esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology and Implementation
### Research Design
#### 3.1.1 Data Collection  
Our application is configured to collect anonymized usage data automatically. This data collection occurs each time a user performs any of the selected actions, namely (i) generation of sentences and (ii) creation, edition, or deletion of pictograms/images. For this purpose, it was necessary to establish a cloud storage system to collect this information in real time. In order to respect users' privacy, no personal data are collected, and all registered events are automatically anonymized.  
Figure 4: Architecture of the study.  
Each user who generates at least one event with the application has a record in the database, with a unique identifier present in every registered action. The identifier, provided by the Android api, does not have any relation to the real information of the user, so no personal data are compromised. Additionally, we track the profile names registered by the application in order to sort different users. This allows us to distinguish between different people using the same device. Nonetheless, this field is equivalent to an identifier, and it is not used for the analysis. This record also contains the country from which such events are generated for statistical purposes. Depending on the type of action executed, the application provides different values. Every action is accompanied by its timestamp referencing local time.  
For generation events, the app registers the final sentence, the mode of generation (punctual or cumulative) that was used, the language or languages chosen for the application, and the voice synthesizer (as they might be different). For the cumulative mode, the app registers the final sentence after the nlg phase, if available. For new pictograms or images, it records the item's name, the source of the image that represents it, the application's language and mode, and the categories to which the new picture belongs, if applicable. The latter also applies to deletion events, ignoring the source of the original image. Finally, the app registers the update of those elements whenever they change the associated written or oral text, as this is the meaningful part of the communication feature.
# Physical Side-Channel Attacks on Embedded Neural Networks: A Survey
## 4 Physical SCA Attacks on DNNs
### Recovering Network Architecture and Parameters  
#### 4.2.1 Micro-Controller Targets  
**EM side-channels:** [PERSON] et al. [20] presented an attack that was able to reverse engineer the network architecture and recovered key parameters such as the pre-trained weights and activation functions as well as the number of layers and neurons per layer through the analysis of EM signatures. The entire model architecture and training data set are considered private and only tested inputs and outputs that are known by the attacker (black-box). The authors targeted a pre-trained model of an MLP consisted of three layers with different number of neurons per layer and a CNN model operating on real numbers introduced previously [26] that consists of three convolution layers, three pooling layers and one fully connected layer. The authors use MNIST [27] and CIFAR-10 [28] data sets (for MLP and CNN models, respectively) and conducted their experiments on two micro-controllers, which are Atmel ATmega328P and ARM Cortex-M3 32-bit. First, the authors analysed the EM side-channel signatures of the most commonly used activation functions (ReLU, sigmoid, tanh and softmax) by randomly varying the input within a certain range ([-2,2]). From the EM traces, the authors measure the timing of the activation function and show that these functions result on very different computing times for the same inputs, which are easily distinguishable. This enables the straightforward deduction of the function that had been used and ReLu was the fastest. Second, the proposed attack targets the data bus when loading, from memory, the secret pre-trained weights and exploits the characteristic of data buses in micro-controllers of pre-charging to all '0's before every instruction. This allows the observation of the new EM leakage signature after each regular load of the weights and permits modelling based on the weight's Hamming Weight (HW). The authors target the multiplication \(m=input\cdot weight\), where the \(input\) is known by the attacker, to deduce the secret \(weight\). Following a CEMA-based method, they correlate the activity of the predicted \(m\) for all hypotheses of the \(weight\) to distinguish the correct weight value among their hypotheses. To limit the problem, the authors assumed that the weight will be bounded in a certain range chosen by the attacker. The authors recover the mantissa bit of the weight before recovering separately the sign and exponent bits. These latter two further narrow the candidate list and enables them to obtain an actual value of the weight. The weights were recovered with small precision errors (at third place after decimal point). Finally, by using SEMA on MLP (three layers, six neurons and sigmoid activation function), the authors were able to easily distinguish multiplication operations and activation functions to count the number of neurons. This is, however, not possible for deeper NNs. The authors then proposed a CEMA-based technique to identify layer boundaries on CNNs.
