esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Theoretical Model
### Pore Fluid Compressibility Function, \(\kappa_{f}\)  
The volumetric compressibility of pore fluid, which is a mixture of water and air contained in voids, is defined as [60]:  
\[\kappa_{f}=(1-S_{r})\kappa_{a}+S_{r}\kappa_{w}, \tag{28}\]  
where \(\kappa_{a}\) and \(\kappa_{w}\) are, respectively, the compressibilities of air and water.  
Pore air can be treated as a perfect gas, so, assuming no temperature changes, its compressibility can be described by the formula:  
\[\kappa_{a}=\frac{1}{u_{c}}, \tag{29}\]  
where \(u_{c}=u+u_{\text{atm}}\) is total pressure.  
Water is very poorly deformable; its compressibility under atmospheric pressure is \(\kappa_{w}=0.45\times 10^{-9}\) Pa\({}^{-1}\) and does not show significant changes with its increase, so it can be treated as a material constant.  
In laboratory tests, [PERSON]'s parameter \(B\) was used to determine the saturation level of the soil because of its easy measurement during triaxial compression tests, in which a direct determination of saturation is extremely difficult. Since parameter \(B\) is not a physical measure nor is it useful in numerical calculations, Relationship (30) was used to determine the initial degree of saturation \(S_{r}^{0}\):  
\[S_{r}^{0}=\frac{\frac{(1-B)}{nB}\kappa_{s}^{p^{\prime}}-\kappa_{a}}{\kappa_{w }-\kappa_{a}}. \tag{30}\]  
Formula (30) was obtained by substituting Equation (3) into (28), whereas many authors use the inverse relationship in their studies, e.g., [61; 62; 63]. In the above form, the formula makes it possible to determine the saturation level on the basis of [PERSON]'s parameter \(B\), porosity and the isotropic compressibilities of soil skeleton, pore water and pore air. Please note that the effect of solubility has a significant influence on the compressibility of the water-air mixture [49] described by Equation (28). The approach presented to determine the degree of saturation based on an experimentally determined [PERSON]'s parameter \(B\) also takes into account the change in pore fluid compressibility due to dissolved air.  
In the course of a change in pore pressure, the volume of pores, \(V_{p}\), also changes, which consequently changes the degree of saturation. Based on the definition of the degree of saturation: \(S_{r}=V_{w}/V_{p}\) and using Equations (13) and (16) for the pore voids and the analogical set for pore water, current soil saturation can be expressed in terms of the compressibility of pore fluid components according to the following equation:  
\[S_{r}=S_{r}^{0}\frac{(1-\kappa_{w}\Delta u)}{\left(1-\kappa_{f}\Delta u\right)}. \tag{31}\]  
In a similar manner, by combining the definitions of soil porosity and compressibility of the pore fluid and keeping in mind the constant volume of the soil skeleton we can also easily determine changes in porosity caused by pore fluid compression during the test, which can be described as:  
\[n=\frac{\left(1-\kappa_{f}\Delta u\right)}{\left(1-\kappa_{f}\Delta u\right) +\frac{1-n_{0}}{n_{0}}}. \tag{32}\]
###### Abstract  
To address chromatic dispersion (CD) and nonlinear impairments in coherent optical four-level pulse amplitude modulation (PAM4) systems, we propose a magnitude-assisted decision feedback equalizer (MA-DFE). The proposed scheme utilizes signal amplitude information to construct the error function, which is robust to carrier phase noise. Therefore, no additional carrier phase recovery operation is required during digital signal processing (DSP). Under conditions without CD pre-compensation, MA-DFE achieves 80 Gb/s single-wavelength transmission over a 25 km standard single-mode fiber (SSMF) in the C-band. When considering a bit error rate (BER) of \(1\times 10^{-2}\) for the soft decision threshold, the link budget achieves 27 dB. In addition, we incorporate the phase into the error function, proposing the phase-assisted decision feedback equalizer (PA-DFE). PA-DFE is also unaffected by carrier phase noise and demonstrates better performance than MA-DFE when equalizing the more severe signal impairments caused by SOA gain saturation. Ultimately, we achieve a link budget of 29 dB using PA-DFE.  
