esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Volatile Compounds in Chicken Meat of Trial 2  
One hundred sixty-six volatile compounds were detected in the chicken breast meat of Trial 2. The results of the Kruskal-Wallis test (presented in Table 2) indicated that 33 compounds showed significant differences among treatments. The main groups of compounds that showed differences were alcohols (6), aldehydes (10), hydrocarbons (9), thiols (2), nitriles (1), and acids (1) while 4 compounds were not identified (unknown). In accordance with Trial 1, there were 7 compounds detected in trace amounts (i.e., \(<\)0.30 ng/g). Levels of 1-pentanol, 1-hexanol, 1-octen-3-ol, hexanal, heptanal, 2-heptenal, octanal, nonanal, decanal, 2,2,4,6-pentamethyl-heptane, decane, and one unknown (RT: 13.40 min) were lower (\(p<0.05\)) in chickens from the HI-dietary treatment compared to SP-fed and C-fed chicken. Levels of 4-ethyl-cyclohexanol, 1-octanol, 2-nonen-1-ol, pentanal, toluene, 2,2,6-trimethyl-octane, undecane, 4-cyano-cyclohexene, and one unknown (RT: 18.15) were lower (\(p<0.05\)) in chickens fed the HI-diet than the ones from C and/or the SP diet, which, in most cases, did not differ (\(p>0.05\)). On the contrary, levels of 2-ocrenal, 2-undecenal, 4-methyl-nonane, 2,6,7-trimethyl-decane, 2-methyl-decane, 2,8-dimethyl-4-methylene-nonane, 2-ethyl-1-hexanethiol, 2-methyl-2-heptanethiol, 2-methyl-decane, and two unknown (RT:15.26 min and RT:16.51 min) were higher (\(p<0.05\)) in HI-fed chickens in comparison to C-fed and/or the SP-fed chickens.  
Linear discriminant analysis of the volatile data separated the dietary groups of Trial 2 with 14 compounds according to the kappa-coefficient (Figure 2). The first component (explaining 61.1% of the variation) separated the HI group (left side of the plot) from the C group (right side of the plot). The compounds that contributed mostly in the separation (i.e., factor loadings higher than 1.0) were hexanal, 1-hexanol, heptanal, 2-heptenal, 4-methyl-nonane, 2,2,6-trimethyl-octane, 1-octen-3-ol, 2,6,7-trimethyl-decane, and 2-decenal. The second component explained 39% of the variation separated the SP group from the HI and C groups (Figure 2). The compounds that contributed to the separation were hexanal, 1-hexanol, heptanal, 2-heptenal, 4-methyl nonane, 2,2,6-trimethyl octane, 1-octen-3-ol, 2-ethyl-2-hexenol, 2,6,7-trimethyl-decane, 2-methyl-decane, and nonanal (Supplementary Table S3).
# RSIMS: Large-Scale Heterogeneous Remote Sensing Images Management System
## 4 Discussion
### Binary Remote Sensing Image Files I/O Performance  
Besides the metadata, the other part of remote sensing images are the pixel data stored in binary files. These data are managed in the form of object by the RSIData of RSIMS. The bottom distributed object storage system, CEPH, provides RSIData with excellent capacity scalability and reliability. On top of CEPH, we designed and implemented two I/O interfaces for compatibility with GDAL interfaces and better I/O performance. To verify the I/O performance of RSIData, we compared it with HDFS under different number of concurrent read clients. The results show that RSIData and HDFS are both stable when faced with high concurrent access, but the data transferring speed of RSIData is faster than HDFS.  
The I/O performance improvement of RSIData is achieved by two factors: (1) The CRUSH algorithm adopted by CEHP in RSIData can keep large amounts of remote sensing files well balanced across the servers. Based on the algorithm, clients can locate the data address quickly and then read data from the corresponding server directly, which can make full usage of the bandwidth resources of different servers; (2) Some information and data blocks of frequently accessed remote sensing files are cached in the client server with the CachedFileBlock module described in Section 2.3, which reduces the network overload and saves a lot of time.
