esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Protocols Verifying the Authenticity of Digital Files in Distributed Networks
### Description of the Protocols with CRP Mechanisms
#### 3.1.2 Verification Cycle  
The verification cycle follows the same steps as the enrollment cycle. The release of \(\omega\) initiates the process, while {C, S, M*} are also needed. The replay of the CRP mechanism retrieves the responses from \(\omega\) and {C, S}. The responses allow the recovery of K, the deciphering of Pk from M* with K, and the deciphering of C with Pk to retrieve F. Algorithm 5 is summarizing this protocol as shown below. A variation of this protocol utilizes a message of authenticity M, instead of Pk, to generate ciphertext M* with K; where, in this case, C is decrypted separately. F can remain secret; while the proof of authenticity of C can be public, informing peers that C is valid.  
```
1: Variable input data: {\(\omega\)}, and {C, \(S\), M*}
2:\(\rightarrow\) Use Module 1: Generate a set of N responses with C, and {\(\omega\),S}:
3:\(\bullet\) Static input data: Positive integers \(d\), \(N\), \(P\), \(\alpha\), \(\beta\)
4: Output data: the \(N\), \(P\)-bit long, responses {\(r_{1},\ldots,r_{N}\)}
5:\(K\leftarrow\text{concatenate}\{r_{1},\ldots,r_{N}\}\)
6:\(Pk\leftarrow\text{Decrypt}(M^{*},K)\)
7:\(F\leftarrow\text{Decrypt}(C,Pk)\)
8:Output: \(F\), \(Pk\)
```  
**Algorithm 5**Decrypt file F
## 2 Materials and Methods
### Machine Learning  
#### 2.3.1 Training Data and Scoring the Model  
In the algorithm description we have introduced five parameters that influence the results of SMD (shifted-matrix decomposition). The five parameters are \(n_{E}\) and \(n_{F}\) from Step 1, \(w\) and \(l\) from Step 2, and \(l_{\mathrm{ir}}\) from Step 3. However, we do not have an intuitive understanding of how the results of SMD are affected by the changes to the parameters, and we have no analytical solutions for optimal values for the parameters. For that reason, we use supervised machine learning to find a set of parameters that provide consistently good SMD results. Once the parameters are optimized in the training stage, SMD can be applied to new seismic data without the need to run the training stage again. Moreover, the parameter \(m\), which also affects the results of SMD, is predetermined and therefore cannot be optimized.  
The first step is to define what is meant by a good SMD output. We desire the matrix reconstructed from SMD to closely resemble the original seismogram matrix \(\mathbf{M}\). Specifically, with a predefined compression rate, we want to capture a big portion of the original data, and we want as little noise as possible to be recorded in the SMD results. We refer to the matrix reconstructed from decomposed data as \(\mathbf{M^{r}}\). When we measured the resemblance between \(\mathbf{M}\) and \(\mathbf{M^{r}}\) as an \(L_{1}\) or \(L_{2}\) norm of the error \(\mathbf{M}-\mathbf{M^{r}}\), we obtained the smallest error norms when the SMD captured a lot of the noise with  
shift vector usually provides high correlation between the original and the reconstructed data in said area.  
Thus, we search for a set of parameters which maximize the dot product \(\mathbf{M}\cdot\mathbf{M}^{\mathbf{r}}\). For training data, we use recordings of reflected wave arrivals from 5 different shot-gathers from marine seismic gathers. We use seismic field data obtained from marine seismic gathers because the data contain a large number of coherent waves with lots of interference, and noise with high coherence which can be difficult to differentiate from signal. To reduce the time spent in the training stage, from each shot-gather we only use 2 s recordings containing reflected waves and the preceding noise recorded by the first 200 receivers. More details on the field data will be provided in the following section. The five shot-gathers are labeled as \(\mathbf{M}^{\mathbf{1}}-\mathbf{M}^{\mathbf{5}}\) and data reconstructed from decomposition of the five seismograms as \(\mathbf{M}^{\mathbf{r,1}}-\mathbf{M}^{\mathbf{r,5}}\). Thus, the overall quality of SMD with a given set of parameters would be quantified as the SMD score (\(\zeta\)):  
\[\zeta=\sum_{k=1}^{5}\mathbf{M}^{\mathbf{k}}\cdot\mathbf{M}^{\mathbf{r,k}}. \tag{9}\]  
The SMD score is only used in training stage to find an optimal set of parameters, and it will not be present in the following sections.
