esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Ablation Study  
The concept of ablation experiments to examine the knowledge structure represented in DNNs. A detailed ablation investigation was conducted to demonstrate the validity of the proposed DL approach. In an ablation research, a layer, activation function, etc. of the deep learning model are removed or replaced in order to evaluate how each component contributes to the representation of the overall network. To be more specific, the performance of the DeepBreastCancerNet model is evaluated without the other model branch as it is removed. The sustainability of the DeepBreastCancerNet architecture is being evaluated as part of this ablation study, which is essential for figuring out how these components impact the system's performance. An ablation analysis was performed using two experiments that involved changing numerous components of the suggested framework. These experiments confirmed the impact of the two activation functions (Leaky ReLU and clipped) on the performance of the suggested model. In the first study, the feature extraction layers solely employed the Leaky ReLU function. Moreover we removed global average pooling. Similarly, in the second study, we replaced the clipped Relu activation function in the feature extraction layers with a global average pooling layer prior to the final FC layer. The performance of the ablated models is summarized in Table 5. According to the results, each component of the suggested DL model that is changed or removed makes the system perform worse.  
Figure 5: Accuracy and loss function graph of DeepBreastCancerNet.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experiment** & **Activation Function** & **Global Average Pooling** & **Results/Accuracy** \\ \hline Ablation Study 1 & Leaky ReLU & No & 99\% \\ \hline Ablation Study 2 & Clipped ReLU & Yes & 99.20\% \\ \hline Proposed Model & LeakyReLU + Clipped ReLU & Yes & 99.35 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Modifying the evaluation network for the ablation study.
## 3 Experimentation  
### Collection of Field Data  
The engines under study are turbocharged compression ignition (C.I.) engines with 12 cylinders, V-type and a maximum power rating of 900 H.P, rotating at 2100 rpm. In CI engines, air is compressed in the combustion chamber such that the injected liquid fuel can easily catch fire and burn progressively for power generation. Figure 11 shows a view of the dumper engine under study (see Appendix A). TBF data of each subsystem is collected for a period of three years from the mechanical register book of the mechanical open pit mine. The failure data in Table 1 were found to be less than 7 for each subsystem.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**S1.No.**} & **The Sub System** & **Failed Component** & **TBF for Engine 1** & **Failed Component** & **TBF for Engine 2** & **Failed Component** & **TBF for Engine 3** \\ \hline \multirow{3}{*}{1} & \multirow{3}{*}{Air suppl} & Compressor & 2655 & Dryers & 422 & Condensate Trap & 77 \\  & & Dryers & 633 & Electric motor & 600 & Moisture Separator & 3585 \\  & & Oil Remover & 4112 & Pressure Gauge & 2036 & Turbo charger & 1673 \\ \hline \multirow{3}{*}{2} & \multirow{3}{*}{Self-starting} & Battery & 1246 & Solenoid & 423 & Starter gear & 1920 \\  & & Filter & 44 & Motor & 185 & Battery & 797 \\  & & Starter gear & 856 & Filter & 761 & Gear pump & 550 \\  & & Solenoid & 2328 & Gear pump & 1197 & Filter & 191 \\  & & Motor & 3913 & Battery & 1116 & Motor & 917 \\  & & Gear pump & 759 & Starter gear & 3450 & Solenoid & 1595 \\ \hline \multirow{3}{*}{3} & \multirow{3}{*}{Fuel supply} & Pulsation & \multirow{3}{*}{423} & \multirow{3}{*}{Shut down valve} & \multirow{3}{*}{96} & Cam & 112 \\  & & Mapetic & 240 & Fuel tank & 346 & Pulsation damper & 1449 \\  & & screen & 525 & Magnetic screen & 914 & Shut down valve & 290 \\  & & Injector & 934 & Injector & 2036 & Fuel tank & 225 \\  & & Throttle & 3856 & Injector & 2036 & Magnetic screen & 2828 \\  & & Cam & & & & \\ \hline \multirow{3}{*}{4} & \multirow{3}{*}{Lubrication} & Turbocharger & Oil pump parts & 1584 & Oil pump parts & 855 \\  & & bearings & 2566 & Tappets and push-rods & 757 & Turbocharger bearings & 1115 \\ \cline{1-1}  & & Valves & 2278 & Valves & 238 & Timing Gears & 1503 \\ \cline{1-1}  & & Tappets and push-rods & 426 & Camshaft and bearings & 991 & Cylinder walls & 1367 \\ \cline{1-1}  & & push-rods & Timing Gears & 916 & Piston rings & 990 \\ \cline{1-1}  & & & & & & & \\ \end{tabular}
\end{table}
Table 1: TBF data (in hours) of engine subsystems of three engines.
