esa-sceva/satcom-chunk-collection huggingface dataset:
# Long-Distance Measurements Using a Chromatic Confocal Sensor
## 4 Methods
### Low-Pass Filtering Method  
The low-pass filtering method involves isolating and retaining only the lowest frequency components of the spectrum. This process is achieved by performing a Discrete Fourier Transform (DFT) on the detected spectral signal, as outlined in Equation (7). The DFT converts the spectral data from the time (or wavelength) domain into the frequency domain, enabling the separation of different frequency components. Mathematically, the DFT of a discrete signal \(S(n)\) is given by  
\[X(k)=\sum_{n=0}^{N-1}S(n)\cdot e^{-i2\pi\frac{k}{N}n} \tag{7}\]  
where \(S(n)\) represents the sampled spectrum, \(N\) is the number of data points in the spectrum, and \(X(k)\) are the frequency-domain coefficients, each corresponding to a specific frequency  
Figure 8: Two captured spectra for different sample positions.  
component of the signal. These coefficients indicate the contribution of various frequency components to the overall spectral data.  
After applying the DFT, a rectangular low-pass filter is employed in the frequency domain to remove higher-frequency components and retain only the low-frequency parts of the signal. This filter essentially \"cuts off\" frequencies above a certain threshold, passing only those below it. Mathematically, the rectangular filter \(H(k)\) can be defined as  
\[H(k)=\left\{\begin{aligned} & 1\text{ if }|k|\leq k_{\text{cutoff}}\\ & 0\text{ if }|k|>k_{\text{cutoff}}\end{aligned}\right. \tag{8}\]  
where \(k_{\text{cutoff}}\) is the cutoff frequency defining the boundary between the low and high frequencies. The filtered signal \(X_{\text{filtered}}(k)\) is then obtained by multiplying the DFT coefficients \(X(k)\) by the filter \(H(k)\):  
\[X_{\text{filtered}}\ (k)=X(k)\!\cdot\!H(k) \tag{9}\]  
By performing the inverse DFT (IDFT) on the filtered frequency components, the signal is transformed back into wavelength domain. The resulting signal now contains only the smooth, slowly varying features, which correspond to the low-frequency components of the original spectrum.  
In Figure 9, the impact of applying low-pass filtering on the spectrum is illustrated. Notably, the maximum intensity value, which originally corresponded to a distinct peak in the unfiltered spectrum, no longer aligns precisely after filtering. The resulting spectrum is smoother, making it easier to track gradual variations in the signal, such as those caused by sample displacement.
## 3 Research Questions  
Given the documented importance of collaboration between formal institutions and informal groups of individuals, this research compares the views of library users and librarians and other library workers with regards to valuing images shared through social media sites for preservation and inclusion in public library digital collections. Further, because of the exploratory and developmental nature of public library digital archival collections, the study examines the considerations that influence content valued for the inclusion in such collections and the reasons for doing so. By comparing the perceptions of library professionals and library users and exploring their value estimation influences, this study hopes to provide support for the notion that community repositories can be co-created.  
