esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploiting Online Services to Enable Anonymous and Confidential Messaging
## 4 Implementing the Proposed Messaging Service
In Step 14, \(E_{i}\) and \(H_{i}\) are retrieved after decryption, and afterwards, a new HMAC \(H_{igen}\) is generated from the retrieved \(E_{i}\) and \(IV_{i}\) (Step 15). \(H_{i}\) and \(H_{igen}\) are compared to ensure the integrity of the message (Step 16). Finally, in Step 17, the plaintext message is retrieved by decrypting \(E_{i}\) with the key \(C_{i}\) and the initialization vector \(IV_{i}\). In order to demonstrate messaging service feasibility and evaluate its performance, a PoC implementing the procedures presented in Figure 3 was developed and tested. The tests were executed between 27 December 2021 and 9 January 2022, and each test ran three simultaneous instances per execution, each one uploading and retrieving secret messages through one of three images of different sizes (small, medium, and large). The smallest image, \"jellybeans\", had a size of 83.4 kB; the medium size image, \"baboon\" had a size of 611 kB; and the larger image, \"airport\", had a size of 703 kB. The secret message embedded in the images had a size of 6 bytes. Elapsed times for each execution were recorded. In order to obtain the duration of the different steps that compose a full execution, the following five steps were considered and their execution durations measured. The first step comprises the time taken to encrypt the original message and embed it into an image. The second step comprises the time taken to upload the image to Imgur and to obtain the image URL within the Imgur website. The third step comprises the time taken to store the  
Figure 3: Sequence diagram of the proposed message service. image URL in a specific area on DontPad by the sender plus URL download from DontPad by the receiver. The fourth step comprises the time taken by the receiver to download the image file from Imgur. Lastly, the fifth step comprises the time taken by the receiver to extract and reveal the secret message. The chosen KDF was Argon2 [42, 43], which was used to create hashes with a length of 32 characters. The hashes were used as a key for AES-256-CBC encryption, with CBC mode chosen due to its lesser resource requirements. An Initialization Vector (IV) with a length of 32 characters was generated for encryption. An HMAC-SHA256 of the encrypted message was also generated for authentication and integrity purposes. Figure 4 presents the PoC runtimes with standard deviations (in milliseconds) of each step per image size normalized to 100%. For all image sizes, upload takes the most time (between 60% and 70% of the total time elapsed), followed by download (around 20% of the total time elapsed). Shorter durations were obtained for small images.
## 5 Implementation and Examples
### Example Verification
#### 5.2.1 Pre-Processing of Product Modelling Information  
1. Acquisition of 3D models of products. The CAD 3D assembly model of the product was constructed, as shown in Figure 9a.
2. The measurement target and reference object of the assembly and the global coordinate system were defined. The geometric functional requirement of the assembly was perpendicularity, as shown in Figure 9a. Then, the part Part\({}_{4}\) was defined as the target part of the assembly, and Part1 was defined the measurement datum part. According to the measurement elements and datum elements, the global coordinate system was established, as shown in Figure 9b.
3. According to the method introduced in reference [27], we extracted the assembly constraint information of the simple stamping model from the product assembly design data structure in the CAD system, such as the assembly constraint relationship between parts shown in Figure 10. In the figure, \(\mathbf{M}_{\text{i(Pu,PV)}}\) indicates the assembly relationship between the assembled workpieces, subscript \(\mathbf{i}\) indicates the assembly number, \(\text{Pu}\),\(\text{Po}\) indicates the assembled part number, respectively, and \(\mathbf{u}\) is not equal to \(\mathbf{v}\). \(\text{APJ}_{\text{k,l-n}}\) indicates the localisation constraint connection between the assembled feature surfaces of the parts, subscript \(\mathbf{k}\) indicates the assembly number of the parts and \(\mathbf{l}\),\(\mathbf{m}\) indicates the feature surface number of the different parts, respectively; we extracted the assembly feature surfaces (AFSs) information of each part, such as the geometric and coordinate feature information of these AFSs and the geometric spatial relationship between each AFE of the part.  
Figure 9: (**a**) Three-dimensional assembly model; (**b**) assembly part and coordinates.