decision feedback equalizer; nonlinearity compensation; coherent detection Article
## 2 Materials and Methods
### Data Preprocessing and Feature Engineering  
Data preprocessing was performed utilizing Python (version 3.10.12) [36], and meteorological data were synchronized with the SMC data. Categorical variables such as soil texture were encoded, and missing data were handled by imputation. Data preparation, such as transformation, cleaning, and statistical testing to ensure uniformity and prevent inaccurate or missing values, was conducted using Python. Six predictive features were selected to train the model including depth (cm), precipitation (mm), temperature (\({}^{\circ}\)C), humidity (%), wind speed (km/h), and solar radiation (W/m\({}^{2}\)), and the target variable is SMC%. These features were chosen based on their availability from field sensors and their well-documented influence on soil moisture dynamics as reported in previous studies, highlighting their agricultural relevance in irrigation management, crop water requirements, and soil water balance assessments [37; 38].  
To determine whether SMC varied significantly across the five depths while accounting for repeated sampling at the same times across time, Repeated Measures ANOVA (Equation (2)) was performed independently for each soil texture using the statsmodels library's AnovaRM. The subject was identified using sampling points, and the within-subject element was depth (cm). The statistical significance level was set to \(p\) < 0.05.  
Using Python, a descriptive statistical analysis was conducted to determine the mean soil moisture content (SMC%), standard deviation (SD), and coefficient of variation (CV%) for each soil texture and depth.  
\[Y_{ijk}=\mu+\alpha_{i}+S_{j}+\epsilon_{ijk} \tag{2}\]  
where  
\(Y_{ijk}\) = SMC measurement at depth i, samplisng point j, time k.  
M = Overall mean, \(\alpha_{i}\) = Fixed effect of depth, \(S_{j}\) = Random effect of sampling point.  
\(\epsilon_{ijk}\) = Residual error.  
The coefficient of variation (CV%) was calculated using the following formula:  
\[\textbf{CV(\%)}=\frac{SD}{Mean}\times\textbf{100} \tag{3}\]
## 2 Material and Methods
### Animal Experiments  
Animal experiments were performed with the permission of authorities of the country of Thuringia (license # UKJ-18-012) in agreement with European law for animal welfare. FLT3-ITD and green-fluorescent protein (GFP)-expressing 32D cells (2 \(\times\) 10\({}^{6}\) or 5 \(\times\) 10\({}^{5}\), see figure legends) in phosphate-buffered saline (PBS) were injected into the tail vein of C3H/HeJ mice (Jackson Laboratory, Bar Harbor, ME, USA). Setanaxib was applied by oral gavage for 9 consecutive days as described previously [25]. Doxorubicin solution for patient use (Doxo Cell 150, STADAPHARM GmbH, Bad Vilbel, Germany, 2 mg/mL) was diluted with sterile 150 mM NaCl to 0.6 mg/mL and 5 uL/g was injected intraperitoneally at day 3, 4, and 5 after transplantation of cells. Mice were monitored for signs of morbidity and were sacrificed at day 9 or 10 post transplantation. The whole bone marrow (BM) containing invaded GFP+ 32D-FLT3-ITD cells was harvested from the tibia, femur, and hip. To this end, the bones were removed from the mice, the muscles were stripped off using a sterile paper towel, and the cleaned bones were placed in a Petri dish. The bones were cut with scissors and the marrow was obtained by flushing the bone interiors with PBS using a syringe. The suspension was collected and passed through a 70 umL cell strainer. Spleens were harvested and smashed by passing the tissue through a 70 umL cell strainer with a syringe pestle. The suspensions were centrifuged and blood cell lysis was performed using BD Pharm Lyse(tm) lysing solution (BD Biosciences, Heidelberg, Germany) according to the instructions of the manufacturer. The percentage of GFP-positive cells in BM or spleen was subsequently determined by flow cytometry using a BD FACSCanto(tm) flow cytometer. The gating strategy is illustrated with example dot-plots for spleen cell analysis in Supplementary Figure S1 (Online Supplement). Data were analyzed using FlowJo software (TreeStar Inc., Ashland, OR, USA).