# Perioperative Nutritional Support: A Review of Current Literature
## 3 Importance of Perioperative Nutrition and Physiopathology
### Mechanisms Related to the Disease That Indicates the Need for Surgical Intervention  
In surgical patients, often of advanced age, there is a situation of chronic low-grade inflammation, sustained mainly by interleukin-6 (IL-6), interleukin-1 (IL-1), tumor necrosis factor alpha (TNF-\(\alpha\)), interleukin-2 (IL-2) and interferon alpha (IFN-\(\alpha\)), in addition to the nuclear factor kappa beta (NF\(\kappa\)), target of rapamycin (TOR), transforming growth factor beta (TGF-\(\beta\)), Janus kinase/activator of transcription (JAK/STAT) and RAS pathway, among others [22; 23; 24; 25]. These cytokines are expressed at above normal values, and their activation produces deleterious effects such as chronic inflammation, release of hepatic acute phase reactants, insulin resistance, increased catabolism with the appearance of sarcopenia (with the consequent decrease in muscle strength), and osteocl activity. Likewise, the intestinal microbiota seems to have a very important role in this process, together with nutritional status, in such a way that both malnutrition and overnutrition favor the pro-inflammatory environment. An oxidative environment also occurs in this context, which could affect the stability of DNA and its repair mechanisms. To counteract this inflammatory state, the pathway of the anti-inflammatory cytokines interleukin-4 (IL-4), interleukin-10 (IL-10) and interleukin-13 (IL-13) is activated inducing the stress response of the hypothalamic-pituitary-adrenal axis, which in turn induces an increase in cortisol synthesis that will cause secondarily, and as unwanted side effects, bone resorption, lipolysis, protein catabolism, gluconeogenesis and immune dysfunction, depending on the system on which it acts, ultimately producing frailty and sarcopenia. The coexistence of inflammatory and anti-inflammatory phenomena has a negative effect on metabolism, bone density, muscle mass and strength, exercise tolerance, vascular system, cognition and affect, ultimately helping to trigger the fragility phenotype, which frequently coexists with other geriatric syndromes [22; 26; 27; 28; 29]. All these mechanisms significantly increase the surgical risk and worsens the results [22; 30].  
On the other hand, we must not forget other factors potentially associated with worse preoperative nutritional status, such as intestinal obstruction (both by extrinsic and intrinsic compression), psychological suffering of the patient or prolonged hospitalization with fasting by diagnostic tests, among others [15; 16; 21; 31; 32].
## 4 Large Spatial GH Shift Around CPA-LPs  
GH shift is the lateral shift and angular deviation of the reflected beam, relative to the position predicted by geometrical optics [65]. How to design, and find, appropriate structures or materials to increase GH shift is the key problem to be considered at present. With reference to the lattice structure of solid crystals, people put forward the concept of PCs, which provide a new approach to solve the above problem. At the bandgap edge of the PCs, there is large GH shift [66], and the optical field localization of the defect PCs can also induce large GH shift [62].  
The spatial GH shift relates to the phase of the reflection coefficient, and is proportional to the slope of reflection phase in accordance with \(\Delta=-d\varphi_{r1}\), \(r_{2}/dk_{y}\)[65], where \(k_{y}=k\)sin\(\theta\) and \(k=\lambda/2\pi\). For another example, the CPA-LPs are singular points in the phase of the reflection coefficient, which is to say that the phase dislocates at the CPA-LPs. Therefore, a large spatial GH shift may be induced around the CPA-LPs. Figure 6a plots the phase of the reflection coefficient in the parameter space. The focusing region is separated into two parts labeled I and II, respectively. The spatial GH shifts are negative in part I, while it is positive in part II. Except for the CPA-LP\({}_{1}\), the spatial GH shifts approximate to zero along the dotted line. The negative and positive polarities of GH shift convert at the CPA-LP\({}_{1}\), approving the singularity of the GH shift.  