# Analysis of the Water Quality Improvement in Urban Stream Using MIKE 21 FM
## 5 Model Verification and Analysis Results
### Model Simulation Results  
The hydraulic and water quality simulation was carried out upstream and downstream of Gulpocheon. The primary analysis spots are presented in Figure 1. The analysis spot number 1 was upstream of the ozone-treated reclaimed water discharged spot (Cheongcheon). There was no effect of the reclaimed water in this analysis spot. The analysis spot number 2, the Gulpocheon midstream, was set to be the spot after the reclaimed water discharged spot (after the joining of Cheoncheoncheon), and spot number 3 (Gulpo treated sewage water discharged spot) was set to be the spot after Gyesancheon. The analysis spot number 4, Gulpocheon downstream, was affected by the reclaimed and Gulpo treated sewage water.
# Research on Quantization Parameter Decision Scheme for High Efficiency Video Coding
## 3 Materials and Methods
### Mutation  
The mutation operation is the core of the differential evolution algorithm, in which the mutation factor is an important parameter for controlling the diversity and convergence of the population. The value range of mutation factor is usually \([0,1]\), and the common values are 0.5 and 0.75. The mutation factor is related to the characteristics of the problem and the nature of the objective function. Generally, the optimal variation factor can be determined through experiments. At the same time, adaptive strategies can also be used to dynamically adjust the size of the mutation factor to adapt to different optimization problems and population states. For example, the mutation factor can be adjusted based on the average fitness, standard deviation, and other indicators of the population to achieve a better-balanced search effect. The smaller the mutation factor and the smaller the disturbance range, the search range of the algorithm will be too narrow and the population's exploration ability will be poorer. The algorithm will converge slowly and may even fall into a local optimal solution when the mutation factor is too large. The range of disturbance will increase, and the exploration ability of the population will be enhanced. However, the search process is too random, which can easily lead to algorithm instability and oscillation. Differently from the fixed mutation factors used by traditional algorithms, by improving the mutation factors in the mutation operation, different mutation factors are used in each mutation process, thereby disturbing the population individuals to varying degrees to ensure the diversity of the population and thereby improving the search efficiency of the algorithm. The formula of the mutation operation is shown in (1) and (2):  
\[V_{i,t}=round(X_{a,t}+MF\times(X_{b,t}-X_{c,t})) \tag{1}\]  
\[MF=rand(0.1,\ 0.9) \tag{2}\]  
where i represents the index of the current individual in the population, t represents the number of iterations, and a, b, c are positive integers that are different from i. For example, Xa,t can represent that in the tth iteration. In order to avoid floating point numbers generated during the mutation operation, the mutation results are rounded through the rounding function. MF represents the mutation factor, which randomly takes a value from 0.1 to 0.9 during each mutation process to ensure that the population individuals have varying degrees of disturbance.
# Experimental Assessment of the Spatial and Temporal Distribution of Non-Contact Tonometer Airflows
## 3 Results
### Evaluation of the Experimental Set Up
#### 3.1.2 Mapping Procedure  
The position accuracy for the CorvisST was within \(\pm\)0.03 mm for the spatial assessment. The variability of the position of the tube opening and the ORA nozzle was \(\pm\)0.44 mm and included the inclination of the ORA nozzle.
