esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Experiments and Results
### Experiment 4: Ablation Study on the Final Segmentation Pipeline  
We have performed the ablation of a varying number of convolutional filters at all stages of the 3D V-Net architecture after training. We have found that certain stages are more significant than others for the quality of veins segmentation.  
The V-Net is divided into several stages (the stages are shown in Figure 9) that operate at different resolutions during the down- as well as upsampling phases. Each stage consists of one to three convolutional layers, with each layer having a specific number of channels (filters). We ablated groups of similar filters at every stage by increasing the percentage  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{**Loss Functions**} & \multicolumn{4}{c}{**3D V-Net**} \\ \cline{2-5}  & **DSC** & **ACC** & **SEN** & **PRC** \\ \hline Dice Loss & \(75.4\pm 4.3\) & \(99.3\pm 0.1\) & \(76.9\pm 4.1\) & \(74.4\pm 7.8\) \\ Dice-BCE Loss & \(75.9\pm 3.5\) & \(99.3\pm 0.1\) & \(75.5\pm 3.1\) & \(76.8\pm 7.1\) \\ Log Cosh Dice Loss & \(74.6\pm 3.3\) & \(99.3\pm 0.1\) & \(73.5\pm 4.2\) & \(76.3\pm 7.4\) \\ Focal Loss & \(74.4\pm 3.7\) & \(99.3\pm 0.1\) & \(70.2\pm 4.0\) & \(79.6\pm 8.0\) \\ Dice Focal Loss & \(75.2\pm 4.3\) & \(99.3\pm 0.1\) & \(75.2\pm 4.3\) & \(75.6\pm 7.0\) \\ Tversky Loss & \(76.0\pm 4.2\) & \(99.3\pm 0.1\) & \(75.7\pm 3.9\) & \(76.8\pm 7.1\) \\ Weighted Dice Loss \(\beta_{l}=6\) & \(\mathbf{76.2}\pm\mathbf{3.7}\) & \(\mathbf{99.3}\pm\mathbf{0.1}\) & \(75.0\pm 3.4\) & \(\mathbf{77.7}\pm\mathbf{6.1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of different loss functions tested on the improved 3D-IRCADb dataset and the 3D V-Net architecture.  
Figure 11: The average dice score for different blending ratios between the RORPO and the original image while using 3D U-Net and 3D V-Net.  
(25%, 50%, and 75%) of similar filters. Since the number of filters varies from stage to stage, it means that the same percentage may correspond to a different number of ablated filters. The similarity between filters within a group was calculated based on the absolute Euclidean distance of the normalized filter weights. Ablations were performed by manually setting the weights and biases of all incoming connections of a filter to 0, thus eliminating any activation of that filter.  
Figure 12 shows the dice scores for ablations of 25%, 50%, and 75% at every stage of the V-Net. Each data point shows the effect of a specific stage ablation. The dice score is affected noticeably more in stages 'in 16', 'down 64', and 'up 64', than in the other stages. Furthermore, the effect of the largest ablation has a stronger influence on some stages than on others. For instance, stage 'up 32' shows a noticeably larger drop in dice score for 75% of the ablated filters, compared to 50%, while the stages 'down 256' and 'up 256' are largely unaffected.
## 3 Results  
### Conditions for Detecting Weak Tumors  
Growth of tumors at the stage of exceeding its size by several millimeters is possible only if a small capillary network with increased blood flow is formed around it [103; 104; 105]. Therefore, the tumor focus can be quite a powerful source of heat in the breast. Malignant neoplasms often have extremely high heat release rates relative to other biological components, especially in the early stages of disease development (see Figure 4).  
We considered separately the problem of detecting weak tumors, which are characteristic of the initial stage of the disease and are poorly distinguished by traditional methods such as mammography. Special datasets were constructed for simulations containing tumors of different radii (\(R=1\) cm, \(R=0.75\) cm, \(R=0.5\) cm) at \(Q^{(can)}=3\times 10^{4}\) W m\({}^{-3}\). Figure 16 shows the effect of tumor size in models with the same internal structure of the breast, when only the value of \(R\) changes and the tumor is located on the axis of symmetry passing through the nipple. Even such simple direct images indicate the difficulty of detecting weak tumors. IR data on the temperature of the skin surface give a poorer opportunity to detect tumors (see Figure 16).  
