based therapies (n = 2), IO plus ADC (n = 1), and other therapies (n = 8).
## 2 Materials and Methods
### The ZigBee-Based WSN System for Environmental Monitoring in the Pig House
#### 2.2.3 The Systematic Composition and Working Principle of the WSN System  
The front end of the system uses ZigBee wireless communication technology in the house to achieve multiple monitoring points of distributed networking. With the technical features, all node devices can be flexibly arranged at any height and location in the barn, the ZigBee network topology will be adjusted to an optimal state upon power-on. Each node device in the system can be equipped with a variety of sensors used to collect various types of data sources, enabling a real-time reflection of the complex temperature, humidity and gas environment in the pig house. The system ends with a data transfer unit (DTU)that packages and uploads real-time environmental information to the server, and the cross-platform Web application finally displays all kinds of data in various sections. The monitoring system is meanwhile a complete set of Internet of Things application system, and its three-layer architecture consists of the pig house environment perception layer, wireless transmission service layer, multiclient application layer.  
The underlying hardware is divided into two device types: ZigBee slave sensor nodes and ZigBee master node. There is only one master node, which is used to collect and summarize all the slave nodes data and upload the information to the service layer. The wireless transmission service layer receives data packets from the master node, relying on General Packet Radio Service (GPRS) technology, establishes a connection with the server according to the agreed IP address and port number, and realizes transparent point-to-point transmission of data using DTU. The server will analyze the received message data one by one and store the historical data in the software database at the same time. The multiclient application layer adopts the browser/server (B/S) model, which connects the database to the browser and realizes the remote monitoring function of the system through web pages. The browser-based application can meet the cross-platform requirements of the computer side or mobile network devices. Figure 3 summarizes the WSN system in the pig housing and its layers.  
The master and slave nodes work closely together to complete the task of collecting and uploading data. Figures 4 and 5 show the positioning of the nodes in the pig housing.
## 3 Results
### Spectral Analysis of PEMGRAPH Measurements  
The spectral analysis (SA) approach was applied to the dynamic time-dependent activity data to quantify different positron emitting radioisotopes. The step-by-step procedure used for this analysis can be found in the proposed scheme shown in Table 3. The 2D images with their associated 1D profiles generated using the SA technique are shown in Figure 6. In Figure 6, the y-axis represents the normalized intensity for each radioisotope. The sum of all radioisotopes (shown as SA_sum in Figure 6), the \({}^{11}\)C (shown as SA_11C in Figure 6) has two peaks--namely, (1) at the entrance and (2) at a higher depth. On the other hand, the \({}^{15}\)O (shown as SA_15O in Figure 6) has a peak at a higher depth, and it appeared after the second peak of \({}^{11}\)C. The \({}^{13}\)N (shown as SA_13N in Figure 6) peak was found to appear after the \({}^{15}\)O peak. In addition, the 1D profiles are the summation of 2D images (see Figure 6) along the y-axis (i.e., lateral position); therefore, the shape of 1D profiles would be smooth without showing any sharp cutoffs.  
Figure 5: The 3D (**first row**), 2D (**second row**) and 1D (**third row**) time—course activity covering 15 to 55 min data sets obtained from the PEMGRAPH measurements.  
The ROIs at which the SA method was applied are schematically illustrated in Figure 4. There are four distinct regions: the whole, edge, plateau and Bragg region (see Figure 4). In Figure 7, the x-axis for all ROIs reflects the extracted half-life of positron emitting radioisotopes (_log(2)/_\(\beta\)_), and the y-axis shows the radionuclide concentration, labeled as normalized \(\alpha\). Considering Figure 7, we observe that for the whole ROI, the contribution from long-lived radioisotopes was dominant. It is observed that the highest contribution from long-lived radioisotopes is for the edge ROI, which is close to \({}^{11}\)C radioisotopes. In the plateau region, it is observed that the total contribution is the same as in the whole and edge regions (i.e., the greatest contribution is from long-lived radioisotopes). On the other hand, for the Bragg ROI, the half-life of radioisotopes was slightly larger than that of the \({}^{13}\)N radioisotopes. From the present analysis scheme, the activity in the distal fall-off region of proton-induced \({}^{13}\)N radioisotopes has been confirmed as the real half-life of \({}^{13}\)N is \(\sim\)10 min. The discretization in SA and measurement errors (background counts and contributions from multiple radioisotopes) would lead to some differences in the estimated half-life value for a specific radioisotope when compared to its known physical half-life.  
Figure 6: The 2D (**first row**) and 1D (**second row**) profile of the SA output \({}^{11}\)C, \({}^{15}\)O and \({}^{13}\)N radioisotopes and their sum (color in 2D plot corresponds to those in 1D profiles).  