## 4 Results
### Impact of Gradient Weighting on Model Performance  
Gradient weighting plays a crucial role in multilingual and federated-like training setups by dynamically adjusting the contributions of different languages, ensuring equitable learning across resource levels. Without proper weighting, high-resource languages such as English tend to dominate training, leading to suboptimal performance for low-resource languages like Twi. Our model employs an adaptive weighting approach that accounts for language resource availability, thereby improving alignment with Twi linguistic patterns, as demonstrated in Table 6. We evaluate three weighting strategies: equal weighting, static weighting, and dynamic weighting. Under equal weighting, high-resource languages overwhelm the training process, resulting in poor performance on Twi. Implementing a static weighting strategy with a predefined 1:2 high-resource to low-resource ratio enhances Twi's performance, confirming that increasing its relative weight improves translation accuracy. However, the most effective approach is dynamic weighting, which optimizes language contributions per training round based on model loss. This strategy yields substantial improvements, achieving a more than 10-fold increase in SpBLEU and reducing the Word Error Rate (WER) from 183.16% to 68.32%. These results demonstrate that gradient weighting is essential for improving translation accuracy and fluency in low-resource language settings, offering a robust solution for enhancing Twi translations in multilingual models.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Gradient Weighting Strategy** & **SpBLEU (\%)** & **ROUGE-1 (\%)** & **ROUGE-2 (\%)** & **ROUGE-L (\%)** & **WER (\%)** \\ \hline Equal Weighting (Baseline mT5) & 2.16 & 15.23 & 7.18 & 14.22 & 183.16 \\ Static Weighting (Fixed 1:2 HR-LR Ratio) & 12.50 & 40.12 & 22.31 & 37.80 & 98.45 \\ Dynamic Weighting (Optimized Per & 25.67 & 65.24 & 50.22 & 62.12 & 68.32 \\ Round) & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Impact of gradient weighting.  
Figure 5: Learning curve of performance.
# Behavior-Based Control Architecture for Legged-and-Climber Robots
## 3 The Climber Hexapod Robot ROMHEX  
The ROMHEX robot is a commercial platform called _xyzrobot bolide crawler Y-01_ with some modifications. The robot is a hexapod with three degrees of freedom in each leg. The reference systems of each leg according to the robot body are referred to as shown in Figure 1a, while the axes of the leg joints are illustrated in Figure 1b. Mainly, the robot is composed of an electronic board called _MCU board Y-01_ and motors called _xyzrobot smart servo A1-16_.  
The development kit _Intel Euclid_ has been added to the robot through a plastic piece that locates it in a proper position to take advantage of all its features. This device provides a motion camera (not used, so external obstacles are not considered), a computer processing unit and a depth camera. Furthermore, suction cups have been added to the legs extremes in order to hold on to any surface and allow the robot to climb. Every suction cup is equipped with its own centrifugal impeller and motor that creates and maintains the vacuum even on porous surfaces, extracting the internal air [26]. The complete griping system consists of (a) an electronic circuit inside the cup that sensorizes the system and measures the pressure and the distance to the support surface, (b) a turbine motor with its variator, (c) an electronic board that acts as a link between sensorization circuits and the control system of the suction cups and the microcontroller, and (d) a mechanical system with three rotary degrees of freedom to properly align with the surface.  
Lastly, to increase the work-space of the legs, the configuration of the motors has been modified, changing the position of the second motor. In this way, the center of mass is lowered, increasing the robot stability.  
A critical aspect of controlling the robot while climbing is to ensure the normal and shear forces at the suction cup do not exceed certain limits during movement, creating the risk of loss of grip [27]. The hexapod robot is a hyper-static system whose elastic model is too complex to be included in a control loop. Given the hyperstatic nature of the problem, a simplified dynamic model is calculated in [28] and included in the control.  
Climber robots are deployed in dangerous situations, where the power consumption must be optimized to guarantee the finalization of the task. Possible solutions are found in weight reduction, calculation of the best path, or optimization of the walking patterns.  
The leg position is a critical aspect that determines the distance traveled in a given period of time. For this reason, it is highly desired to optimize the robot's leg positions.
# A Novel 3D Topography Stitching Algorithm Based on Reflectance and Multimap
## 2 Materials and Methods
### New Reflectance-Based Stitching Algorithm
#### 2.2.1 Naive Reflectance Algorithm  
The first approach of selection of lighting angles is a naive approach: lighting angles are arbitrary selected, e.g., \(\phi\) = 40\({}^{\circ}\). Reflectance maps are calculated for these lighting angles, but no optimization of the lighting angles occurs. This approach can reduce the quality of in-plane registration as the overlapping regions might not be lighted adequately, although computations are fast. A more interesting approach is an optimization process aiming at finding the best lighting and viewing angles so that the reflectance map contains as many details and contrast as possible.