1. Do public library users and library professionals value images posted to social media differently?
2. What considerations influence digital images valued for inclusion in image collections in public libraries and why?
## 2 Materials and Methods
### Data Analysis
#### 2.3.3 Feature Extraction and Classification  
Extracting features that provide the most information about the stimulus-related evoked HR is one of the main challenges for the classification problem at different velocities. CSP is a widely used method of feature extraction in multivariate biological signals [38], which has found application in a diverse range of fields, particularly in motor imagery BCIs based on EEG [43] or RNIRS [44, 45]. For binary classification, given a two-class dataset (e.g., stim-on vs. stim-off), CSP computes spatial filters that maximize the ratio of the variance of the data of one class to the variance of the other and projects the resultant data into a low-dimensional space via a linear transformation. A set of spatial filters \(W^{*}\) can be determined by solving the following optimization problem:  
\[W^{*}=\arg\max_{w}\left\{\frac{W^{\mathrm{T}}\Sigma_{\mathrm{L}}W}{W^{\mathrm{ T}}\Sigma_{\mathrm{2}}W}\right\} \tag{1}\]  
where \(\Sigma_{c}\) is the covariance matrix corresponding to the class \(c\). Given \(W^{*}\), the filtered signal can be presented as:  
\[\overline{x}_{i}=W^{\mathrm{*}T}x_{i} \tag{2}\]  
Assuming that the spatial feature vectors captured by CSP follow a Gaussian distribution, i.e., \(\overline{x}_{i}\sim\mathcal{N}(\mu,\,\Sigma)\), and are independent and identically distributed (i.i.d.), we apply RDA for classification of combined HbO and HbR time series to the provided velocity labels. K-fold cross validation (\(K=10\)) is used to learn the hyperparameters of the classifier, including shrinkage and regularization parameters, and to maximize the AUC.  
\begin{table}
\begin{tabular}{c|c c c c c c c c c c} \hline \hline  & AK118 & AM113 & AR127 & BH119 & EB112 & EC130 & EP129 & KB132 & KR134 & NG135 & NP105 \\ \hline Passive & **63.5\%** & **26.9\%** & **50.0\%** & **80.8\%** & **71.2\%** & **23.1\%** & **38.5\%** & **55.8\%** & **21.2\%** & **44.2\%** & **40.4\%** \\ Active & **59.6\%** & **86.5\%** & **73.1\%** & **73.1\%** & **86.5\%** & **94.2\%** & **63.5\%** & **73.1\%** & **67.3\%** & **88.5\%** & **82.7\%** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Percentage of channels per subject remaining in the feature space by meeting the AUC \(>0.6\) threshold.  
Figure 2: Stim-on vs. Stim-off discriminatory contribution of each link across 11 participants when both HbO and HbR are fed to the RDA classifier. Channels with AUC \(>0.6\) where chosen for feature extraction (**a**) Passive (somatosensory only) task. (**b**) Active (motor+somatosensory) task.
## 9 A Complete Pattern: WS-Trust
### Problem  
Establishing security relationships is fundamental for the interoperation of distributed systems. Without applying relevant trust relationships expressed in the same way between the involved parties, web services have no means to assure security and interoperability in their integration. How can we define a way for the parties to trust each other's security credentials?  
The possible solution is constrained by the following forces:  
* _Knowledge:_ In human relationships, we are concerned with first knowing a person before we trust her. That attitude applies also to web services. We need to have a structure that encapsulates some knowledge about the unit we intend to trust.
* _Policy consideration:_ The web service policy contains all the required assertions and conditions that should be met to use that web service. The trust structure should consider this policy for verification purposes.  
* _Confidentiality and Integrity_**:** Policies may include sensitive information. Malicious consumers may acquire sensitive information, fingerprint the service and infer service vulnerabilities. This implies that the policy itself should be protected.
* _Message integrity_: The data to be transferred between the partners through messages may be private data that need to be protected. Attackers may try to modify or replace these messages.
* _Time Validity_: For protection purposes, any interactions or means of communications (including the trust relationships) between the web services should have a time limit, that determines for how long the trust relationship is valid.
## 5 Proposed Prioritized User Association Algorithm  
The problem proposed in Equation (11a) is mathematically challenging because of a nonconvex objective function and nonlinear constraints. Therefore, it is not easy to provide an optimal solution to this problem. To solve this challenging problem, we adopted a Q-learning-assisted \(\epsilon\)-greedy algorithm that maximized the proposed reward function (\(\mathcal{R}\)) defined as  
\[\mathcal{R}=\underbrace{C_{m,m\
eq 0}C_{m,m=0}^{2}}_{(a)}-\underbrace{(C_{m,m=0}-C_{ \min})}_{(b)}-\underbrace{(C_{m,m\
eq 0}-C_{\min})^{2}}_{(c)} \tag{12}\]  
In Equation (12), the term (a) implies that the higher the sum rate for users, the higher the reward. Moreover, we can notice that the GBS user sum rate is squared, which in turn prioritizes the emergency users in the proposed scenario. The terms (b), (c) are the deviations of the users' rate from their defined threshold, which in turn are subtracted from the reward.  