# Development of Fast Protection System with Xilinx ZYNQ SoC for RAON Heavy-Ion Accelerator
## 3 Composition of Fast Protection System
### Mitigation Node  
The mitigation node consists of a main board for signal processing and data storage and a daughter board for _FPS_ device control. The main board has a memory and buffer arranged around the multiprocessor system-on-chip (_MPSo_C). The front panel consists of eight SFP+ transceiver modules, Ethernet transceivers, SD cards, and USB to JTAG ports, and the rear panel consists of connectors for data output to the daughter board. Table 2 presents the specification of the mitigation node prototype.  
The _PL_ of the mitigation node receives the status information of the _FPS_ external device input from the acquisition node at 1 \(\upmu\)s interval to the postmortem system (_PMS_) data module through the GTH interface. If an abnormal signal is detected, the cutoff signal is transmitted to bthe output of 24 channels, and then status information is stored in the BRAM inside the PL, and a log file is created [11]. In addition, the time stamp and event code received from the event generator (_EVG_) are delivered to the \(\mathrm{{}^{EVR}}\) module via the GTH interface. The time stamp data is stored in BRAM along with external device status information stored in real time. BRAM stores not only the status signals of external devices but also built-in test (BIT) data, such as firmware version, temperature data, and status information of the mitigation node [12].  
PetaLinux is ported in the _PS_ area. In addition, the _EPICS IOC_ operates the _FPS_ system. The interface with the _PL_ is connected to the AXI Interconnect using the device driver inside the kernel to control BRAM and external devices. The _EPICS IOC_ uses processing values (PVs) to create the elements that are needed for system operation and data transfer. _OPI_ is used to control and monitor the generated _PV_[13]. Figure 3 shows the _PL_ and _PS_ configurations of the mitigation node.
## 2 Materials and Methods
### MRI Acquisition and CVR Processing
To account for the temporal lag between 5% CO\({}_{2}\) inhalation and its subsequent cerebrovascular effects, which are known to occur within tens of seconds in the GM [2; 20], we performed a stimulus timing corrected analysis using the SPM12 first-level analysis function. This involved evaluating potential timing corrections in increments of one TR (2.5 s). Given that cerebrovascular responses to hypercapnic stimuli are subject to physiological delays--primarily due to the gas delivery system and respiratory dynamics--they typically emerge within approximately 10 to 30 s after stimulus initiation [2; 20]. To accurately align the modeled CO\({}_{2}\) onset with the observed BOLD signal change, we sought to identify the optimal physiological delay. To this end, we systematically shifted the modeled onset of the CO\({}_{2}\) stimulus in each participant's first-level general linear model (GLM), applying delays in 2.5 s increments (the fMRI sampling rate, TR) across a 0 to 50 s range. For each shifted onset, beta-maps were computed for all participants. Subsequently, a one-sample \(t\)-test was performed at each time point across subjects to evaluate the group-level response. The physiological delay was determined by identifying the time point at which the mean beta values across the whole brain reached their maximum. This peak was observed at 20 s, which was then applied as the optimized delay in modeling the CO\({}_{2}\) stimulus onset (Figure 3). This timing correction was then applied to the transition from air to CO\({}_{2}\) inhalation periods. Consequently, the mean BOLD signals for the air inhalation periods were extracted from 0 to 120 s and 261 to 380 s, while the mean BOLD signals for the CO\({}_{2}\) inhalation periods were extracted from 141 to 260 s and 381 to 500 s, effectively incorporating the 20 s stimulus timing correction. Finally, the stimulus timing corrected MRI (tcMRI) CVR was calculated as in (1):  
\[CVR=\frac{\left[BOLD\ (CO_{2})-BOLD\ (AIR)\right]}{BOLD\ (AIR)}\times 100\% \tag{1}\]