Figure 5: (**a.c**) The phase of reflection coefficient varying with the gain–loss factor and angular frequency for light incident from the left and right, respectively. (**b.d**) The phase curves versus the angular frequency for several specific refractive indices around the CPA-LP\({}_{1}\) for light incident from the left and right, respectively.  
Figure 6b plots the spatial GH shift versus angular frequency for the gain-loss factor \(q=1.875\). One can see that the GH shift is positive, and a peak appears around the CPA-LP\({}_{1}\). Figure 6c gives the GH shift for \(q=1.975\). It shows that there is also a maximum in the curve, but the peak becomes sharper and narrower when the gain-loss factor is closer to \(q_{\rm LP1}\). However, as \(q=2.075\), the spatial GH shift is negative, and there is a valley in the curve shown in Figure 6d. For the three special gain-loss factors, the corresponding maxima of GH shift are \(3.4\times 10^{3}\), \(1.13\times 10^{5}\), and \(-3.25\times 10^{3}\) times of the incident wavelength around \(\omega_{\rm LP1}\). Here, we show that a large GH shift in a bulk multilayer structure and the imaginary part of refractive index play an important role in realizing CPA-LPs of the system. The spatial GH shift is highly sensitive to the refractive index of dielectric and angular frequency of incident light, as shown in Figure 6b-d around the CPA-LP. However, the applications in sense at the exceptional point appear to be rather challenging, such as the effect of fluctuations and noise [67; 68; 69].
## 2 Materials and Methods
### Data Analysis  
#### 2.3.1 Preprocessing  
NIRS brain AnalyzIR toolbox [41] was used for preprocessing of the fNIRS data. The following modules of Version 615 with default settings were used for preprocessing the data:  
* _BaselineCorrection()_ for removing DC-shift and motion correction which detects statistical outliers following an autoregressive integrative model fit of the data,
* _OpticalDensity()_ for calculating \(-\log\frac{raw}{mean(raw)}\), and
* _BeerLambertLaw()_ for applying the modified Beer-Lambert law.  
Before and after preprocessing, the time series were visually inspected to make sure no abundantly noisy or abnormal segments are present in the data.  
The input dataset to our algorithm were the estimated concentration changes of HbO and HbR over time. The time series (52 channels \(\times\) 2 wavelengths) were detrended and bandpass filtered [0.01 Hz-0.18 Hz], in order to retain physiologically relevant components [42] of the fNIRS signal.  
Following preprocessing, the fNIRS trials were split into stim-on and stim-off periods of 20-s duration by using the timestamps of stimulus pulse train. For feature extraction, only the last 10 s of stim-on, and the last 10 s of stim-off were considered. At the \(i\)-th trial, the data \(x_{i}\ \in\ \mathbb{R}^{m\times d}\) from all channels are windowed to 5-s blocks with 80% overlap, and then concatenated, such that \(m=n_{ch}\times n_{s}\), where \(n_{ch}\) and \(n_{s}\) are number of channels and number of samples in each time window, respectively, and \(d\) is total number of 5-s blocks within the \(i\)-th trial. When considering the 80% overlap, there are six 5s blocks within each 10 s period of stim-on and stim-off, which adds up to \(d=12\) blocks per trial. At 7.8 Hz sampling rate, there are \(n_{s}=39\) samples per 5 s block per channel, adding up to 4056 samples for the \(n_{ch}=104\) channels of HbO and HbR per velocity. These blocks are the high-dimensional inputs to the CSP algorithm for dimensionality reduction.
## 4 Correlation Analysis
### Oxford Government Responses and Citizens' Mobility Features
#### 4.1.3 Citizens' Mobility Features--New Deaths  
The following are some findings from the correlation analyses conducted for the endpoint new deaths (Appendix C, table (b)):  
* Oxford government response features: The highest global score level of correlation is 6.85 for the stay-at-home requirement feature (UK and France have levels higher than 0.85 in the first wave). The Spearman level is 6.691 for the workplaces closing feature (the peak is in the UK's first wave, equal to 0.887).