## 4 Case Study
### The Construction of the Evidential-Network-Parameter Model  
The establishment of the recognition framework, corresponding to the evidence network for the capabilities of the space-navigation-and-positioning system of systems in Figure 1, is shown in Table 1. Among the recognition frames, \"high\" means that the current capability's situation is \"able to support\" the capability requirement or that the capability's degree of requirement satisfaction is high; \"medium\"  
Figure 1: Evidential network structure for the capabilities of the space-navigation-and-positioning system of systems.  
means that the capability's current situation \"can support under certain conditions\" the capability requirement or that the capability's degree of requirement satisfaction is normal; and \"low\" means that the capability status \"cannot support\" the capability requirement or that the capability's degree of requirement satisfaction is low. For convenience's sake, this article divides the top level into three levels and the remaining levels into two.  
We took into consideration the fact that satellite navigation signals are likely to be more compressed in actual combat operations and that satellite survivability is more important than ground-station survivability due to the high launch cost of satellites. Encryption capability is more important than anti-virus capability, and navigation-positioning capability is the core competence in spatial-information-support system of systems. The conditional belief functions are calculated by using the weight coefficient of capabilities [48]. Then, we established the belief-parameter table, shown in Figure 2.  
In this figure, \"_m(A)=H_!_AC=H_, _AS=L_) = 0.7\" indicates that, with the conditional belief of 0.7, the anti-jamming capability of the space-navigation-and-positioning system is \"high\" when the anti-suppress interference capability is \"high\" and the anti-spoofing interference capability is \"low\". The notation \"_m(O_A)_!_AC=H_, _AS=L_) = 0.2\" indicates that there is a cognitive uncertainty, that is, a partial ignorance of the subject. At this point, we assigned a portion of the belief to the complete set of recognition frames \(\Theta\) for node states.
# Simulation of Forest Carbon Fluxes Using Model Incorporation and Data Assimilation
## 3 Methodology
### The MOD_17 Model  
The MOD_17 model is a light-use-efficiency model based on remote sensing [27] and is driven by \(\mathrm{LUE_{max}}\), a scalar of the eight-day vapor-pressed deficit (VPD), a scalar of the eight-day minimum air temperature (T\({}_{\mathrm{min}}\)), and absorbed photosynthetically active radiation (APAR, M] day-1). Therefore, GPP is calculated as follows:  
\[\mathrm{GPP}\,=\,\mathrm{LUE_{max}}\times f(\mathrm{T_{min}})\times f(\mathrm{ VPD})\times\mathrm{APAR}, \tag{1}\]  
where \(\mathrm{LUE_{max}}\) is the potential maximum LUE without environmental stress [28]; and APAR is derived from PAR multiplied by \(f\)PAR.  
The MOD_17 model requires forcing data from the following three sources: (1) biome specific parameters such as \(\mathrm{LUE_{max}}\); (2) meteorological data (i.e., PAR, T\({}_{\mathrm{min}}\), and VPD); and (3) \(f\)PAR. In the past, MODIS GPP products have been generated using data from MODIS surface reflectance with information regarding vegetation phenology, the canopy absorbance of \(f\)PAR, and climate data from the NASA Data Assimilation Office (DAO) climate model. Specifically, for estimating GPP, biome specific parameters (\(\mathrm{LUE_{max}}\)) are assigned based on an eight-class MODIS land cover classification product (with a 1 km resolution) and the associated Biome Parameters Look Up Table (BPLUT) [29; 30; 31; 32]. Using scalars (0-1) for T\({}_{\mathrm{min}}\) and VPD, GPP is then calculated [33].  
MODIS GPP products have been evaluated for different ecosystems in various studies [34; 35; 36; 37; 38; 39]. Uncertainties in the original products included those from biome-specific parameters, input data, and vegetation maps [40]. Specifically, coarse-scale meteorological data (e.g., T\({}_{\mathrm{min}}\), VPD) were required for DAO climate data [41]; and errors in radiometry data could lead to the miscalculation of \(f\)PAR.