## 4 Results and Conclusions  
Seven children with AD (three males (43%) and four females (57%)) were treated with duplilumab during the reference period. Of these, three were also affected by severe asthma. Patients' demographic and clinical baseline characteristics are reported in Table 1. From the data obtained, a reduction in the EASI score, SCORAD, P-NRS, S-NRS and c-DLQI was observed at 12 weeks of treatment with duplilumab in all patients. These successful clinical results also led to an improvement in the quality of sleep, with better school performance and social relationships. In all cases, we recorded a reduction in the value of IgE. Additionally, we observed a lower use of SCs and TCs. In the three asthmatic children, despite stable spirometric values before and after duplilumab, there was a general improvement in symptoms and a reduction in the use and/or dosage of ICS. Indeed, in one of them, after 3 months of duplilumab, the administration of antihistamines was suspended (case 1); in another one, LABA was suspended (case 4); in case 5, the patient continued to use antihistamines and ICS-LABA but at a lower dosage. Of the seven patients, three of them experienced mild side effects: case 6 and case 1 developed a mild conjunctivitis (this later developed into headache as well), successfully treated with topical TCs, and one patient (case 4) developed a mild and transient injection site reaction. In only one case, we recorded a marked increase in the eosinophil count, as also reported in the literature [18]. Asymptomatic SARS-CoV-2 infection occurred in one child (case 5), but he regularly continued duplilumab treatment, as suggested by the guidelines [19]. None of the patients discontinued duplilumab. Our data seem to confirm the effectiveness of duplilumab in both AD and asthma in children, in terms of a reduction in the extent and severity of skin lesions, intensity of symptoms, sleep, QoL and a general increase in asthma control, with a good safety profile. These encouraging results stimulate us to expand the use of duplilumab in atopic children. In this evolving field, [PERSON] et al. [20] recently conducted the first randomized, double-blind, placebo-controlled, phase three trial involving 162 patients aged six months to six years, with moderate-to-severe AD. The authors showed a significant reduction in AD signs and symptoms, in terms of the Investigator Global Assessment (IGA) and EASI-75 without significant differences in AEs between duplilumab and placebo groups, except for a higher incidence of conjunctivitis in the duplilumab group compared to the placebo group (5% vs. 0%). Due to this pivotal trial, duplilumab is the first ever biologic medicine for people living with AD from infancy to adulthood. Considering its good safety profile and effectiveness, duplilumab has the potential to alleviate the multidimensional burden that asthma and moderate-to-severe AD place on infants, toddlers and their families [11; 21].
## 3 Theory
### Body Movement Removing  
The movement of the chest due to breathing can be considered a type of micro-motion. The human target therefore remains in the same range cell. Any body movement or twitch during recording may cause a great distortion in the breathing signal. A random body movement, e.g., a change in the position during sleep, causes a macro-motion. If this occurs, the human target falls in several range cells. During such intervals, the respiration rate may be barely detectable. Before identifying the respiration signal, the artifacts caused by body movements are removed using a threshold method. The motion is detected using the time delay that maximizes the received signal after clutter removing algorithm:  
\[\tau_{m}(t)=\arg\max_{\tau}\,y(\tau,t) \tag{12}\]  
The human range is estimated as: \(d(t)=c\tau_{m}(t)/2\). The sliding standard deviation using a moving window of length \(L=30\) (about 4 s) is then computed:  
\[\sigma_{d}(n)=\sqrt{\frac{1}{L}\sum\limits_{i=-L+1}^{n}{d^{2}(i)-\left(\frac{1 }{L}\sum\limits_{i=-L+1}^{n}{d(i)}\right)^{2}}} \tag{13}\]  
If the moving deviation (13) is higher than a threshold distance, it is considered a movement by the patient, and these samples are discarded and not used for the estimation of the breathing rate. Since the chest displacement is typically smaller than 2.5 cm, the threshold distance is chosen equal to 2.5 cm.