# Privacy-Preserved Image Protection Supporting Different Access Rights
## 3 Proposed Scheme
### Image Protection Method  
The image protection method supporting different access rights in a single image for privacy protection is mainly composed of the initialization phase and image protection phase. In the initialization phase, the image manager can configure system parameters and generate keys for every user according to the access policy. In the image protection phase, the image in need of protection is set access rights on blocks to generate the corresponding encrypted image and authentication information. The initialization phase and the image protection phase are presented in the following.
## 3 Metrics for Evaluating Prediction Performance
### Dimensional Metrics  
The mean absolute error (MAE) signifies the average of the absolute differences between the predicted values and the observed values. A reduction in the MAE value indicates enhanced prediction accuracy, as depicted in Equation (22):  
\[\text{MAE}(\hat{\mathbf{y}},\,\mathbf{y})\ =\ \frac{1}{N}\times\sum\
olimits_{i\,=\,1}^{N} \lvert\hat{y}_{i}-y_{i}\rvert \tag{22}\]  
where \(\hat{\mathbf{y}}\) and \(\mathbf{y}\) are the predicted and true value vectors of structural response, and \(\hat{y}_{i}\) and \(y_{i}\) (\(i\) = 1, 2, \(\dots\), \(N\)) are the predicted and true values, respectively, of the \(i_{\text{th}}\) time step in the response sequence.  
The mean squared error (MSE) represents the expected value of the squared differences between the predicted parameter value and the true parameter value. A smaller MSE value indicates higher accuracy in the model's predictive performance, as demonstrated in Equation (23):  
\[\text{MSE}(\hat{\mathbf{y}},\,\mathbf{y})\ =\ \frac{1}{N}\times\sum\
olimits_{i\,=\,1}^{N }(\hat{y}_{i}-y_{i})^{2} \tag{23}\]  
The RMSE is the arithmetic square root of the MSE and can be calculated according to Equation (24):  
\[\text{RMSE}(\hat{\mathbf{y}},\,\mathbf{y})\ =\ \sqrt{\text{MSE}(\hat{\mathbf{y}},\,\mathbf{y})} \tag{24}\]  
It is evident that dimensional indices encompass both the magnitude of the value and the associated physical unit. While such indices directly reflect differences in prediction accuracy for the comparison of similar data types, they pose challenges when comparing different bridge responses due to the presence of multiple response types. In contrast, non-dimensional indices solely consist of value magnitudes without units, facilitating the comparison of prediction effects across various response types.
## 6 Circuit Design  
The block diagram of the standalone renewable energy system is illustrated in Figure 12. As can be easily seen, the whole system is broadly composed of a power generator (wind turbines and PV array), an uploading circuit, a three-phase rectifier bridge, an interleaved Buck circuit, a DC/DC conversion circuit, a switch circuit, a power supply circuit, an amplifier, a driver circuit, a voltage and current monitoring, a load, battery units, and a control system. The electric energy generated by the wind turbine converts into direct current through the three-phase rectifier bridge, and concentrates with the electric energy generated by the photovoltaic power generator. Then the electric energy generated by the wind energy and the solar energy are converted into stable direct currents to loads and the battery units by the interleaved Buck circuit. The wind turbine side is designed with an unloading circuit to prevent excessive output power from damaging the equipment under high wind conditions. The bus voltage is 24 V and the battery voltage is 12 V, thus, a DC/DC conversion circuit is designed. The model's parameters of circuits were chosen by empirical and commercial general specifications, which is a common method in circuit design.  
Figure 11: The flowchart of the power supply strategy.
## 3 Results and Discussion  
### Process Window  
The deposition rate of SiN\({}_{\mathrm{x}}\) thin films was investigated as a function of precursor dosing time and plasma exposure time at 400 \({}^{\circ}\)C. The growth rate per cycle (GPC) increased and saturated at 0.38 A/cycle, as CSN-2 dosing time increased to 5 s. An apparent saturation for GPC can also be investigated when the plasma exposure time was 20 s or longer. This GPC saturation indicates that the SiN\({}_{\mathrm{x}}\) thin film RPALD process using CSN-2 and N\({}_{2}\) plasma is a self-limited reaction with no thermal decomposition of the precursor.  