The key steps involved in the proposal are to apply the \(k\)-means algorithm to form a cluster that places the ABS in the optimum 3D position and then assign the optimum power and associate the users to the ABS and GBS by adopting the [PERSON]'s equation given by:  
\[Q(s_{t};a_{t})=\max_{a}(E[\mathcal{R}_{t}+\beta Q(\hat{s}_{t},a_{t})]) \tag{13}\]  
Here, \(E\) is the expectation operator and \(\beta\) the discount factor that lies in the range \(0\leq\beta\leq 1\). In Q-learning, the temporal difference (TD) is used to approximate the Q-function. For the Q-learning approach, the easiest one-step approach, adopted here, is to calculate  
\[Q(s_{t},a_{t})\leftarrow(1-\alpha)Q(s_{t},a_{t})+\alpha max_{\hat{a}_{t}}( \mathcal{R}_{t}+\beta Q(\hat{s}_{t},\hat{a}_{t})) \tag{14}\]  
where \(\alpha\) is the learning rate.  
After defining all the initial parameters, the agent (ABS) performs actions by allocating discrete power between \(P_{\min}\) and \(P_{\max}\) in every state and calculates the corresponding reward, and then updates the Q-table. Once the Q-table is updated, it results in a maximum number of connected users with the highest reward for the given optimized ABS location. Iterations are performed until the maximum reward is achieved. We summarized the proposed scheme in Algorithm 1.  
```
1 Input: Declare key variables and initialize the Q-table (\(Q(s_{t},a_{t})=0\));
2 Output: Optimized user association;
3 for iterations \(=1:\) maxIteration do
4 Choose action (\(a_{t}\)) from a given set of actions (i.e., user association combinations for various ABS heights);
5 Take action and observe the reward using (12), and move to the next state (\(x_{t+1}\));
6 Update the Q-table using \(Q(s_{t},a_{t})\leftarrow(1-\alpha)Q(s_{t},a_{t})+\alpha max_{\hat{a}_{t}}( \mathcal{R}_{t}+\beta Q(\hat{s}_{t},\hat{a}_{t}))\);
7
8 end for Return Optimum user ABS association that maximizes the sum rate.
```  
**Algorithm 1**Prioritized User Association for Sum-Rate Maximization Algorithm.
# Frequency Contour-Strip Method for Characterization of Damage in Structures under Noisy Conditions
## 4 Proposed Frequency Contour-Strip Method with Superiority in Characterizing Damage
### Contour-Strip Method Fourmulation  
Considering the randomness of noise, the measured \(i\)th eigen-frequency, \(\omega_{i}^{*}\), is really a variable occupying an interval \(\omega_{i}\)\((1-\varepsilon_{i})\)\(<\)\(\omega_{i}^{*}\)\(<\)\(\omega_{i}\)\((1+\varepsilon_{i})\) in the light of Equation (10). In the eigen-frequency panorama, \(\omega_{i}\)\((1-\varepsilon_{i})\) and \(\omega_{i}\)\((1+\varepsilon_{i})\) can evoke two frequency contour-lines between which the bounded area forms a strip. Owing to the usual small value of \(\varepsilon_{i}\), this strip is relatively narrow, roughly giving rise to a frequency contour-strip of the eigen-frequency panorama. The projection of the frequency contour-strip onto the \(x\)-\(y\) plane produces a frequency projection strip that contains possible estimates of the crack in terms of the \(i\)th eigen-frequency under noise conditions. In a similar way, when \(\omega_{i}^{*},\ i=1,2,\cdots,n\), is considered, \(n\) frequency projection strips can be obtained in the \(x\)-\(y\) plane, each giving possible estimates of the crack with respect to that eigen-frequency. The intersection region of \(n\) frequency projection strips specify the possible locations and depths of the crack. This procedure constitutes the frequency contour-strip method for crack identification of a beam with a crack, which is a promotion of the frequency contour-line method, featuring accommodation of random noise to characterize damage in a beam. Figures 10 and 11 illustrate the