* The level of correlations with the mobility features are notably high. For instance, the global score for the parks feature is \(-\)7.177, for transit stations \(-\)8.466, and for residential 9.737 (0.912 for Italy in the first wave).
* The level of correlation with the new confirmed cases feature is also very high.  
Further discussion regarding the analysis and comparison of the correlation levels assumed in different periods and for other countries can be found in the following two subsections: the first devoted to the \"new confirmed cases\" and the second to the \"new deaths\" endpoint.
## 3 Results and Discussion
### Sequential Batch Photobioreactor Experimentation with Duckweed and microalgae
#### 3.3.2 Tolerance Test of the Microalgae to RW-Based Media Pre-Treated with Duckweed
Moreover, the choice of microalgal species notably influences their tolerance to RW-based media, with _Chlorella_ exhibiting superior performance when compared to _Scenedesmus_ cultured in flasks.
## Abbreviations  
The following abbreviations are used in this manuscript:  
FFE  
Fine feature extraction  
MHAR  
Multi-head self-attention re-embedding  
R-CNN  
Region-based convolutional neural network  
GAN  
Generative Adversarial Network  
SE  
Squeeze and Expand  
MC  
Multi-Scale Convolution  
DSC  
Depthwise separable convolution  
WSPLINs  
Weakly supervised patch label inference networks  
PLIN  
Patch label inference network  
FCN  
Fully convolutional network  
LVE  
Linear viscoelastic  
MLET  
Multi-layered elastic theory  
FWD  
Falling Weight Deflectometer  
GPR  
Ground-Penetrating Radar  
ViT  
Vision Transformer  
Swin-T  
Swin Transformer  
RRT  
Re-embedding region Transformer  
W-MSA  
Window-based multi-head self-attention mechanism  
SW-MSA  
Shifted window-based multi-head self-attention mechanism  
VGG16  
Visual Geometry Group 16-layer network  
FFVT  
Feature Fusion Vision Transformer  
TransFG  
Transformer Architecture for Fine-Grained Recognition  
DMTC  
Dense Multiscale Feature Learning Transformer  
DDACDN  
Deep Domain Adaptation for Pavement Crack Detection  
PicT  
Pavement image classification Transformer
## 2 Materials and Methods
### HyPlant Processing Chain  
The HyPlant processing chain consists of a number of steps that enable the processing of hyperspectral image cubes recorded by the DUAL and FLUO module from raw data to final products (i.e., top-of-canopy (TOC) radiance and TOC reflectance, reflectance indices and fluorescence maps). The processing chain can be organized into four main clusters. The first cluster describes the transfer of raw data, associated navigation and header files, and calibration data to the two separated processing lines for the DUAL and FLUO module. The second cluster illustrates the processing of DUAL raw data into the final products. The processing of the FLUO data is divided into two clusters. While the third cluster describes the processing from FLUO raw data up to at-sensor radiance, in the fourth cluster, four different SIF-retrieval methods are described, which can be used to generate SIF maps. The following sections provide a detailed overview of the different processing clusters, which, together, constitute the complete HyPlant processing chain (Figure A1).
## 4 Results and Discussion
### Results on Computer Vision Tasks
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Proposed Method** & **FHE** & **FL** & **DP** & **DP + FL** \\ \hline FasterRCNN & 0.79 & 0.73 & 0.70 & 0.69 & 0.67 \\ SSD & 0.81 & 0.75 & 0.72 & 0.71 & 0.69 \\ YOLO & 0.85 & 0.78 & 0.76 & 0.74 & 0.72 \\ EfficientDet & 0.88 & 0.80 & 0.78 & 0.76 & 0.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of recall on computer vision tasks for different methods. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Proposed Method** & **FHE** & **FL** & **DP** & **DP + FL** \\ \hline FasterRCNN & 0.80 & 0.75 & 0.72 & 0.71 & 0.69 \\ SSD & 0.83 & 0.76 & 0.75 & 0.73 & 0.71 \\ YOLO & 0.86 & 0.80 & 0.78 & 0.76 & 0.74 \\ EfficientDet & 0.89 & 0.81 & 0.80 & 0.78 & 0.76 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of accuracy on computer vision tasks for different methods. data processing and accurate output. For example, in FasterRCNN, the RPN-based region proposal mechanism effectively locates target areas, and our method enhances this by introducing user privacy weights during region proposal generation, significantly improving precision and recall. SSD benefits from multi-scale detection, and our method amplifies this advantage by adding differential privacy protection and user attention mechanisms at each feature map layer, ensuring high precision and high recall. YOLO, as a single-stage detection model, balances speed and accuracy, and our method improves precision and recall by dynamically adjusting attention weights and incorporating differential privacy protection at each layer of the YOLO network, protecting sensitive user information. EfficientDet's optimized network structure and parameter settings allow it to perform exceptionally well when applying our method with multi-level privacy protection mechanisms and dynamic attention adjustments resulting in superior performance across all metrics. Overall, the experimental results and theoretical analysis demonstrate that the proposed user-centered data privacy protection framework effectively protects user privacy and significantly improves model performance in various computer vision tasks. This indicates the framework's broad potential and practical value in real-world applications.