# An Efficient Location Verification Scheme for Static Wireless Sensor Networks
## 5 Security and Performance Analysis
### Neighbor Table Management Overhead  
For the MSRLV, a sensor in the network should manage the neighbor table in its memory. This could be an overhead when considering the limited memory of sensors. A neighbor table consists of the sensor ID, coordinates, a random value and RSSI. Each of the items takes up 2, 16, 8 and 8 bytes, respectively. Thus, total memory usage for maintaining one neighbor in the neighbor table is 34 bytes. Since the number of neighbors will be determined by the sensor density of the network, 0-10 sensors will be around a sensor when the total number of sensors in the network varies from 100-700. This leads to 0-340 bytes of memory consumption in each sensor. Considering the memory or storage capacities of sensors in the real world, the size of the neighbor table can be easily accommodated. Table 4 describes the supported memory size in each sensor product [26] and the required memory size for the location verification.  
Figure 12: Cost for location verification of a single sensor: (**a**) communication cost for each scheme and (**b**) computation cost for each scheme.
## 3 Results and Discussion
### Segmentation Results  
The performance of the segmentation method differentiated for each patterns is shown in Table 5.  
The average Dice index, obtained by averaging the result of Dice on all analyzed images (95 images), was equal to 81.1%.  
It is easy to see that, in spite of the remarkable diversity of the patterns analyzed, the method achieved very similar segmentation results for the different patterns, demonstrating a good robustness of the proposed method. The cell segmentation method presented in this paper has been compared with other segmentation methods of HEp-2 images that have been proposed in recent years. The performance comparison, in terms of the average Dice index, is shown in Table 6.  
It should be noted that it was possible to make a comparison with those studies that presented the Dice index as a figure of merit, or for which it was possible, from the data presented, to obtain the aforementioned index.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Images Dataset** & **Average Dice Index** \\ \hline [PERSON] et al. [23] & 196 & 88.9\% \\ [PERSON] et al. [58] & 28 & 62.1\% \\ [PERSON] et al. [59] & 22 & 86.8\% \\ [PERSON] et al. [60] & 28 & 56.8\% \\ Our method & 95 & 81.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparative performance of segmentation methods on the IIF images.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Images Dataset** & **Sensitivity** & **Specificity** & **Accuracy** & **A\({}_{\text{Z}}\)** \\ \hline [PERSON] et al. [22] & 71 & – & – & 85.7\% & – \\ [PERSON] et al. [24] & 1006 & – & – & 85.5\% & – \\ Our method & 2080 & 92.9\% & 70.5\% & 87.0\% & 91.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative fluorescence intensity analysis.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Pattern** & **DICE Index** \\ \hline homogenous & 85.6\% \\ Speckled & 86.6\% \\ Centromere & 81.1\% \\ Nucleolar & 74.1\% \\ Nuclear dots & 79.0\% \\ Nuclear membrane & 76.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of the segmentation method.  
All of the work presented in Table 6 investigated six patterns, but the patterns typology was almost never identical; obviously, from this, derives a different segmentation-complexity. In our work, considering that the \"speckled\" pattern can present itself in two forms, \"coarse\" and \"fine\", conceptually the analyzed patterns, therefore, have been seven.
# Discrete-Time Asymptotic Tracking Control System for a Satellite with a Solar Panel
## 1 Introduction
### Related Works
The theoretical results are tested on a numerical example and computations are performed within the MATLAB environment, confirming the efficiency and robustness of the developed approach. The main advantage of combining the Cayley-Tustin time discretization procedure with the sophisticated general regulation theory is the preservation of important system-theoretic properties (see, e.g., [21]), which immensely helps in studying the underlying discrete-time control problems.