The brightness temperature \(T_{B}\) is not a local characteristic, as it is calculated as an integral of some volume (see (12)). The thermodynamic temperature \(T\) is more sensitive to small-scale tissue inhomogeneities and heat sources. Figures 17 and 18 show temperature distributions (\(T\)) from the nipple to the sternum in different models, which clearly indicate the tumor presence even at \(R=0.35\) cm, but the contribution of such local formations to the brightness temperature is small even at \(Q^{(can)}=3\times 10^{4}\) W m\({}^{-3}\). Colder small tumors at \(Q^{(can)}<2\times 10^{4}\) W m\({}^{-3}\) are not detected at all, as the integral heat power from the tumor is proportional to \(Q^{(can)}\times R^{3}\).  
Figure 16: Brightness and infrared temperature distributions of three models with different tumor sizes: (**a**,**e**) \(R=1\) cm; (**b**,**f**) \(R=0.75\) cm; (**c**,**g**) \(R=0.5\) cm. The models in panels (**d**,**h**) do not contain tumors.  
Figure 17: Distributions of thermodynamic temperature over depth for tumors of different radii \(R\) at \(Q^{(can)}=3\times 10^{4}\) W m\({}^{-3}\): (**a**) \(R=1\) cm, (**b**) \(R=0.75\) cm, (**c**) \(R=0.5\) cm, (**d**) \(R=0.35\) cm.
## 4 Discussion
### Ability of an Open-Source Sensing System to Classify Behaviors
The models derived based on thermoneutral data only (20 \({}^{\circ}\)C) had notable reductions in accuracy and extremely poor precision for nearly all behaviors when evaluated on data obtained from 27 \({}^{\circ}\)C and 35 \({}^{\circ}\)C. Lying behavior was the most resilient, with marginally improved accuracy and acceptable precision when evaluated against data from 27 \({}^{\circ}\)C, likely due to the fact that the time budgets for lying behavior were similar between 20 \({}^{\circ}\)C and 27 \({}^{\circ}\)C. The models derived from data at 27 \({}^{\circ}\)C and 35 \({}^{\circ}\)C also performed poorly when compared against data obtained from other thermal environments, with several behavioral evaluations yielding accuracies of \(\leq\)50% and precision of 0. Collectively, this exploration shows a critical limitation of precision technologies leveraging machine learning to translate sensor data to behavioral classification. The training data for these algorithms requires the demonstration of a wide array of expected production conditions to help ensure adequate behavioral classification among those conditions. For example, many evaluations of accelerometers explain details about the animals, their housing, the time of day, and other important methodological aspects of data collection, but they rarely refer to other environmental descriptors, such as the temperature. Similarly, longitudinal analyses of precision technologies focus predominantly on overall classification performance and fail to specifically investigate systematic errors in prediction, such as those which may be driven by failing to properly account for behavioral budget shifts during altered thermal environments. As the body of the literature leveraging commercial and open-source behavioral sensing technologies expands and matures, efforts to collate data among tools, and associated meta-data, may be essential to help better understand how methodological and environmental factors (such as age, breed, housing system, thermal environment, feeding schedules, group size, etc.) can be controlled for help develop robust behavioral classification techniques which are applicable across a broader array of potential production contexts.
# Experimental Validation of GNSS Interferometric Radio Occultation
## Appendix A Local Oscillator Offset
\begin{array}{rcl}\mathbf{R}\left(\tau\right)&=&\int_{0}^{T}\mathbf{A}\left(t+ \tau-u\left(t+\tau\right)\right)\mathbf{A}^{*}\left(t-u\left(t\right)\right)\\ &&\cdot e^{-j2\pi f_{\mathrm{off}}\tau}\cdot e^{j2\pi f_{\mathrm{c}}\left[u_{ \mathrm{x}}\left(t\right)-u_{\mathrm{x}}\left(t+\tau\right)\right]}\cdot e^{j2 \pi f_{\mathrm{off}}t}\mathrm{d}t\end{array}  \tag{10}\]  
The phase terms inside the integral do all change very little during the integration interval of length \(T\). We can, thus, approximate their values by the values they have at the beginning of the integration period. This gives the following result, where we take all complex exponentials out of the integral. Note also that for GPS C/A signals the envelope is real valued. This means that the outcome of the integral will always be real-valued, and does not carry any phase information. \[ \begin{array}{rcl}\mathbf{R}\left(\tau\right)&=&e^{-j2\pi f_{\mathrm{off}} \tau}\cdot e^{j2\pi f_{\mathrm{c}}\left[u_{\mathrm{x}}\left(t\right)-u_{ \mathrm{x}}\left(t+\tau\right)\right]}\\ &&\int_{0}^{T}\mathbf{A}\left(t+\tau-u\left(t+\tau\right)\right)\mathbf{A}^{* }\left(t-u\left(t\right)\right)\mathrm{d}t\end{array}  \tag{11}\]  