# High-Speed Underwater Optical Wireless Communication with Advanced Signal Processing Methods Survey
## 1 Introduction
The signal processing enhancement, which includes transmitter frequency response improvement [27; 29; 35], transmitter shot noise minimization [36], and inter-symbol interference (ISI) elimination techniques [28; 37; 38], has also achieved remarkable progress in recent years. Digital signal processing (DSP) technologies can significantly improve the signal-to-noise ratio (SNR) and reduce the bit-error rate (BER) of UOWC systems with low cost and high efficiency. Although the previous survey papers [2; 13] included the DSP aspect, only a short and simple introduction is presented. Therefore, this survey provides a comprehensive review of the recent developments of advanced DSP techniques in UOWC systems, which include:  
1. A brief introduction and summary of equalization principles. 2. A detailed review of NML equalization techniques in UOWC systems in recent years, including both linear equalizers and nonlinear equalizers. 3. A detailed review of ML techniques in UOWC systems, including both supervised learning and reinforcement learning schemes. The rest of this survey is organized as follows: in Section 2, we introduce the general architecture and principles of UOWC systems. Moreover, we discuss and summarize equalization technique principles and introduce ML applications in UOWC systems. In Section 3, we provide a detailed review of the linear equalizers and nonlinear equalizers applied in recent UOWC progress. In Section 4, we provide a review of ML applications in UOWC. Moreover, we discuss the challenges of both NML and ML equalizers and provide our views on future UOWC technologies from the advanced signal processing techniques perspective in Section 5. Finally, we conclude the survey in Section 6. \begin{table}
\begin{tabular}{l c l} \hline \hline
**References** & **Year** & **Area of Focus** \\ \hline [PERSON] et al. [8] & 2016 & •UOWC LOS, NLOS, and retro-reflector channels \\  &  •Optical attenuation modeling \\  &  •UOWC system design \\  &  •Future scope \\ \multirow{3}{*}{[PERSON] et al. [15]} & \multirow{3}{*}{2017} & •UOWC LOS and NLOS channels \\  & & •Optical attenuation and turbulence modeling \\  & & •Theoretical modulation and coding \\  & & •Practical implementations of UOWC \\ \multirow{3}{*}{[PERSON] et al. [19]} & \multirow{3}{*}{2018} & •UOWC typical LOS and NLOS channels \\  & & •Optical attenuation and turbulence modeling \\  & & •Future challenge in transceiver technologies \\
# SARFNet: Selective Layer and Axial Receptive Field Network for Multimodal Brain Tumor Segmentation
## 4 Experiments and Results
### Evaluation Metrics  
Quantitative and qualitative analyses were carried out by evaluation metrics, including the Dice similarity coefficient (Dice) score and the Hausdorff distance (HD).  
The Dice coefficient is a measure of the similarity between the network-predicted segmentation results and manual masks in image segmentation, which can be represented as follows:  
\[Dice=\frac{2 TP}{2 TP+FP+FN} \tag{9}\]  
where \(TP\), \(FP\), and \(FN\) are the values of true positive, false positive, and false negative voxels, respectively.  
HD represents the maximum distance between the predicted segmentation region boundary and the real region boundary. The smaller the value, the smaller the prediction boundary segmentation error and the better the quality. The \(HD\) can be written as follows:  
\[HD(P,T)=max\{sup_{t\in T}\text{ }in\text{ }f_{p\in P}d(t,p),\text{ }sup_{p\in P} \text{ }in\text{ }f_{t\in T}d(t,p)\} \tag{10}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Basic Configuration** & **Value** \\ \hline PyTorch Version & 1.10.0 \\ Python & 3.8.10 \\ GPU & NVIDIA GeForce RTX 3090(24 G) \\ Cuda & 11.3 \\ Learning Rate & \(1.00\times 10^{-4}\) \\ Optimizer & ranger \\ Batch Size & \(1\) \\ Input Size & \(128\times 128\times 128\) \\ Output Size & \(128\times 128\times 128\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model parameter configuration.  
where \(t\) and \(p\) represent the real region boundary and predicted segmentation region boundary, respectively; \(d(\bullet)\) represents the distance between \(t\) and \(p\); and \(sup\) denotes the supremum.