use of the frequency contour-strip method involving the first three eigen-frequencies to identify a crack in the beam. According to the frequency contour-strip method, the resulting frequency contour-strips of the eigen-frequency panorama are shown in Figure 10a, while Figure 10b presents the extracted frequency contour-strips to provide a clearer presentation. Figure 11a shows the frequency projection strips, on which the zoomed-in intersection region \(R\) is shown in Figure 11b, where the translucent setting of frequency projection strips clearly depicts \(R\) as a polygon with \(p\) vertexes \((x_{1},y_{1})\), \((x_{2},y_{2})\),..., \((x_{p}\),\(y_{p})\). Differing from the frequency contour-line method, which designates a crack with a single solution, the frequency contour-strip method characterizes a crack with a set of possible estimates of crack parameters. From the intersection region \(R\), a sole estimate of crack location and depth, more suitable for characterizing a crack in noisy conditions, can be identified with \(p\) vertices as  
\[\mu_{x}=EX=\frac{1}{p}{\sum_{i=1}^{p}x_{i}} \tag{11a}\] \[\mu_{y}=EY=\frac{1}{p}{\sum_{i=1}^{p}y_{i}} \tag{11b}\]  
Figure 9: \(\varepsilon\)-noise cube.
## 6 Conclusions  
To select a suitable edge server for a task, computation offloading in MEC is characterized by low latency, energy consumption, and server usage. The results of task offloading to mobile users are delayed by frequent user mobility. COME-UP is enabled by novel method that predicts the mobile user's next location and direction, from where all edge servers are shortlisted. COME-UP determines the best server among the shortlisted ones by using weighted sum method and place the incoming task accordingly. COME-UP becomes a lightweight technique as it uses LSTM for training and prediction instead of sophisticated deep learning models. The performance tests reveal that COME-UP outperforms the others. The average values show the improvement as 32 percent reduction in latency, a 16 percent energy savings, and a 19 percent increase in resource (CPU) utilization. It is also noted that COME-UP holds good accuracy levels during training and testing.
## 4 Results and Discussion
### Model Validation  
In addition to the verification with the numerical results, the analytical model is also validated against the experimental data from the literature. Recently, [PERSON] et al. [40] conducted a field-scale experiment of a 500 m coaxial borehole heat exchanger system, and the in situ ground temperature was measured over time. To proceed with the validation against this field-scale experiment, the properties (related to the geometry and thermal physics) and the borehole heat flux are both necessary for the input of our developed analytical model. Firstly, consistent geometrical and thermophysical properties of the ground and working fluid (i.e., water) are prescribed in the analytical model, as listed in Table 2. It is noted that the analytical model only requires the specific heat of the working fluid to calculate the borehole heat flux, and thus there is no need to present other properties in the table here. Secondly, the borehole heat flux is calculated by \(q=\tilde{m}c_{p,water}(T_{outlet}-T_{inlet})/L\), where \(\tilde{m}\) is the mass flow rate of the water (1.66 kg/s here), \(L\) is the depth (using 250 m since the top half of the borehole is covered by the grout with a relatively high thermal resistance), and \(T_{inlet}\) and \(T_{outlet}\) are the inlet and outlet temperatures (\(T_{inlet}=70\)\({}^{\circ}\)C and \(T_{outlet}\) is interpolated over time). The borehole heat flux is, therefore, expressed as \(q=7.309\times 10^{4}t^{-0.4679}+713.4\).  
