esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
Nevertheless, the lower values were measured in the leaves of the plants irrigated with the highest amount of Mg, while, in the same treatment, the roots of the plants contained the highest level of carbohydrates (Figure 1c). Under Mg deficiency conditions, the accumulation of non-structural carbohydrates (free-soluble) in plant leaves is usually observed [2]. However, a similar decrease in soluble carbohydrates in \"Newhall\" orange leaves, with increasing Mg in the nutrient solution from 0 to 192 mg L\({}^{-1}\), was not observed. This can probably be explained by the fact that even plants irrigated with a solution without Mg showed no symptoms of Mg deficiency, as inferred from chlorophyll concentrations and plant weights that were not significantly affected. In addition, the absence of symptoms of Mg deficiency in the treatment of 0 mg Mg L\({}^{-1}\) is probably related to the short duration of the experiment, combined with the excellent nutritional status of the plants at the beginning of the experiment (i.e., the pre-existing amount of Mg in the plant tissues did not have time to dilute much). It is well-known that a sufficient Mg supply is necessary for the photosynthetic absorption of CO\({}_{2}\)[43; 54]. Since electrons and excitation energy not employed in photosynthetic processes produce excessive ROS generation in cell compartments, restricted absorption under magnesium deficiency is known to increase oxidative stress [43]. Proline contributes to the antioxidant protection of plants [55]. However, no significant interaction between the concentration of Mg in the nutrient solution and the concentration of proline in leaves and roots of the sweet orange plants was recorded (Figure 1c). Figure 1: Effect of Mg concentration of the nutrient solution (0–192 mg L\({}^{-1}\)) on (**a**) \(\mathrm{F_{v}/F_{m}}\), \(\mathrm{F_{v}/F_{0}}\) of leaves, (**b**) chlorophyll content of leaves, and (**c**) carbohydrate and proline content of leaves and roots of sweet orange plants. (All the values are the means of six replications \(\pm\) S.D. Means followed by the same letter in the same column are not significantly different at \(p\) < 0.05 ([PERSON]’s multiple range test, \(n\) = 6)).
## 3 Applications and Numerical Results
### Propagation in Ferrites  
As a last example, we investigate the case of wave propagation through a ferrite slab. The nonzero elements of the permeability tensor are given by [22]  
\[\mu_{xx}(\omega) =\mu_{yy}(\omega)=\mu(\omega) \tag{19a}\] \[\mu_{yx}(\omega) =-\mu_{xy}(\omega)=j\kappa(\omega)\] (19b) \[\mu_{zz}(\omega) =1 \tag{19c}\]  
Figure 2: (**a**) The ordinary and extraordinary relative permittivities of 5 CB in the low THz spectrum. (**b**) Transmittance calculated by the proposed FDTD method and the Berreman method.  
with  
\[\mu(\omega) =1+\frac{\left(\omega_{0}+j\omega a\right)\omega_{M}}{\left(\omega_{0 }+j\omega a\right)^{2}-\omega^{2}} \tag{20a}\] \[\kappa(\omega) =\frac{\left(\omega_{0}+j\omega a\right)\omega_{M}}{\left(\omega_{ 0}+j\omega a\right)^{2}-\omega^{2}}, \tag{20b}\]  
where it is assumed that the magnetic bias is in the \(z\)-direction. Loss is accounted for via the complex resonant frequency; namely, \(\omega_{0}\rightarrow\omega_{0}+j\omega a\). The CCPR parameters for this case are given in Table 3 and they are described by modified Lorentz relations.  
A computational domain with 5000 cells with a cell size of 15 \(\upmu\)m is assumed, which is backed with a 12-cell CPML. A ferrite slab of thickness 15 mm is surrounded by air, and an amplitude-modulated Gaussian pulse with a frequency range from 500 MHz to 14 GHz is excited and impinges upon the slab. The gyromagnetic response frequency is \(\omega_{0}=2\pi\times 4\times 10^{9}\) rad/s, while the saturation magnetization frequency is \(\omega_{M}=2\pi\times 5.6\times 10^{9}\) rad/s with \(a=0.05\) and \(\varepsilon_{r}=10\)[26]. The transmission and reflection coefficients for RCP and LCP waves are calculated using the proposed formulation and compared to the finite element method (FEM) solution implemented in the software COMSOL Multiphysics. A direct comparison of the results is provided in Figure 3, once again demonstrating the efficiency of the proposed FDTD algorithm.
## 4 Discussion
They achieved high accuracies (R2-score > 0.992), while reducing the inference time by a factor of over 100 in comparison to FEM simulations. As mentioned before, designing the architecture is not a straightforward process; however, if the network is deep enough and suitable optimization methods are available (e.g., Adam optimizer) the network can be also efficiently trained utilizing early stopping. As already reported in literature [32; 33; 34; 35], we experienced in our setting that PINNs are not straightforward to design and train. Due to several plateaus in the loss function, early stopping did not prove to be effective. Therefore, we set a fixed number of training epochs. One reason for our observation could be the existence of a non-convex Pareto frontier [36]. In the multi-objective optimization problem, the optimizer might attempt to adjust the model parameters while situated between the different losses, leading it to favor one loss at the expense of the other [37]. Possible approaches to overcome this problem are adaptive optimizers [38], adaptive loss [39], and adaptive activation functions [40]. Moreover, PINNs are objects of current research and will gain more and more attention in the future. Besides other fundamental methods, we additionally plan to aim in that direction for improved surrogate modeling.