We investigated the growth rate of SiN\({}_{\mathrm{x}}\) thin films at various temperatures (100-600 \({}^{\circ}\)C) as shown in Figure 2. The growth rate of the SiN\({}_{\mathrm{x}}\) thin film was nearly constant at 0.38 A/cycle in the deposition temperature range of 200-500 \({}^{\circ}\)C, known as the ALD process window. In the temperature regions below 200 \({}^{\circ}\)C and above 500 \({}^{\circ}\)C, the self-limited reaction is disturbed depending on process temperature. The former is due to precursor condensation on the substrate and the latter is due to thermal decomposition of the precursor; a \"CVD-like\" process [25; 26; 27]. As the deposition temperature increased, the refractive index of SiN\({}_{\mathrm{x}}\) increased to 1.98 which is similar to 2.01 of the stoichiometric SiN\({}_{\mathrm{x}}\) thin film, indicating that the quality of the film was improved. It can be also assumed that the film density is improved by increasing the refractive index [28].
## 5 Program Customizability and Adaptability  
Some programs, especially serious games, may allow users to customize their user interface, including the avatar, if it is used, game scene, as well as difficulty level. In [61], a user is asked to customize the avatar and the virtual environment of his/her choice. The user is also asked to calibrate the tactile feedback of the game controller he/she will be using in the program because the authors anticipated that different users would have different sensitivity preferences. Furthermore, different methods were provided in [61] for a user to indicate the specific emotion they recognized, including a visual non-verbal code where each emotion to be recognized was represented by a particular color/icon (happiness was represented by yellow, angle was represented by red, disgust was represented purple, fear was represented by green, sadness was represented by light blue, surprise was indicated by dark blue, pain was represented by black, neutral was represented by white, and funny face was represented by a trash can), emotional words that told the emotion as it was, and idiomatic expressions, which were short sentences containing a figurative meaning, e.g., an idiomatic expression for fear could be \"to get cold feet\". In [25], all three motion-based games were designed for customizability. A user could decide on appropriate game speed, object density, and which body parts to enable for game playing.  
Different from program customization, it might be desirable for a program to dynamically adjust its flow of control, task difficulty level, or graphic content based on how a user responded to deliver a personalized program for each user [149]. Of course, this does not apply to all programs because some studies were designed to establish patterns of behaviors of individuals with ASD. In these studies, obviously there is no need to adapt the program flow and content based on the input from the participants. [PERSON] et al. [28] studied social attention patterns of high functioning children with ASD in a virtual public speaking task based on eye gaze.  
Many treatment programs are not adaptive in that the flow of the programs are not changed dynamically based on the input collected while being administered. For mobile games [14], a participant must provide the correct answer to each question before the participant can complete the current level, and only after completing the current level, can the participant continue on to the next level. Such games do not consider how many times a participant has failed a level.  
An adaptive program requires the processing of input data from participants in real time. This can be done either automatically using technology and computer algorithms, or by a therapist manually.
## 2 Literature Review  
There are many works in the literature on the issue of embedding a watermark in an image. To generalize, they can be divided into classical methods and those using ANNs.  
The classical methods include such methods as the least significant bit method [9], the mask method [17], and frequency-domain manipulations (discrete cosine transform [18], discrete sine transform [19], discrete wavelet transform [10], discrete Fourier transform [20], and hybrid domains [21; 22; 23], among others). Assorted variations of the above-mentioned methods are used to embed watermarks in both static images and videos.  