# Optimization of Submodularity and BBO-Based Routing Protocol for Wireless Sensor Deployment
## 3 Routing Protocols for Wireless Sensor Networks  
### Communication Model  
The energy consumption of data sent by sensor nodes is shown in Equation (16).  
\[E_{TX}(k,d)=E_{elec}(k)+E_{amp}(k,d)=\left\{\begin{array}{l}kE_{elec}+kE_{fs}d^{ 2},d<d_{0}\\ kE_{elec}+kE_{mp}d^{4},d\geq d_{0}\end{array}\right., \tag{16}\]  
where \(k\) is the number of bits of transmitted data, \(d\) is the transmission distance, \(E_{elec}(k)\) is the energy consumption of the transmitting circuit to send \(k\) bit data, and \(E_{amp}(k,d)\) is the transmission power amplifier transmitting \(k\) bit data when the transmission distance is \(d\). \(E_{elec}\) is the unit energy consumption of the transmitting or receiving circuit, and \(d_{0}\) is the threshold. \(E_{fs}\) is the energy consumption parameter of the transmission power amplifier under the free space channel model and \(E_{mp}\) is the energy consumption parameter of the transmission power amplifier under the multipath fading channel model.  
The calculation of the energy consumption of the receiving circuit to receive \(k\) bit data is shown in Equation (17).  
\[E_{RX}(k)=E_{TX-elec}(k)=kE_{elec}. \tag{17}\]
# Late-Stage Optimization of Modern ILP Processor Cores via FPGA Simulation
## 4 FPGA Implementation  
### Pnx Processor Architecture  
To demonstrate the effectiveness of the proposed methodology, we introduce a concrete example, which is based on an our in-house PNX processor. The baseline PNX processor is a typical modern four-issue processor with out-of-order execution, which has 10+ pipeline stages and supports a 128-bit SIMD execution. It has separate L1 instruction (ICache) and L1 data (DCache) cache and a shared L2 cache. The data can be prefetched in a stride-detecting manner for the L1 DCache and the unified L2 cache. A wide range of parameters must be tuned to explore the best trade-offs in terms of the selected system merits, such as performance and area, and consequently optimize performance.  
Several late-stage performance simulations require various RTL models for numerous design options. The configurability of the PNX processor is largely determined by the result of the late-stage aware RTL design. Each processor model corresponds to a fixed microarchitecture for performance evaluation and optimization. Table 3 lists examples of the configuration. Brackets are used to denote the basic processor configuration. These options are composed of \"E\" and \"M\" design efforts. For other design options with \"E\" and \"M,\" modifying the processor model is also feasible. The design options are notably beyond the range of the basic options with the MAX or MIN type and unsuitable for late-stage simulation. For example, assume _decodeWidth_ is 4. If we model a processor with _decodeWidth_ larger than 4, then its design effort should be \"H\".  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Block** & **Unit** & **Configuration** \\ \hline \multirow{8}{*}{Fetch block} & \multirow{8}{*}{Branch predictor} & BTBEntries (2K), TableSize (4K), \\  & & RASSize (32), \\  & & IndirectPredictorSize (512), \\  & & preType (TAGE algorithm) \\ \cline{2-3}  & \multirow{2}{*}{Ifetch} & fetchBufferSize (32) \\ \cline{2-3}  & & Decode & decodeWidth (4) \\ \hline \multirow{8}{*}{Out-of-order block} & \multirow{8}{*}{Bispatch} & Rename & RenameMapTableSize (32), \\  & & RenameQueueSize (16) \\ \cline{1-1} \cline{2-3}  & & Dispatch & DispatchQueueSize (12), \\ \cline{1-1} \cline{2-3}  & & RenameRegisterSize (128), \\ \cline{1-1} \cline{2-3}  & & RegisterFilePortNum (12), \\ \cline{1-1} \cline{2-3}  & & numROBEntries (128) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Design option examples for PNX processor core.