# Polyp Generalization via Diversifying Style at Feature-Level Space
## 5 Discussion  
We have introduced augmentation strategies operating in the feature space, which have exhibited superior performance across various polyp datasets and demonstrated the ability to generalize in challenging and unseen environments compared to traditional data augmentation techniques and previous methodologies. We hypothesize that our proposed method facilitates the learning of deep learning models to acquire domain-agnostic and content-specific visual representations by substituting or exchanging original style components with new ones, primarily focusing on domain-irrelevant and class-specific aspects. Indeed, simply transferring style statistics at both the image level and feature space level has led to significant performance improvements. Training on stylized versions of the polyp dataset has resulted in notably enhanced performance compared to traditional augmentation methods. Similarly, experiments involving the transfer of style features at the feature space level have yielded comparable performance gains. Furthermore, the experiments on generating a more diverse style transfer technique (the NSSM) demonstrated that the proposed method can deal with arbitrary styles, in contrast to traditional augmentation approaches, the SGM and AGM, which rely on a fixed set of data style transformations. Allegedly, prior research has not employed style-based data augmentation, either at the image level or the feature space level, for the task of polyp segmentation and generalization using deep learning models. Some earlier studies have applied style transfer techniques in different domains, such as skin lesion classification [44] and histology datasets [15]. However, these studies utilized transformations relevant to medical contexts to address image scarcity and imbalance issues in datasets. One variant of our proposed method (the SCM) bears some resemblance to this approach. Moreover, the study by [15] emphasized \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Method** & **U-Net** & **UNet++** & **DCRNet** & **MKDCNet** & **ACCSNet** & **PraNet** & **HardNet** & **Proposed Method (NSSM)** \\ \hline mDice & 0.384 & 0.421 & 0.213 & 0.269 & 0.578 & **0.937** & 0.393 & 0.854 \\ mIoU & 0.343 & 0.345 & 0.136 & 0.142 & 0.509 & **0.890** & 0.292 & 0.760 \\ FM & 0.386 & 0.397 & 0.215 & 0.272 & 0.560 & **0.926** & 0.395 & 0.906 \\ SM & 0.673 & 0.694 & 0.318 & 0.335 & 0.754 & **0.873** & 0.436 & 0.759 \\ maxE & 0.68 & 0.746 & 0.323 & 0.349 & 0.764 & **0.951** & 0.426 & 0.771 \\ MAE & 0.034 & 0.032 & 0.523 & 0.510 & 0.059 & **0.074** & 0.436 & 0.164 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparative result comparison of the proposed method on the Hyper Kvasir Capsule dataset. Evaluation scores of U-Net [21], UNet++ [22], DCRNet [28], ACSNet [26], PraNet [23], HardNet-MSEG [24] and MKDCNet [29] are computed.
## 3 Results and Discussion
### Plasmid Purity and Integrity  
Figure 6 presents a gel electrophoresis image obtained from different samples of pVAX1-NH36 at different stages of the nanoparticle preparation process. These samples were taken to verify initial purity of pVAX1-NH36, and also to evaluate its integrity towards the nanoparticle preparation process. Future studies should include transfect _E. coli_ with recovered pDNA and produce kanamycin resistance, thus demonstrating that released pDNA remained functional. Figure 6 presents four channels, where the first one shows the size markers for plasmid over the range of 2972-12,138 bp. Channel two presents initial pVAX1-NH36 solution used to formulate pDNA-NPs. This solution contains pVAX1-NH36 in its supercoiled form (band of 4000 bp) and two relaxed isoforms. Channel three and four display samples obtained after pVAX1-NH36 was released from pDNA-NPs for two different batches prepared with 0.5% and 0.8% TDL respectively. Gel image showed single bands representing supercoiled and two relaxed forms of pVAX1-NH36 plasmid consistent with initial plasmid solution bands. Difference in brightness is due to difference of pVAX1-NH36 concentration in each sample. In accordance with electrophoresis gel, plasmid quality over encapsulation experiments was constant without any noticeable damage. Studies of integrity of pure pVAX1-NH36 in aqueous solution during the use of sonication at different potencies were performed and no plasmid damage was observed by using the technique of electrophoresis in agarose gel (data not shown in this work). [PERSON] et al. thoroughly  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Description**} & \multirow{2}{*}{**Unit**} & \multicolumn{3}{c}{**TDL (\%)**} \\ \cline{3-6}  & & & **0.2** & **0.5** & **0.8** \\ \hline \(k_{b}\) & Burst constant & days\({}^{-1}\) & 4.4513 & 3.6538 & 3.6722 \\ \(\theta_{b}\) & Fraction of burst release & - & \(1.059\times 10^{-8}\) & 0.1914 & 0.4977 \\ \(k_{r}\) & Degradation-relaxation constant & days\({}^{-1}\) & 0.2060 & 0.2792 & 0.2439 \\ \(t_{max}\) & Time to achieve 50\% of release & days & 12.7481 & 15.4318 & 14.6954 \\ \(\theta_{r}\) & Fraction of NP relaxation release & - & 0.9999 & 0.8086 & 0.5023 \\ \(R^{2}\) & Coefficient of determination & - & 0.9961 & 0.9996 & 0.9969 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of plasmid pVAX1-NH36 release from pDNA-NP. The parameters were determined and used in the mathematical development of the release model.  