# A Resilient LoRa-Based Solution to Support Pervasive Sensing
## 4 Our Proposed Solution  
Our solution employs a mesh network with no defined hierarchy, where all nodes work along with each other to maintain the network and deliver messages across it. To support this network, we have developed a comprehensive set of solutions that are summarized in the following.  
* The network is self-initialized and self-maintained through **synchronization among nodes**. This also allows to perform certain actions at specific times, like polling a connected sensor every few minutes and creating predetermined receive windows (i.e., a node will transmit only when the destination will be listening). This feature can limit data loss, retransmission, and power consumption.
* Our **routing protocol** enables devices to communicate with each other even beyond the transmission range, with messages forwarded along a specific (multi-hop) route.
* We implemented an **adaptive spreading factor** that automatically adjusts the spreading factor used by the nodes in the network in order to keep the connection between devices stable and limit errors during message exchanges.  
To allow message exchange between devices, we used LoRaCTP, a MicroPython library that allows the transfer of blocks of bytes over a LoRa channel [27]. This library provides mainly two functions, one to send and one to receive data between two nodes. These functions use acknowledgments and retransmission packets to ensure messages are correctly received. Along the base sends and receives, we created two more functions: one is a broadcast function that allows us to have a device send a message to more than one node, and the other one is a receive function that uses timeouts to limit the listening window of devices (the original receive function will keep listening until a message is received, potentially blocking a device indefinitely) and can support both unicast and broadcast messages (when a broadcast message is received, no acknowledgments are sent).  
Figure 2 shows a simplified flow chart for our solution.
## 3 Proposed Sensor Fusion Framework Model Using PDR and W-Fi Localization Systems
### Proposed Sensor Fusion Framework Algorithm using Wi-Fi Fusion and PDR Position Results  
The proposed sensor fusion framework as well uses the advantages of the LKF for combining the Wi-Fi fusion and PDR position results. As compared to other sensor fusion frameworks such as EKF, complementary EKF, UKF and Markov model [45], the LKF is simple and easy to use for the fusion process. In our experiment scenarios, we tackled the problem formulation in the linear perspective and the LKF is the best option for combining the user position results. The proposed sensor fusion framework uses the same LKF implementation explained in Figure 5 for Wi-Fi and PDR systems fusion. The LKF is a recursive filter that estimates the state in a linear system. The estimation in LKF is repeated to minimize errors from inaccurate measurements and observations and optimum performance can be achieved when the noise is Gaussian noise. The LKF implementation consists of two phases, prediction and correction as illustrated in Figure 5.  
The position results from Wi-Fi and PDR are fused by LKF filter and the results from LKF shows better performance than individual systems. In the LKF prediction phase, the state variable \(\hat{x}_{k}\) and error covariance matrix \(P_{k}\) are estimated using state transition matrix \((A)\). It also predicted the corrected state \(\hat{x}_{k-1}\) and the error covariance \(P_{k-1}\) at previous time. The Gaussian noise \((Q)\) is used for error covariance prediction. In the correction phase, the Kalman gain represents the reliability of the predicted value and the measured value. The larger the Kalman gain, the more reliable the measured value, and the smaller the Kalman gain, the more reliable the predicted value. The final output value \(P_{k-1}\) of the LKF is determined according to the Kalman gain.