## 2 Modeling of the DC Sub-Microgrid
### Modeling of PV System  
PV system with a non-inverting buck-boost converter is used in the model. High-level control directs the operation of the PV in MPPT mode. The regression plane method is used for MPPT, which operates on temperature and irradiance data at a definite point to create a reference signal \(V_{pv-ref}\)[44]. \(V_{pv-ref}\) can be calculated for any value of temperature and irradiance using the following equation:  
\[V_{pv-ref}=322-(1.34*Temperature)-(0.00964*Irradiance) \tag{9}\]  
The internal circuitry of the DC-DC converter can be found in Figure 3. It comprises of an inductor \(L_{pv}\), IGBT switches \(S_{2},S^{\prime}_{2}\), two diodes \(D_{1},D_{2}\), the input capacitor \(C_{pv}\), and an output capacitor \(C_{dc}\). The converter is assumed to be in continuous conduction mode. There exist two modes of operation for the converter, when both the switches are ON and when both the switches are OFF. When \(S_{2}\) and \(S^{\prime}_{2}\) are ON, the load will be disconnected as diode \(D_{1}\) is reverse biased. When \(S_{2}\) and \(S^{\prime}_{2}\) are OFF, the load is connected to \(L_{pv}\) through diode \(D_{2}\). The buck-boost converter associated with the PV system can be expressed as the average state model by following differential equations.  
\[\frac{dV_{pv}}{dt}=\frac{I_{pv}}{C_{pv}}-\mu_{2}\frac{i_{L}}{C_{pv}} \tag{10}\]  
\[\frac{di_{L}}{dt}=\mu_{2}\frac{V_{pv}}{L_{pv}}+\ \mu_{2}\frac{V_{dc}}{L_{pv}}- \frac{V_{dc}}{L_{pv}} \tag{11}\]  
\[\frac{dV_{dc}}{dt}=\frac{i_{L}}{C_{dc}}-\frac{I^{\prime}_{pv}}{C_{dc}}-\mu_{2 }\frac{i_{L}}{C_{dc}} \tag{12}\]  
where \(\mu_{2},\ V_{pv},\ i_{L}\), and \(V_{dc}\) are the values of the control signal, PV input voltages, current of the PV system, and output voltage \(V_{dc}\) of the PV system.
## 2 Materials and Methods
### Modeling Methods
#### 2.5.3 Least Squares Support Vector Machines (LSSVM)  
The LSSVM is an extension of the support vector machine (SVM). It employs a least squares linear system instead of the quadratic programming method in SVM to estimate the errors between the predicted and true values [38]. LSSVM simplifies the solution process using equation constraints based on the Lagrange multiplier method, resulting in improved computational efficiency and prediction capabilities [39]. In this study, the calibration and validation datasets were initially normalized. After thorough debugging, a regularization parameter of 100 and a kernel parameter of 10 were selected.
# Study of Marine Particles Using Submersible Digital Holographic Camera during the Arctic Expedition
## 2 Methods and Equipment
### Data Analysis
#### 2.3.2 Methods and Equipment for Verification and Comparison  
Data for verification and comparison are obtained by standard methods, namely:  
* For plankton--by vertical net catching followed by sample fixation, taxonomic determination, counting of individuals, and laboratory measurement under a microscope;
* For suspension--by processing turbidimetric turbidity measurements;
* For floating bubbles--by using hydroacoustic data of seeps--places of massive release of bubble methane--previously discovered in the Eastern Arctic Seas [31; 33; 47; 55].  
Net Sampling of Plankton  
Zooplankton was collected by vertical net sampling in the water column using the Juday net with a mesh size of 180 \(\upmu\)m and a net inlet diameter of 0.37 m. Zooplankton samples were concentrated and fixed with a neutral 4% formaldehyde solution. Subsequently, Rose Bengal dye-stained samples were analyzed in the Bogorov chamber under a stereomicroscope. The organisms were identified according to types, classes, and orders using determinants [56; 57].  
Figure 8: (**a**) Two-dimensional display of a holographic image of the studied volume with bubbles against the background of the test particles; (**b**) histogram of the distribution along the diameters of the bubbles.  