Figure 8: Comparison of computation between the analytical and numerical models.  
Figure 9 shows the validation results regarding the temperature profile at a 400-m depth for four measuring days: the 6 th, 11 th, 16 th, and 21 st days. The field-scale experiment has two spatial measurements: one at the borehole (\(x=\mid a\mid\)), and another one at 2.5 m away (\(x=\pm 2.5\) m). It is rather clear that the developed analytical solution is capable of predicting the temperature around the boreholes with a great validation against the field-scale experiment. The MAD between the analytical solution and the experimental data (here it can be also called the absolute error) is found to be within the range of 0.4727%\(\sim\)6.2952%.
## 3 Results and Discussion  
### Background-Temperature Effect on Enhanced Biological Phosphorus Removal (EBPR) without Cu(II)  
#### 3.1.1 Anaerobic Metabolisms  
Figure 1a shows the experimental results of the anaerobic metabolisms of PAOs at different temperatures. Both the specific phosphate release rate (SPRR) and specific substrate uptake rate (SSUR) of PAOs in the anaerobic stage increased with an increasing temperature from 10 to 30 \({}^{\circ}\)C. The results were similar to [PERSON] et al. [23], who studied the long-term temperature effect of EBPR at 20-35 \({}^{\circ}\)C. Nevertheless, the result of SSUR differed from [PERSON] and [PERSON] [24], who found the SSUR in a PAO-enriched sludge increased as temperature increased from 10 to 20 \({}^{\circ}\)C, whereas the difference in the acetate uptake rate between 20 and 30 \({}^{\circ}\)C was insignificant.  
In Figure 1a, the anaerobic PHA synthesis rates also increased with an increasing temperature, similar to phosphate release and acetate uptake, indicating the energy required for PHA synthesis of PAOs was mainly supplied by the hydrolysis of poly-P. Similar trends were found for the rates of glycogen degradation, which provided the reducing power for PHA synthesis, indicating PAOs provided more reducing power for PHA synthesis via degrading more glycogen while the temperature was increased.  
Figure 1c shows the temperature effects on the ratios of P/HAc, PHA/HAc, and glycogen (Gly)/HAc in anaerobic conditions. The P/HAc values were nearly fixed around 0.204-0.226 P-mol/C-mol, regardless of temperature variation. Past studies obtained similar P/HAc values, e.g., 0.178-0.263 [23], 0.190 [30], and 0.132-0.173 [31]. This stoichiometric parameter is temperature-independent, as suggested by [PERSON] et al. [31]. The Gly/HAc was also stable around 0.493-0.549 C-mol/C-mol, regardless of temperature. These Gly/HAc values were close to that proposed by [PERSON] et al. [30], who obtained a Gly/HAc value of 0.5 C-mol/C-mol in an enriched PAO culture system. The PHA/HAc values at 10, 20, and 30 \({}^{\circ}\)C were 1.18, 1.05, and 1.12 C-mol/C-mol, respectively. These PHA/HAc values were also close to that proposed by [PERSON] et al. [30], who obtained a PHA/HAc value of 1.33 C-mol/C-mol in an enriched PAO culture system. [PERSON] et al. [31] obtained PHA/HAc value of 0.73-1.01 C-mol/C-mol in the temperature range 5-30 \({}^{\circ}\)C, and they also found a slight temperature effect on this ratio.  
The PHA/Gly values, regarding the supply of reducing powers for PHA synthesis, at 10, 20, and 30 \({}^{\circ}\)C were 2.40, 1.96, and 2.04 C-mol/C-mol, respectively, as shown in Figure 1d. And, the PHA/P-released values, concerning energy supply for PHA synthesis, at 10, 20, and 30 \({}^{\circ}\)C were 5.77, 5.02, and 4.95 C-mol/P-mol, respectively.  