Figure 7: The spectral analysis (SA) for different ROIs. Pink shows no radioisotope components, magenta shows \({}^{11}\)C, blue shows \({}^{15}\)O and green shows \({}^{13}\)N radioisotopes, respectively.
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 6 HARRF SPDT Design
### SPDT Integration with X-Band OFF and K/Ka-Band ON
#### 7.3.1 Gain Performance  
Figure 25 presents the simulated gain between 23 dB and 27 dB for the K/Ka-band arm from 23 to 28 GHz.
# SS3 DNet-AF: A Single-Stage, Single-View 3D Reconstruction Network with Attention-Based Fusion
## 3 Proposed Methods
### Image Encoder
#### 3.1.1 Initial Convolution and Pooling  
During the image encoding phase, we employ a convolutional operation and subsequent max pooling layer to analyze the input image \(I\) and extract essential low-level features. The mathematical representation of these operations is expressed as  
\[\begin{split}& X_{conv1}=Conv(I,W_{conv1}),\\ & X_{pool}=MaxPool(X_{conv1}),\end{split} \tag{2}\]  
where \(I\) is the input image, Conv signifies the convolution operation with a weight matrix \(W_{conv1}\), and MaxPool denotes the max pooling function.
# Simplified Derivative-Based Carrierless PPM Using VCO and Monostable Multivibrator
## 3 Proposed Techniques of Demodulation and Modulation
### Proposed Carrierless PPM Techniques
#### 3.2.4 Proposed Derivative-Based PPM Modulation Using VCO and Monostable Multivibrator  
Building on the previous analyses, which show that all major carrierless PPM demodulation methods yield the derivative of the message signal, we propose a novel modulation technique. Instead of generating PPM through conventional PWM comparison and monostable multivibrator circuits, this method first differentiates the message signal and then uses it to drive a voltage-controlled oscillator (VCO). The VCO output, representing a frequency-modulated signal, is then fed into a monostable multivibrator to generate the PPM signal. This approach leverages the fundamental relationship between phase, frequency, and pulse position modulation, and directly aligns the modulation process with the inherent properties revealed in demodulation. The block diagram of this simplified structure is shown in Figure 11. This method offers an efficient and structurally streamlined alternative for carrierless PPM generation.
# Cooperative UAV Scheme for Enhancing Video Transmission and Global Network Energy Efficiency
## 2 Related Work
### VOEI Operation
#### 3.2.2 UAV Replacement  
Energy-awareness is a key factor in FANETs in order to keep the UAVs cooperating as much as possible during a mission, as well as to maintain the delivery of videos with a better QoE while. In this way, the \(CN\) is aware of the remaining energy \(RE_{i}\) of all UAVs, enabling to detect that a given UAV is running out its energy supply. In such case, the UAV must fly to the battery replacement location \(L(X_{r},Y_{r})\) in order to replace the battery.  
Periodically, the \(CN\) computes the required energy \(E_{replacement}\) to fly from their current location \(L(X_{i},Y_{i})\) to the battery replacement location \(L(X_{r},Y_{r})\) for all UAVs based on Equation (10). In this way, a given UAV \(v_{i}\) must fly for the battery replacement location \(L(X_{r},Y_{r})\), as soon as it has the remaining energy \(RE_{i}\) value closer the required energy to fly for battery replacement \(E_{replacement}\). This is because, without flying to the battery replacement location, such UAV \(v_{i}\) will run out of the battery resources, reducing the number of UAVs in the scenario, causing route failure and void area, as well as causing network partitioning. In addition, a given UAV \(v_{i}\) flying for battery replacement cannot be selected as a relay node \(RN_{i}\) or source node \(SN\).  
A given source node \(SN\) or relay node \(RN_{i}\) could also require battery replacement, even computing the minimal energy \(E_{min}\) to become source node \(SN\) or relay node \(RN_{i}\) based on Equation (6). This is because minimal energy \(E_{min}\) does not consider the energy for computing tasks, to receive packets, and also other sources of energy depletion. In this case, the \(CN\) must detect such situation and perform the source or relay node replacement. The \(CN\) computes the cost function \(C_{SN/RN}\) for all UAVs \(v_{i}\) based on Equation (11) in order to select a new source or relay node. It is important to mention that source or relay node with low energy only starts to fly for the battery replacement after the selected UAV arrives closer to its location, avoiding route failure during video transmission.