# Progressive Dynamic Registration Method for Tile Maps Based on Optimum Multi-Features
## 1 Introduction  
The most widely used coordinate system for online tile maps is the WGS-84 coordinate system. Google Maps, Bing Maps, and Open Street Map usually use WGS-84, while some maps use other coordinate systems. For example, Tianditu uses CGCS2000, Google Maps and Amap within China use GCJ-02, which was introduced by the National Bureau of Surveying and Mapping of China, and Baidu Maps uses BD-09. Some niche maps may use local independent coordinate systems. There are differences in display results between different coordinate systems [1], so data from different sources will be offset when overlapped and used [2]. Therefore, before using maps from different sources simultaneously, it is necessary to register maps with different coordinate systems. Map registration refers to the process of configuring a map with an unknown or different coordinate system to a map with a known coordinate system. The traditional method based on the formula needs to include the coordinate projection formula of the two maps, and the usage scenarios are relatively limited. The implementation process of semi-automatic map registration involves selecting some corresponding points between different maps as control points by computer or human, calculating the registration parameters, and then using the registration tool to perform projection transformation on the map to be registered [3]. Among the fully automatic registration methods, the feature-based registration method can automatically acquire image features for registration, and it is currently the most widely used method of image automatic registration [4].  
Using the registration method based on image features, we only need to obtain the map image service to complete the registration, and the registration result is more accurate to a certain extent. Different feature elements are used to match different types of mapsand different regions. Compared with the registration method that only uses feature points, the PDR method has better matching accuracy and wider application scenarios.  
Feature-based automatic image registration usually includes four steps: (1) feature extraction; (2) feature matching; (3) determining the image transformation model; (4) image transformation and interpolation [5]. In the feature extraction stage, the features can be divided into feature points, feature lines, and feature surfaces [6].
# Opportunities and Challenges for Error Control Schemes for Wireless Sensor Networks: A Review
## 5 Review on ECS Methods on WSN
### Fec
#### 5.1.1 FEC for Single Hop Asymmetric Structure (SHAS) WSN
The result indicates that the use of re-transmission is reduced significantly by the use of strong code (which is so obvious as FEC reduces BER) and saves power consumption in sensor nodes in WSN with SHAS. Table 2 shows a summary of FEC based ECS for SHAS structure. The review conducted on FEC used in SHAS WSN indicates that there are limited works on the use of ECS in SHAS structure, and found that there is no recent research work reported in the literature. As SHAS WSN is a practical WSN for home and industry automation and the use of capacity approaching codes such as LDPC, turbo and RA codes in SHAS WSN has huge potential, there is a need for further research work on the use of strong and capacity approaching codes (such as LDPC and turbo codes) in SHAS WSN.
## 3 Results and Discussion
### Reset Operation
In addition, the Peltier effect can force the programming current down to 2.3 mA in the positive bias and up to 2.95 mA in the reverse bias (solid circles), respectively. The RESET programming current polarity in the simulation (2.3-2.95 mA) is close to that in the literature data (2.3-3.2 mA, hollow circles) [20], which confirms our simulation. Thomson heating in the mushroom cell is simulated as a contribution to 5% of the programming current reduction. The impact of isotropic scaling on thermoelectric effects is also addressed. The cell size-scaling can result in the reduction of the RESET current and change the corresponding thermal profiles, so the impact of thermoelectric effects on the RESET operation is influenced, shown in Figure 6. The programming current reduction, compared with the results neglecting thermoelectric effects, represents the contributions of Thomson and Peltier heating to the RESET operation. The contribution of the Thomson effect basically keeps constant regardless of the size scaling (solid circles). However, the influence of the Peltier effect on programming current gradually weakens, resulting in the decrease of current reductions induced by thermoelectric effects. Given the cell size aggressive scaling trends, the effect of scaling on the thermoelectric effects requires further investigation for the design of PCM cells. Figure 5: Cell resistance as a function the RESET current for a fixed pulse width of 100 ns with and without Peltier effect. Figure 6: Impact of thermoelectric effects on the programming current for the RESET operation with the bottom electrode width (W\({}_{\text{BE}}\)) scaling from 250 nm to 50 nm, assuming isotropic downscaling.