The argument of the correlation is  
\[\arg\left(\mathbf{R}\left(\tau\right)\right)=-2\pi f_{\mathrm{off}}\tau+2\pi f _{\mathrm{c}}\left[u_{\mathrm{x}}\left(0\right)-u_{\mathrm{x}}\left(\tau\right)\right] \tag{12}\]  
We can make a second correlation measurement at a later lag, \(\tau+T\), and proceeding as previously the phase of this new correlation value is  
\[\arg\left(\mathbf{R}\left(\tau+T\right)\right)=-2\pi f_{\mathrm{off}}\tau-2 \pi f_{\mathrm{off}}T+2\pi f_{\mathrm{c}}\left[u_{\mathrm{x}}\left(0\right)- u_{\mathrm{x}}\left(\tau\right)\right] \tag{13}\]  
Taking the difference of the phases of both measurements we obtain the phase measurement that is related to the Doppler frequency  
\[\phi=\arg\left(\mathbf{R}\left(\tau+T\right)\right)-\arg\left(\mathbf{R} \left(\tau\right)\right)=2\pi f_{\mathrm{c}}\left[u_{\mathrm{x}}\left(0\right) -u_{\mathrm{x}}\left(\tau\right)\right]-2\pi f_{\mathrm{off}}T \tag{14}\]  
The Doppler frequency is estimated with  
\[\hat{f}_{d}=\frac{\phi}{2\pi T}=\frac{f_{\mathrm{c}}\left[u_{\mathrm{x}} \left(0\right)-u_{\mathrm{x}}\left(\tau\right)\right]}{T}-f_{\mathrm{off}}=f_ {\mathrm{d}}-f_{\mathrm{off}} \tag{15}\]  
Thus the frequency difference in the estimation of the Doppler is  
\[\Delta f_{\mathrm{d}}=-f_{\mathrm{off}} \tag{16}\]  
We see that any local oscillator frequency offset that is not considered in the phase model \(\theta\left(t\right)\) translates directly into the estimated Doppler frequency. If this frequency offset is estimated or known at a later stage, the correction of the Doppler frequency can be done straightforward from Equation (15). This is the case with the dataset collected in this work, where the SPIR receiver provides accurate reference frequency estimates from which the actual local oscillator frequency can be obtained.
## 3 Security and Privacy Challenges
### AI Role in EC Security
#### 3.3.3 Multi-Access Edge Computing  
Cloud computing capabilities are brought to the edge servers or nodes in a Multi-access Edge Computing (MEC) network shown in Figure 12. MEC exists between the central cloud servers and edge nodes primarily for managing and processing huge amounts of raw data generated from IoT edge devices [171]. It comprises four functional layers, i.e., end devices or hosts, access network, edge network, and core infrastructure. The hosts are connected to the access network, serving as the connection between the functional layers and the Internet. Radio access networks (RANs) establish a connection between the hosts and the remainder of an operator's network. MEC has the potential to improve the quality of service by reducing the end-to-end latency between the edge nodes and data processors, as well as improvement in data security and privacy. MEC also fosters data encryption, authentication, and access control at the edge, thus ensuring authorized access and processing of the data. MEC is deployed either by Mobile Network Operators (MNOs) or by private cloud service providers closer to end customers and has less latency and higher availability [172]. Previous research works focused on resource allocation algorithms rather than ensuring the security of MEC servers and end devices. Of late, limitations of mobile devices and support for resource-intensive applications were introduced by Mobile Cloud Computing (MCC). MCC supports extended battery lifetime, unlimited storage on demand, improved processing capability, and self-service provisioning.  
Due to the distributed, small-scale MEC infrastructure, there is less concentration of significant data, thus there is less chance of security and privacy-related attacks. Also, there is a possibility that MEC servers are owned privately, which eases data privacy concerns. For example, the enterprise deployment of MEC skips uploading of users' classified data to remotely located datacentres, as the enterprise administrator manages the authorization, access control, and classifies different levels of service requests at its discretion without involving external parties [173]. MEC can introduce newer classes of services, but its unique characteristics open new types of security and privacy challenges. A huge amount of heterogeneous data generated at IoT edge nodes aggregated, stored, transmitted, and utilized in MEC networks may suffer data leakage incidents [174].
## 5 Movement Control Algorithm Verification Using Eccentricity  
### Movement Control Algorithm Simulation  
In order to verify the proposed movement control algorithm using eccentricity, the algorithm and the magnetic levitation system are modeled using MATLAB/Simulink as shown in Figure 14. The magnetic levitation system was configured in the form of 24-pole 72 slots, and the system algorithm was implemented in the configuration of 4C1M in which each controller was connected to four divided stators.  