## 2 Materials and Methods
### Numerical Models of the RCM User's Body and Exposure Scenarios  
A fundamental feature of devices designed to be worn on the body is that they may be located near various parts of the body [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15]. Our numerical simulations cover the most common exposure scenarios, employing numerical models of the head, arm, forearm, and chest. The novelty of our approach is in simultaneously using both simplified [S] body sections (multi-layer) and those reflecting the relevant anatomical [A] structure of the human body (by using a model of the whole body with an RCM located near particular body parts corresponding to the considered simplified models of particular body sections), and then comparing the outcome from both approaches in particular exposure scenarios.  
The anatomical [A] model used in the study was the commercially licensed Duke v3.1, developed by IT'S (Foundation for Research on Information Technologies in Society, Zurich, Switzerland) [30]. It consists of over 300 tissues/organs, is 177 cm tall, weighs 70.2 kg, and has a body mass index (BMI) of 22.4. Details of the simplified, multi-layered numerical models developed for this study are presented in Figure 3 and Table 3.  
The dimensions of the simplified [S] models of the head, arm, and forearm correspond to the dimensions of the 50 th percentile of adult males of the Polish population, and the thicknesses of particular layers correspond to the medians defined for humans, or to the anatomical [A] model used [31; 32]. In turn, the earlier developed simplified [S] numerical model of the chest corresponded to a physical phantom designed for research on WBANs, as presented in [8]. Its dimensions are 320 mm in width and 188 mm in depth, making it comparable to the dimensions of the chest of the anatomical [A] model used (approx.  
Figure 3: Transverse cross-sections of the models of body parts at the height of the ARCM location in particular exposure scenarios ([A] cross-sections—anatomical models; [S] cross-sections—simplified, multi-layer models; green box—side view of the ARCM; yellow box—front view of the ARCM).  
318 mm in width and 215 mm in depth). The numerical models of the chest are comparable to the dimensions of the 50 th percentile of adult males of the Polish population in a standing position (292 mm in width and 242 mm in depth) [31].  
The exposure scenarios also covered models from two groups of WIT devices: (1) a model of a device for use in the form of a disposable, adhesive plaster (APD), where the ARCM is located directly on the body (0 mm distance between the ARCM and the body) and (2) a model of a classic, reusable, wearable electronic device mounted inside a plastic housing (CWED), where the ARCM is located 2 mm away from the body surface.
Sensors_**2015**, _15_, 22266-22290; doi:10.3390/s150922266  
_Sensors_  
ISSN **1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
Energy-Efficient Transmissions for Remote Wireless Sensor Networks: An Integrated HAP/Satellite Architecture for Emergency Scenarios  
**[PERSON] 1,2,*, [PERSON] 1,2, [PERSON] 1,2, [PERSON] 2 and [PERSON] 2**  
\({}^{1}\) College of Communications Engineering, PLA University of Science and Technology,  
88 Houbiaoying Rd., Nanjing 210007, China; E-Mails: [EMAIL_ADDRESS] (H.L.);  
[EMAIL_ADDRESS] (X.G.)  
\({}^{2}\) Institute of China Electronic System Engineering Corporation, 13 Dacheng Rd., Beijing 100141,  
China; E-Mails: [EMAIL_ADDRESS] (Q.L.); [EMAIL_ADDRESS] (J.W.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-189-1027-1519.  
Academic Editor: [PERSON]  
_Received: 9 July 2015 / Accepted: 1 September 2015 / Published: 3 September 2015_  
**Abstract:** A typical application scenario of remote wireless sensor networks (WSNs) is identified as an emergency scenario. One of the greatest design challenges for communications in emergency scenarios is energy-efficient transmission, due to scarce electrical energy in large-scale natural and man-made disasters. Integrated high altitude platform (HAP)/satellite networks are expected to optimally meet emergency communication requirements. In this paper, a novel integrated HAP/satellite (IHS) architecture is proposed, and three segments of the architecture are investigated in detail. The concept of link-state advertisement (LSA) is designed in a slow flat Rician fading channel. The LSA is received and processed by the terminal to estimate the link state information, which can significantly reduce the energy consumption at the terminal end. Furthermore, the transmission power requirements of the HAPs and terminals are derived using the gradient descent and differential equation methods. The energy consumption is modeled at both the source and system level. An innovative and adaptive algorithm is given for the energy-efficient path selection. The simulation results validate the effectiveness of the proposed adaptive algorithm. It is shown that the proposed adaptive algorithm can significantly improve energy efficiency when combined with the LSA and the energy consumption estimation.  
keywords:remote wireless sensor networks; emergency communications; integrated high altitude platform/satellite architecture; path selection; energy-efficient transmissions; link-state advertisement +
Footnote †: journal: Journal of the American Statistical Association