Turbidimetric Measurements  
This is a standard integral method used to determine the turbidity on a study scale of about 1 cubic meter by measuring the scattering of light passed through the water column. The Sea-Bird Scientific USA CTD probe SBE 911 plus was used for these purposes and was additionally equipped with a turbidity sensor by WET Labs ECO NTU. The comparison of turbidity versus suspended matter concentrations measured by the weight method shows that the sensor data most likely reflects the real long-term variability of the suspended matter content in the Eastern Arctic Seas.  
Acoustic Survey  
This is an integral method that uses standard or specialized echosounders and sonars. This method, which is based on the generation of an acoustic pulse and the reception of a scattered acoustic signal, is the most effective for observing floating bubbles and obtaining estimates on the amount of methane delivered by them to the water and surface layers of the atmosphere (Figure 9). Modern hydroacoustic tools are able to detect single floating bubbles at distances of up to 2 km or more, estimate their size, and track their ascending speed [12; 47; 55; 58; 59; 60; 61; 62; 63; 64].  
The experiment in places marked with an arrow was carried out by simultaneously holographic gas bubbles using the DHC. The comparison of the DHC and acoustic data for methane bubbles is not presented in this paper, since the issue of synchronizing acoustic and holographic data on this expedition could not be addressed. Some preliminary results from the expedition are discussed below.
# Unsupervised Scene Image Text Segmentation Based on Improved CycleGAN
## 2 Related Work  
### Scene Text Segmentation  
Scene text with complex background is the most challenging problem in scene text segmentation. Traditional text segmentation methods mainly include the threshold algorithm, probabilistic model algorithm and clustering algorithm. Each of these methods has its own characteristics and plays an important role in different application scenarios. As a key technology in text segmentation, the threshold algorithm is mainly used to distinguish the foreground (such as text) and background in an image. According to different threshold processing methods, it can be further divided into the global threshold method [1; 2; 3] and local threshold method [4; 5; 6]. The probabilistic model approach [7; 8; 9; 10] has significant advantages in dealing with uncertainty, prediction and interpretation. The Gauss hybrid model and Markov random field model are representative of them. The clustering method [11; 12; 13] performs well in processing degraded text extraction in videos, mainly by mining the similarity between pixels to achieve the aggregation of text regions. Convolutional neural networks with learning ability can process all kinds of scene images, but their bottleneck is the high dependence on large-scale data, especially on manually labeled data. [PERSON] et al. [19] generated COCO-TS and MLT-S. The COCO-Text [20] data set and MLT data set [21] were used for the weakly supervised processing of these two new data sets to obtain pixel-level annotations. However, the annotation quality of the new machine-generated data set COCO-TS is still far from that of manual annotation. [PERSON] [22] proposed a network in which two branches guide each other. One branch generates the mask of the polygon, and the other branch generates the pixel-level text mask. The whole network training adopts a semi-supervised mode, but the mask is generated in the process of data processing. The accuracy of supervised segmentation methods depends on paired data, but the amount of paired data is limited, and manual labeling is time consuming. Therefore, in recent years, segmentation algorithms with unsupervised methods as the core have attracted much attention, such as fully convolutional adaptive networks (FCANs) [14], CyCADA [15], CrDoCo [16], etc. The experiment of transferring FCAN [14] from game video (GTA5) to an urban street scene has achieved good results, but the experimental effect of expanding to specific segmentation scenes needs to be verified. The CyCADA network [15] combines a cyclically consistent adversarial model of image translation with adversarial adaptive methods, primarily for urban streetscapes and digital. The unsupervised domain adaptive adversarial learning method of the CrDoCo network [16] also aims at the semantic segmentation of street scenes.