# Access Control and Pilot Allocation for Machine-Type Communications in Crowded Massive MIMO Systems
## System Model and Preliminaries
### SUCRe Protocol  
SUCRe protocol is presented in detail in [25]. In this paper, we summarize four steps of the SUCRe protocol as follows.  
1. Select Random Pilot Sequence: Each MTCD randomly selects a pilot from the set of pilots \(P_{0}\) in each RA slot with equal probability \(1/\tau_{p}\), and then this pilot is sent to the BS.
2. Generate and Broadcast Precoded Random Access Response (PRAR), ACB Factor and the Information of Idle Pilots: After the BS receives the pilot signal, it generates the PRAR and broadcast this PRAR to all active MTCDs.
3. Contention Resolution using Pilot Repetition and Pilot Reselection: Each active MTCDs receives the PRAR information and checks whether it has the strongest uplink signal gain, which can be accomplished by comparing its own channel gain and an estimate of the sum of the channel gains of the other contending MTCDs. Only the strongest MTCD should repeat the RA pilot.
4. The BS Allocates Dedicated Payload Pilots: The repeated RA pilots are transmitted by MTCDs to the BS during step 3. Then, the BS checks whether an MTCD suffers from pilot collision or not based on the repeated RA pilot signals. The BS allocates the dedicate data pilots to MTCDS who do not suffers from collision.  
In Step 4, the resource allocation in the DL is similar to the precoded response in Step 2. This random access procedure is repeated after a given interval. The MTCDs that do not receive the Dedicated Payload Pilots during Step 4 will choose an integer value as a random waiting time. After the random waiting time, those failed MTCDs will reattempt the random access in the new RA slot.  
Figure 2: Pilot random access block and data block model.
###### Abstract  
This study investigates the physical, mechanical, and structural characteristics of handmade paper samples derived from cellulose extracted from grass clippings using two distinct methods as follows: (1) alkali treatment and (2) alkali treatment followed by bleaching, coupled with the incorporation of barium sulfate as a mineral filler. Our investigation revealed that the handmade paper samples' densities, moisture contents, and thicknesses varied within the ranges of 0.436 to 0.549 g/cm\({}^{3}\), 5.60 to 2.51%, and 0.41 to 0.50 mm, respectively. The tensile strength and folding endurance of the papers produced through alkali treatment with barium sulfate were notably superior to those produced from bleached pulp and barium sulfate. Our analysis indicates that several critical factors, including paper density, thickness, the crystallinity index, and the microfibrillar structure of cellulose, intricately influence the mechanical and strength properties of the samples. Using Fourier transform infrared spectroscopy (FT-IR) and X-ray diffraction (XRD) techniques, we identified characteristic cellulose bonds and examined cellulose's crystalline and amorphous phases. Additionally, the crystallinity index of the samples was determined using both the Segal and peak deconvolution methods. Scanning electron microscopy (SEM) micrographs revealed interconnected networks of cellulose fibers with varying thicknesses and lengths, along with incorporated mineral filler within the cellulose fiber structure. Variations in mineral particle retention were attributed to the presence or absence of cellulose microfibrils. These findings contribute to our understanding of the observed strength characteristics of the paper samples and underscore the potential applications of cellulose derived from grass clippings, especially when combined with barium sulfate as a mineral filler in paper production.  
grass clippings; cellulose; alkaline treatment; bleaching; mineral filler; handmade paper +
Footnote †: journal: Applied Sciences
## 3 Architecture
### Reports and Visualization Component  
Figure 7 shows the reports-and-visualization component of MIRD. This component inputs both the detailed and the aggregated PQ evaluations that result from the power-evaluation component and delivers them as tabular reports and interactive plots--reports and plots persist as HTML files. The component provides the functionality it offers, using the Django and Bokeh Python libraries. MIRD generates the PQ indexes for all PQM in the CFE-DCO power distribution network. However, it presents specific reports and visualizations to users according to their role in the management hierarchy. An external relational database provides user authentication and their corresponding access rights. A complementary functionality of this component sends a summary of reports to MIRD users via email.  