For example, the X-axis movement command (X_trans_ref) converts the current X-axis position value (\(POSm\_x\)) and the difference into an error value (X_err) and applies it as the position controller's command (\(F_{x}\)). The applied position control command is converted into the current command (\(loz1*\), \(loz3*\)) of the stat  
Figure 13: Block diagram of position and velocity control system algorithm using PID double loop.  
current command is input to the controller through coordinate conversion, output as a voltage signal to the inverter, and drives the magnetic levitation system.  
The following figures show the simulation results of the command and output values for rotation and movement control using the magnetic levitation system algorithm modeling.  
Figure 15a shows the three-phase current of the stator part A generated from the floatation and rotation control of the magnetic levitation system. The weight of the rotor was simulated based on the 1.33 kg of the actual test model. As for the rotation speed command, 50 rpm was applied as shown in Figure 15b, and the levitation command was inputted as 0.2 mm. In addition, 0.05 mm was input for the X-Y axis movement command, and the output characteristics that converge the command values were confirmed as shown in Figure 16.  
Figure 14: Modeling of maglev system control algorithm using MATLAB/Simulink.  
Figure 15: Current and speed output using magnetic levitation system algorithm: (**a**) simulated 3-phase current phase of Part A stator from standstill to 50 rpm operation; (**b**) simulated command speed 50 rpm and output speed of magnetic levitation system.
## 2 Materials and Methods
### Study Area and Data Processing  
#### 2.2.1 Study Area  
As shown in Figure 1a, Wanglang National Nature Reserve (103\({}^{\circ}\)50\({}^{\prime}\)-104\({}^{\circ}\)58\({}^{\prime}\)E, 32\({}^{\circ}\)49\({}^{\prime}\)-33\({}^{\circ}\)02\({}^{\prime}\)N, referred to as Wanglang Reserve in the following) is located at the intersection of the 800 mm rainfall line, the boundary of China's first Ladder, and the ridge of Qinling mountain, which is a typical mountainous region in southwest China. As shown in Figure 1c, the total area of the Wanglang Reserve is approximately 320 km\({}^{2}\), and includes evergreen needleleaf forest (ENF, 27.10%), deciduous broadleaf forest (DEF, 1.42%), mixed forest (MF, 11.85%), shrubland (SHR, 6.66%), grassland (GRA, 25.11%), and other lands (27.86%).  
In 2017, an integrated observation and experiment station for mountain ecological remote sensing (referred to as Wanglang station in the following) was established in the Wanglang Reserve (Figure 1b). The main reasons for selecting the Wanglang Reserve to carry out research related to mountain ecological remote sensing are that (1) the topographic features of Wanglang Reserve are heterogeneous (Figure 1d-f), with elevation, slope, and SVF ranges of 1699-4914 m, 0-79\({}^{\circ}\), and 0.15-1.00, respectively, and (2) the location of Wanglang Reserve is important for research associated with global climate change. Wanglang station mainly contains three observation systems (as shown in Supplementary Figure S1), including a shrubland tower with a height of 10 m, an MF tower with a height of 30 m, and an ENF tower with a height of 75 m.  
As shown in Figure 2, during Day Of Year (DOY) 1-273 in 2020, the annual average air temperature, soil temperature, incoming solar radiation, precipitation, and VPD over Wanglang Reserve were 5.52 \({}^{\circ}\)C, 8.22 \({}^{\circ}\)C, 152.63 w m\({}^{-2}\), 639 mm, and 2.72 hpa, respectively. In general, the seasonal characteristics of Wanglang Reserve can be mainly described as (1) dry season (i.e., from November to April of the next year) with abundant sunshine, low rainfall, low air humidity, and low temperature, and (2) wet season (i.e., from May to October) with high-frequency cloudiness, concentrated heavy rainfall, high air humidity, and high temperature. Over Wanglang Reserve, the phenomenon that the simultaneous appearance of high heat, heavy rainfall, and significant diurnal temperature difference in the wet season, maximally promotes vegetation growth, and is conducive to the accumulation of vegetation biomass.
## 4 Results and Discussion
### Determination of the Rotating Speed of the Rotary Base  
The trimming operation was performed when the rotary base was commanded to turn at different speeds. Taking the consumed time and similarity as the evaluation indexes, the results of the experiments were calculated and are listed in Table 4 as follows.  