# A Measure Based on Beamforming Power for Evaluation of Sound Field Reproduction Performance
## 2 Problem Statements
### Effect of Amplitude Decay with Distance  
The amplitude of a monopole decays with distance by \(1/r\), where \(r\) is the distance to the monopole position. If a linear array or a circular array of loudspeakers is used, e.g., a 2.5D reproduction, the amplitude decay will differ. Figure 2 shows the amplitude decay in a computer simulation where the reproduced field is generated with a circular array of 16 loudspeakers at 1 kHz. The radius of the circular array is 1.5 m, and the position of a desired monopole is at (10 m, 0). The desired and the reproduced decays are compared with a special focus in the controllable zone (9.6 m \(<x<\) 10.4 m). The magnitude is normalized to have 0 dB at the center of the controllable region (\(x=10\)). The reproduced field differs clearly from the desired decay. The difference in the amplitude decay would significantly increase the spatial error. On the contrary, the amplitude decay hardly affects the human localization in terms of perception; directional perception is mainly due to the inter-aural time difference (ITD) and the inter-aural level difference (ILD), and the distance perception is more attributed to the loudness and coloration than the amplitude decay [35; 36; 37; 38].  
Figure 2: Amplitude decay of the reproduced and the desired field.
## 4 Non-Ideality and Sensitivity Analysis
### Non-Ideal Gain and Sensitivity Analysis
The modified expressions of the filter transfer functions, \(f^{\prime}_{0}\) and \(Q^{\prime}\) for the MISO/ SIMO configurations are presented in Equations (26)-(31):  
\[V^{\prime}_{out(VM\text{-}Mole)}=\left[\frac{s^{2}C_{1}C_{2}R_{1}R_{2}V_{2}- Yg_{m1}S_{C1}R_{1}R_{2}V_{3}+\alpha^{\prime}_{N}Yg_{m1}R_{1}R_{2}V_{1}}{s^{2}C_{1}C_{2}R_{1 }R_{2}+\alpha^{\prime}_{P}\beta_{P}sC_{1}R_{1}+\beta_{N}\alpha^{\prime}_{N}Yg_ {m1}R_{2}}\right] \tag{26}\]  
\[I^{\prime}_{out(TAM\text{-}Mole)}=\frac{\alpha_{P}\beta_{P}}{R_{2}}\left[\frac{ s^{2}C_{1}C_{2}R_{1}R_{2}V_{2}-Yg_{m1}sC_{1}R_{1}R_{2}V_{3}+\alpha^{\prime}_{N}Yg_{m1}R_{1 }R_{2}V_{1}}{s^{2}C_{1}C_{2}R_{1}R_{2}+\alpha^{\prime}_{P}\beta_{P}sC_{1}R_{1}+ \beta_{N}\alpha^{\prime}_{N}Yg_{m1}R_{2}}\right] \tag{27}\]  
\[I^{\prime}_{out(CM\text{-}Mole)}=\left[\frac{-\alpha_{P}\left[s^{2}C_{1}C_{2}R_{1 }R_{2}+\beta_{P}\alpha^{\prime}_{P}sC_{1}R_{1}+\beta_{N}\alpha^{\prime}_{N}Yg_{ m1}R_{2}\right]I_{2}+\alpha_{P}\beta_{P}\alpha^{\prime}_{P}sC_{1}R_{1}+ \alpha_{P}\beta_{P}\alpha^{\prime}_{N}Yg_{m1}R_{1}}{s^{2}C_{1}C_{2}R_{1}R_{2}+ \alpha^{\prime}_{P}\beta_{P}sC_{1}R_{1}+\beta_{N}\alpha^{\prime}_{N}Yg_{m1}R_{ 2}}\right] \tag{28}\]  
\[V^{\prime}_{out(TIM\text{-}Mole)}=R_{3}\left[\frac{-\alpha_{P}\left[s^{2}C_{1}C_{2 }R_{1}R_{2}+\beta_{P}\alpha^{\prime}_{P}sC_{1}R_{1}+\beta_{N}\alpha^{\prime}_{N}Yg_ {m1}R_{2}\right]+\alpha_{P}\beta_{P}\alpha^{\prime}_{P}sC_{1}R_{1}R_{2}+ \alpha_{P}\beta_{P}\alpha^{\prime}_{P}sC_{1}R_{1}+\alpha_{P}\beta_{P}\alpha^{ \prime}_{N}Yg_{m1}R_{1}}{s^{2}C_{1}C_{2}R_{1}R_{2}+\alpha^{\prime}_{P}\beta_{P}sC_{ 1}R_{1}+\beta_{N}\alpha^{\prime}_{N}Yg_{m1}R_{2}}\right] \tag{29}\]  