# Design of a Cargo-Carrying Analysis System for Mountain Orchard Transporters Based on RGB-D Data
## 3 Model Construction and Volume Measurement Strategy and Deployment
### YOLOv7-tiny Object Detection Algorithm
#### 3.1.1 Optimization of Loss Function  
The loss function of the YOLOv7-tiny model is composed of three parts: localization, confidence, and classification loss [21]. Among them, the classification and confidence loss are evaluated by the cross-entropy (CE) loss. However, the YOLOv7-tiny model generates many bounding boxes during prediction, but only a few of them contain detection objects, which creates the problem of imbalance between positive and negative examples and can easily lead to a deviation in model optimization. We introduced focal loss to address the imbalance and optimize the loss function.  
The cross-entropy loss for binary classification (\(p_{t}\)) is defined as  
\[p_{t}=\left\{\begin{aligned} & p& y=1\\ & 1-p& otherwise\end{aligned}\right.CE(p,y)=CE(p_{t})=- \log(p_{t}) \tag{1}\]  
where \(y\) specifies the ground-truth class and \(p_{t}\) is the estimated probability for the class.  
Focal loss [22] introduces \(\alpha_{t}\in[0,~{}1]\) to balance the importance of positive and negative examples and adds a modulating factor, \((1-p_{t})^{\gamma}\), with a tunable focusing parameter, \(\gamma\geq 0\)  
Figure 4: Custom RGB dataset. (**a**) Carrying one layer of fruit or baskets. (**b**) Carrying two layers of fruit or baskets.  
to differentiate between easy and hard examples, as shown in Formulas (2) and (3). The focal loss is shown in Formula (4).  
\[\alpha_{t}=\begin{cases}\alpha&y=1\\ 1-\alpha&\text{otherwise}\end{cases}CE(p,y,\alpha)=\begin{cases}-\log(p)*\alpha&y=1 \\ -\log(1-p)*(1-\alpha)&y=0\end{cases} \tag{2}\]  
\[FCE(p_{t})=-(1-p_{t})^{\gamma}\log(p_{t}) \tag{3}\]  
\[FL(p_{t})=-\alpha_{t}(1-p_{t})^{\gamma}\log(p_{t}) \tag{4}\]
## 2 Circular-Slotted Antenna  
The design included a conventional model and model 1, as depicted in Figure 1. The conventional CSA (Figure 1a) applies linear polarization (LP) radiation. The slot radius \(R\) determines the resonant frequency of this antenna. The antenna is simulated on a substrate with a relative permittivity \(\varepsilon_{r}\) of 2.17, thickness \(h\) of 1.6 mm, loss tangent of 0.0005, and slot radius \(R\) of 26.5 mm. The copper thickness \(t\) of 0.035 mm, the antenna width \(W\) of 95 mm, and the length \(L\) of 100 mm yield a resonant frequency of 2.17 GHz. The feedline length \(L_{f1}\) is 40 mm, \(L_{f2}\) is 8 mm, width \(W_{f1}\) is 4 mm, and \(W_{f2}\) is 4 mm. Figure 2 depicts the final design, referred to as model 2. The dimensions of the CSA model are provided in Table 1.