From Table 4, the trimming operation under all rotating speeds could be completed with desirable performance, similarities all above 96%. However, during the experiments, it was noticed that when the rotating speed of the rotary base was too fast, larger than 13.5 r/min, the branches missed by the cutter units increased and more circles were required to complete a desirable trimming. A speed lower than 13.5 r/min could also result in a good trimming effect, but the time consumed increased. Considering both efficiency and performance, the rotating speed of the rotary base was determined at 13.5 r/min, and the trimming operation was to be completed in two circles with a corresponding time consumption of 8.89 s.
## 3 Methodology
### Adjustment of the UFPA Model through Genetic Algorithm (GA)  
Empirical propagation models are based on extensive measurement campaigns to establish the average behavior of the propagated signal in a given environment, considering parameters that influence signal propagation such as distance and height between transmitters and receivers, frequency of operation, etc.  
Each model has an operating range determined for each parameter based on the measurements carried out in its design. This way, a model operates with a minimum and maximum distance and height and in a specific frequency range. Another aspect that should be highlighted is the path loss exponent, which indicates that the signal decay factor in certain types of environments (urban, suburban, rural) varies according to the morphological characteristics of the studied environment.  
Therefore, adjustments may be needed to use a specific model because it is outside a particular parameter established in its design.  
The chosen path loss model operates at a frequency of 5.8 GHz. To achieve this, adjustments are necessary in the frequency range of 5.8 Ghz to 915 MHz. Using the data obtained in the measurements, distance values, and RSSI, it was possible to optimize parameters \"k1\", \"k2\", \"a\", and \"b\" in Equation (3). The bio-inspired GA technique was implemented to minimize the error between the UFPA propagation model and the measured data. That way, it was adjusted to follow the trend of the measured data, thus making the positioning of the gateways more accurate [19].
# Static Evaluation of a Midimew Connected Torus Network for Next Generation Supercomputers
## 5 Cost Effectiveness Analysis of MTNs
### Packing Density  
In order to show that MTMNs could be used for very large-scale integrations (VLSIs), the packing density was measured. This was the ratio between the total number of nodes and the cost, which indicated that a higher value for packing density required less area for the VLSI chip. The packing density of the MTN was evaluated using Equation (6).  
\[packing\:density=\frac{total\:nodes}{C} \tag{6}\]  
The packing density of the MTN was better, as compared to the higher values found in torus, TESH, TFBN, and MMN. The MTN was almost similar to mesh, with a slightly higher result.
# Hybrid Techniques for the Diagnosis of Acute Lymphoblastic Leukemia Based on Fusion of CNN Features
###### Abstract  
Acute lymphoblastic leukemia (ALL) is one of the deadliest forms of leukemia due to the bone marrow producing many white blood cells (WBC). ALL is one of the most common types of cancer in children and adults. Doctors determine the treatment of leukemia according to its stages and its spread in the body. Doctors rely on analyzing blood samples under a microscope. Pathologists face challenges, such as the similarity between infected and normal WBC in the early stages. Manual diagnosis is prone to errors, differences of opinion, and the lack of experienced pathologists compared to the number of patients. Thus, computer-assisted systems play an essential role in assisting pathologists in the early detection of ALL. In this study, systems with high efficiency and high accuracy were developed to analyze the images of C-NMC 2019 and ALL-IDB2 datasets. In all proposed systems, blood micrographs were improved and then fed to the active contour method to extract WBC-only regions for further analysis by three CNN models (DenseNet121, ResNet50, and MobileNet). The first strategy for analyzing ALL images of the two datasets is the hybrid technique of CNN-RF and CNN-XGBoost. DenseNet121, ResNet50, and MobileNet models extract deep feature maps. CNN models produce high features with redundant and non-significant features. So, CNN deep feature maps were fed to the Principal Component Analysis (PCA) method to select highly representative features and sent to RF and XGBoost classifiers for classification due to the high similarity between infected and normal WBC in early stages. Thus, the strategy for analyzing ALL images using serially fused features of CNN models. The deep feature maps of DenseNet121-ResNet50, ResNet50, ResNet50-MobileNet, DenseNet121-MobileNet, and DenseNet121-ResNet50-MobileNet were merged and then classified by RF classifiers and XGBoost. The RF classifier with fused features for DenseNet121-ResNet50-MobileNet reached an AUC of 99.1%, accuracy of 98.8%, sensitivity of 98.45%, precision of 98.7%, and specificity of 98.85% for the C-NMC 2019 dataset. With the ALL-IDB2 dataset, hybrid systems achieved 100% results for AUC, accuracy, sensitivity, precision, and specificity.  
CNN; RF; XGBoost; ALL; PCA; hybrid method +
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