\[f^{\prime}_{0}=\frac{1}{2\pi}\sqrt{\frac{\beta_{N}\alpha^{\prime}_{N}Yg_{m1}}{C_{1} C_{2}R_{1}}} \tag{30}\]\[Q^{\prime}=\frac{R_{2}}{\alpha_{p}^{\prime}\beta_{p}}\sqrt{\frac{\beta_{N}\alpha_{ N}^{\prime}Yg_{m1}C_{2}}{C_{1}R_{1}}} \tag{31}\]  
The sensitivities of \(\omega_{0}^{\prime}\) and \(Q^{\prime}\) with respect to the non-ideal gains and passive components are given below:  
\[S_{g_{m1}}^{\omega_{0}^{\prime}}=S_{\alpha_{N}^{\prime}}^{\omega_{0}^{\prime}}= S_{\beta_{N}}^{\omega_{0}^{\prime}}=S_{\gamma}^{\omega_{0}^{\prime}}=-S_{C_{1}}^{ \omega_{0}^{\prime}}=-S_{C_{2}}^{\omega_{0}^{\prime}}=-S_{R_{1}}^{\omega_{0}^{ \prime}}=\frac{1}{2} \tag{32}\]  
\[S_{g_{m1}}^{Q^{\prime}}=S_{\alpha_{N}}^{Q^{\prime}}=S_{\beta_{N}}^{Q^{\prime}} =S_{\gamma}^{Q^{\prime}}=-S_{C_{1}}^{Q^{\prime}}=S_{C_{2}}^{Q^{\prime}}=-S_{R_ {1}}^{Q^{\prime}}=\frac{1}{2} \tag{33}\]  
\[S_{R_{2}}^{Q^{\prime}}=-S_{\alpha_{p}^{\prime}}^{Q^{\prime}}=-S_{\beta_{p}}^{ Q^{\prime}}=1 \tag{34}\]  
The sensitivities are low and have absolute values not higher than unity.
# Anomaly Detection in Automotive Industry Using Clustering Methods--A Case Study
## 2 Clustering Algorithms  
### K-Means  
The K-Means is a well-known clustering algorithm because it is easy to implement and understand, requests low computational effort, and presents fast convergence [10; 30]. It has been adapted to many challenging problems in distinct domains [31; 32].  
The K-Means initialization starts with the random generation of the centroids' position. The data are allocated to each group, and then a given sample will belong to the cluster with the shortest distance to the respective centroid. After this phase, the new positions of the centroids are recalculated, and they move to the geometric center of the cluster formed in the current iteration [9].  
The algorithm runs to minimize the sum of the internal distance (Sum of Squares Within Clusters--SSE) between the data and the centroids by Equation (1). In this equation, the measure _dist_ is the Euclidean distance given by Equation (2):  
\[SSE=\sum_{k=1}^{K}\sum_{i=1}^{n_{q}}dist(\mathbf{x}_{i}-\mathbf{c}_{k})^{2} \tag{1}\]  
where \(\mathbf{c}_{k}\) is the centroid of cluster \(k\), \(\mathbf{x}_{i}\) is the \(i\)-th object of the \(k\)-th cluster, \(K\) is the number of clusters, and \(n_{q}\) is the number of elements on each cluster and  
\[dist(\mathbf{x}_{i},\ \mathbf{c}_{k})=\sqrt{\sum_{d=1}^{D}(x_{i,d}-c_{k,d})^{2}} \tag{2}\]where \(d\) is each number of dimensions of each object on the data set, and \(D\) represents the last dimension of each object [10].  
\[\mathbf{c}_{k}=\frac{1}{n_{k}}\sum_{i=1}^{n_{k}}\mathbf{x}_{i}^{k} \tag{3}\]  
where \(n_{k}\) is the number of objects in the respective cluster.  
This process is repeated until a stop criterion is reached. The most used are the number of iterations or a predefined limit of a similarity measure.  
We highlight that the strong dependence on the initialization is widely known. In addition, the user must specify the number of clusters a priori [25; 33].