Figure 1: Relations between temperature and (**a**) the anaerobic metabolic rates; (**b**) the aerobic metabolic rate; (**c**) ratios of P-release/acetate (HAc), glycogen (Gly)/HAc, and polyhydroxyalkanoate (PHA)/HAc; (**d**) PHA synthesis/P-released, PHA synthesis/Gly degradation, Gly synthesis/PHA degradation, and P-release/PHA degradation.
# VNF-Enabled 5G Network Orchestration Framework for Slice Creation, Isolation and Management
## 4 Proposed Strategy
### Slice Management through Resource Transfer (SMART)
#### 4.3.2 VNF for Inner Slicing  
This procedure begins when one of the slices (i.e., eMBB, uRLLC, or mMTC) of the current NSR fails from provisioning. VNF-M searches for the potential of delivering service to the failed nodes in the remaining two slices until it finds the resources in the associated slice of PI which are treated as \"Alien Nodes\". In other words, Alien Nodes are nodes of unsuccessful NSR slices that have been provisioned in two unassociated slices which are referred to as \"Alien Slices\". The concept of inner slicing and Alien Nodes is depicted in Figure 3b. As per the figure, the first part assumes that the eMBB nodes of the NSR slice are unsuccessful and the remaining uRLLC and mMTC slices are successful. This situation is realized by VNF-M which attempts to distribute resources from the mMTC and uRLLC slices. To begin, the VNF-M tries to assign resources from the mMTC slice, but if there are not enough resources available, it searches for resources for Alien Nodes in uRLLC. The provisioned eMBB nodes are becoming the Alien Nodes of the mMTC slice. The Alien Nodes are denoted as Ae1 and Ae2 which are allocated in mMTC. The eMBB nodes use the resources of mMTC and uRLLC until they find the space in the eMBB slice of PI. The same inner slicing is applicable in case of uRLLC or mMTC becomes unsuccessful. An improvement in resource efficiency and acceptance ratio is achieved by transferring available resources from successful slices to the failing slice.  
Figure 4: Workflow diagram for slice management through resource transfer (SMART).
# Low-Carbon Certification Systems in Agriculture: A Review
## 3 Existing Low-Carbon Certification Systems
### EU Countries  
#### 3.1.1 Solagro (EU)  
In 2010, the European Commission, following a request from the European Parliament, carried out a pilot project on the \"certification of low-carbon farming practices in the European Union\" to promote reduction in GHG emissions in the sector [20]. The overall aim was to incorporate into policies initiatives that would be implemented by European farmers in order to promote the reduction in GHG emissions in agriculture and produce agricultural products with carbon-neutral or low-carbon-footprint farming practices.  
The project included the following stages: (i) a review of existing farm-level lifecycle-based climate-related certification and labeling schemes, (ii) the development and testing of a user-friendly open-source carbon calculator suitable for assessing the lifecycle of GHG emissions from different types of farming systems across the entire EU, and (iii) the design/assessment of policy options for promoting low-carbon farming practices. The findings of this study indicated that there exist multiple options for using a farm-level carbon calculator for promoting low-carbon farming practices in the EU. An Excel-based carbon calculator was developed in the frame of the SOLAGRO project and evaluated by farmers in different EU member states (Figure 2) [47]. It was concluded that it is possible to envisage a certification scheme based on this calculator that would inform the granting of subsidies or assist in allocating support for rural development measures addressing climate change issues. However, after its development, no additional efforts or other initiatives were followed.  
Figure 2: Excel-based carbon calculator developed from the SOLAGRO project.
# Recent Progress on Novel DSP Techniques for Mode Division Multiplexing Systems: A Review
## 5 Novel DSP Techniques for MIMO Systems
### Artificial Intelligence (AI) Technology for MIMO Equalization
(**a**) WDM QPSK CO-OFDM at 3200 km; (**b**) single-channel 16 QAM CO-OFDM at 2000 km.