tested structural and functional integrity over sonication experiments and their results suggested that polymer (PLGA) protects plasmid DNA from being directly affected by sheering due to high energy of sonication [28]. [PERSON] et al. verified plasmid integrity after performing electrophoresis gels confirming that plasmid was amenable to gene transfection [61].
# Design of an IMCPID Optimized Neural Network for Stepless Flow Control of Reciprocating Machinery
## 4 Algorithm Simulation and Results  
In this section, some simulations are completed to illustrate the effect of the proposed IMCPIDNN controller in a stepless flow control system. The design and training process of IMCPIDNN and PID, IMCPID and FLSMC (feedback linearization sliding mode control) are done with the MATLAB/SIMULINK environment. Some possible external disturbances have also been taken into consideration during the simulation process. The simulation sampling time is set as 0.01s. The controller output threshold is set as \([0,1]\) to simulate the real action of the actuators. The learning rate \(\eta\) and the inertial coefficient \(\alpha\) are set as 0.5 and 0.1, respectively. In addition, the simulation results of the control effects are compared in terms of step responses, anti-interference and robustness, which shows the superior performance of the proposed IMCPIDNN controller in all aspects.  
The industrial process presented in Section 2 is a two-input and two-output system whose open-loop transfer function can be described as:  
\[G_{m}(s)=\left[\begin{array}{cc}\frac{244}{6s+1}e^{-s}&\frac{-394}{5s+1}e^{ -3s}\\ \frac{254}{72s+1}e^{-s}&\frac{133}{13s+1}e^{-2s}\end{array}\right]. \tag{48}\]  
The RGA matrix is:  
\[\beta(s)=\left[\begin{array}{cc}0.25&0.75\\ 0.75&0.25\end{array}\right]. \tag{49}\]  
According to the controller design method presented in Section 3, we know that the system coupling is serious, and the best paring configuration is \(u_{1}/y_{2}\) and \(u_{2}/y_{1}\). Thus, the process model \(G_{m}(s)\) can be simplified as:  
\[G_{m}(s)=\left[\begin{array}{cc}0&\frac{-394}{5s+1}e^{-3s}\\ \frac{254}{72s+1}e^{-s}&0\end{array}\right]. \tag{50}\]  
The value of the filter parameter \(n\) is selected as \(n=1\) to meet the order of the model \(G_{m}(s)\). The PID-IMC controller \(G_{c}(s)\) can be obtained as the following equation according to the controller design method given in Equations (16)-(26):  
\[G_{m}(s)=\left[\begin{array}{cc}0&-\frac{13}{394(2\lambda_{1}+3)}(1+\frac{2 }{13}\frac{1}{s}+\frac{15}{13}s)\\ \frac{145}{254(2\lambda_{2}+1)}(1+\frac{2}{145}\frac{1}{s}+\frac{72}{145}s)&0 \end{array}\right]. \tag{51}\]