Figure 5: Forecasting component of MIRD. Nearly 4000 univariate forecasting models persist as SciKit Learn Pickle files. Input features and predictions persist as Parquet files.  
Figure 6: Power-quality-evaluation component of MIRD. Detailed PQ indexes persist for both distribution circuits and quality-control nodes. MIRD aggregates the PQ indexes at the four-level geographical hierarchy used in CFE-DCO: PQM or device, substation, area, and zone.  
Figure 7: Reports-and-visualization component of MIRD. HTML files for tabular reports and Bokeh, interactive plots can be later arranged as personalized analytics dashboards.
## 2 Finite Element Model
### Law of Behavior Applied  
Milling of a composite structure requires subjecting the material to significant deformation when in contact with a cutting tool, which results in the creation of chips. Thus, it is essential to determine the constitutive law appropriate to the material constituting the part and the cutting tool in order to be able to carry out effective numerical simulations. In this work, the material constituting the NHC core exhibits isotropic elastoplastic behavior [22; 23]. This behavior is made up of two parts: a reversible elastic part and an irreversible plastic part, as described in Equation (1).  
\[\varepsilon=\varepsilon^{el}+\varepsilon^{p} \tag{1}\]  
\(\varepsilon\) is the total stress tensor, and \(\varepsilon^{\mathrm{el}}\) and \(\varepsilon^{\mathrm{p}}\) represent the elastic and plastic strain tensor, respectively.  
In general, the elastic behavior of the material is described using [PERSON]'s law, which constitutes a fundamental model of linear elasticity. This law describes a linear relationship between the stress \(\sigma\) and the strain \(\varepsilon\) of elastic materials according to Equation (2).  
\[\left\{\varepsilon\right\}=\left[\mathrm{C}\right]\left\{\sigma\right\} \tag{2}\]  
C is the tensor of the reasoner of order 4.  
The law, therefore, becomes the following:  
\[\begin{pmatrix}\varepsilon_{11}\\ \varepsilon_{22}\\ \varepsilon_{33}\\ \varepsilon_{23}\\ \varepsilon_{31}\\ \varepsilon_{12}\end{pmatrix}=\left(\begin{array}{cccccc}\frac{1}{ \varepsilon}&-\frac{\theta}{\varepsilon}&-\frac{\theta}{\varepsilon}&0&0&0\\ -\frac{\theta}{\varepsilon}&-\frac{1}{\varepsilon}&-\frac{\theta}{\varepsilon }&0&0&0\\ -\frac{\theta}{\varepsilon}&-\frac{\theta}{\varepsilon}&-\frac{1}{\varepsilon }&0&0&0\\ 0&0&0&\frac{1}{\mathrm{C}}&0&0\\ 0&0&0&0&\frac{1}{\mathrm{C}}&0\\ 0&0&0&0&0&\frac{1}{\mathrm{C}}\end{array}\right)\begin{pmatrix}\sigma_{11} \\ \sigma_{22}\\ \sigma_{33}\\ \sigma_{23}\\ \sigma_{31}\\ \sigma_{12}\end{pmatrix} \tag{3}\]  
To precisely define the elastic properties of a material, it is essential to determine the Lame coefficients, \(\lambda\) and \(\mu\). These coefficients are linked to [PERSON]'s modulus E and [PERSON]'s ratio \(\
u\) by Equations (4) and (5).  
\[\lambda=\frac{\theta E}{(1+\theta)(1-2\theta)} \tag{4}\]  
\[\mu=\frac{E}{2\left(1+\theta\right)} \tag{5}\]  
Isotropic elastoplastic behavior has been attributed to Nomex material by several authors [24]. Figure 4 demonstrates the mechanical properties of the Nomex material, highlighting the elastic thresholds and variations in plastic behavior as a function of wall thickness. The principal mechanical characteristics attributed to Nomex material are described in Table 1.  
Figure 3: Dimensions of the UCK milling cutter.