In the literature, there are many publications on embedding a watermark in a static image using the classical methods [24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34]. These methods use the whole image or some of its regions, constituting the areas of interest. The least significant bit method [9; 32], as its name implies, uses the least significant bit of each channel value of a given colour palette, affecting the image slightly, but it is prone to losing the watermark due to all kinds of conversions. The whole image is also influenced by methods that manipulate the frequency domain [18; 19; 20; 21; 22; 23], which are characterized by better survival despite various types of conversions than are those generated via the method of the least significant bit. The modification of the most convenient areas takes place in different methods. An example of one such methods is that which allows for the selection of the optimal colour channel to be modified, as presented in the work of [PERSON] et al. [27].  
With the development of ANNs and the areas of their use, they began to also be used to embed watermarks in static images [35; 36; 37; 38]. The introduction of the use of ANNs has created new opportunities for research in watermark embedding, and those are also explored in this paper.  
The subject of this publication is the embedding of a watermark in a video; therefore, it should be noted that there are various methods for this purpose, both classical [13; 14; 15; 16; 39; 40; 41; 42; 43] and those based on artificial neural networks [11; 12]. Among the works describing the process of embedding a watermark in a video, there are some dedicated specifically to particular video codecs, such as H.264/AVC (Advanced Video Coding) [13; 14; 40; 41] and its successor H.265/HEVC (High-Efficiency Video Coding) [15; 16; 42; 43].  
Embedding a watermark in a video is a more sophisticated issue than is that process with a static image due to the different characteristics of the codecs used to compress video material. For the purposes of embedding a video watermark, methods used with static images [12; 13], as well as those dedicated to specific video codecs, are applied, using their specific properties [15; 16; 43]. Depending on the assumptions made, it is possible to recover the watermark from a single video frame or from a fixed number of frames.  
In this article, a method based on using DNNs as applied to the HEVC codec to embed watermarks in videos will be discussed.
## 3 Results
### Results of the Estimated Clocks  
In this section, we analyze the results of the estimated clocks (true--estimated). Figure 3 shows the clock errors for different scenarios and 10 days of the simulation period from the MEO-only constellation. A similar behavior for the total error of the estimated clocks is achieved for the MEO+IGSO and MEO+GEO constellations. Referring to Figure 3, we provide a separate detailed analysis on the mean error and the standard deviation of the estimated clocks in the following subsections.
# Reducing Defense Vulnerabilities in Federated Learning:
## 4 Attack Scenarios and Results
### Datasets  
The CIFAR-10 [28], F-MNIST [29], MNIST [30], and German Traffic Sign Recognition (GTSR) [31] datasets are used in this study. As model architecture, ResNet18 is used in the CIFAR-10 dataset, and CNN is used in the F-MNIST and MNIST datasets.  
The CIFAR-10 dataset consists of 32 \(\times\) 32-sized color images of airplanes, cars, birds, cats, gazelles, dogs, frogs, horses, ships, and trucks. There are 60,000 images in total. A total of 50,000 of these images are used for training and 10,000 for testing.  
The F-MNIST dataset consists of 28 \(\times\) 28-sized gray images of t-shirts, trousers, sweaters, dresses, jackets, sandals, shirts, sneakers, bags, and boots. There are 70,000 images in total. A total of 60,000 of these images are used for training and 10,000 for testing.  
The MNIST dataset consists of 28 \(\times\) 28 pixel-sized gray images of digits from 0 to 9. There are 70,000 handwritten digit images. In simulation, there are 70,000 images in total. A total of 60,000 of these images are used for training and 10,000 for testing.  
The GTSR dataset contains a collection of over 50,000 traffic sign images, categorized into 43 distinct classes. This dataset is characterized by a significant class imbalance, with individual class sizes ranging from 200 to 2000 samples. To facilitate our experimental analysis, we focused on a subset of 10 classes that exhibit higher sample counts, mitigating the impact of the dataset's inherent imbalance. This approach aligns with the methodology employed by [PERSON] et al. [7]. The selected classes encompass a diverse range of traffic signs, including prohibitive signs such as \"No passing\" and \"No passing for vehicles exceeding 3.5 metric tonnes\", regulatory signs like \"Priority\" and \"Yield\", cautionary signs such as \"Roadworks\", directional signs like \"Keep right\", and various speed limit indicators (30 km/h, 50 km/h, 70 km/h, and 80 km/h).