# Spam Review Detection Techniques: A Systematic Literature Review
## 2 Unsupervised Learning
### K-means Clustering  
K-means clustering has been shown to work well for large-scale data and its accuracy level is also high compared to other clustering algorithms [68]. The K-means clustering algorithm collects the extracted terms according to their feature values into K number clusters, and K is any positive number that is used to determine the number of clusters. The K-means clustering algorithm performs the following steps.  
1. Pick a number (K) of cluster centers (at random)
2. Assign every item to its nearest cluster center (e.g., using Euclidean distance)
3. Move each cluster center to the mean of its assigned items
4. Repeat steps 2 and 3 until convergence is achieved (change in cluster assignments less than a threshold)  
Specifically, previous works reported that K-means clustering yields promising results in the domain of opinion mining and spam detection [69]. [PERSON] et al. [62] reported 71% precision by employing K-means clustering on Chinese language reviews about products. [PERSON] et al. [60] used a K-means approach on mobile phone reviews and reported 72% accuracy.
# Pilot-Scale Fenton-like System for Wastewater Treatment Using Iron Mud Carbon Catalyst
## 3 Results and Discussion
### Analysis of Economic and Environmental Benefits of Heterogeneous Fenton Pilot  
According to the experimental results, the amount of FSC is about 144 kg (the total catalyst is 360 kg, according to the total catalyst 2/5), the operation hours are 1247 h, the inlet flow rate is 2.18 L/min, and the total amount of treated water is 2.18 \(\times\) 60 \(\times\) 1247 = 163.1 t. According to the current catalyst failure calculation, about 0.88 kg FSC per ton of water is added. According to the experience of biochar preparation in the park, the average preparation cost per ton of biochar is about 68.95 dollars, and the cost of biochar per ton of water is 0.0607 dollars. According to the optimal oxidation conditions optimized in this experiment, the concentration of H\({}_{2}\)O\({}_{2}\) is 1.2 mmol-L\({}^{-1}\) and the concentration of potassium ferrite is 0.15 mmol-L\({}^{-1}\). The treatment of each ton of wastewater requires 169.83 mL H\({}_{2}\)O\({}_{2}\) and 37.13 g potassium ferrite, with the market prices of H\({}_{2}\)O\({}_{2}\) 904 USD/t and potassium ferrite 9000 USD/t as references. The cost of the oxidizers required to treat each ton of water is 0.9270 USD/t, and sulfuric acid and potassium hydroxide are used to adjust the pH. The cost of the acid-base buffer solution per ton of water is 0.7324 USD/t, so the cost of treating each ton of wastewater in the advanced oxidation stage is 0.8409 USD/t. The project also carried out cost calculations for Fenton technology and ozone technology, as shown in Table 3. After accounting, the cost of Fenton technology and ozone technology is 1.0505 and 1.4052 USD/t of sewage, respectively, and the cost of FSC/Fe(VI)/H\({}_{2}\)O\({}_{2}\) is lower than the other two technologies. The data is sourced from a water plant in Wuxi, China (Table 4). This result not only provides strong data support for the technology selection of enterprises, but also provides an important theoretical basis for the technical optimization and cost control of the sewage treatment industry.  
Figure 8: Mechanism scheme of organics degradation in the FSC/Fe(VI)/H\({}_{2}\)O\({}_{2}\) system.
# Dynamic Modeling and Optimization of Tension Distribution for a Cable-Driven Parallel Robot
## 4 Optimization Algorithm of Tension Distribution
### Factors Affecting Minimum Cable Tension
#### 4.5.2 Strength Limitation of Cable Material  
The material strength limit of the cable is the maximum tension that the cable can withstand, and this parameter is determined by the material properties of the cable. Excessive tension will cause the cable to break, which will affect the safety and stability of the system.
# Hyperbolic Asynchronous Method of a Radio Navigation Technique
## 5 Measurement's Verification of the Asynchronous Method of Radio Navigation
### Analysis of Measurement's Investigation Results  
The demonstrator of the technology of asynchronous object localization, which was used in the measurement tests, is characterized by the following system parameters, resulting from the obtained permissions to access the electromagnetic spectrum:  
* the 431.5 MHz operating frequency,
* operating band width adjustable from 1 MHz to 4 MHz,
* the power of the signals transmitted 30 W.  
A scenario of measurement tests carried out in the waters of the Gulf of Gdansk is shown in Figure 20, where the measurement route of the floating laboratory and the location of the reference stations are marked, which is the same as in the case of the computational tests described on page 2. In this scenario, a bandwidth of 1 MHz was used.  
Figure 20: Measurement scenario of tests carried out in the Gulf of Gdansk.  
A total of seven thousand measurement cases were collected. The results of these studies were used to verify computational (theoretical) tests. They are presented in the following way. Figure 21 presents graphs of absolute error \(\delta_{d}[m]\) distribution for both configurations of reference stations. An absolute error (Equation (17a)) has been calculated as a difference between the position given by the asynchronous system and the position provided by an GNSS receiver which uses both GPS and GLONASS satellite systems to calculate its position [26; 27]. On the other hand, Figure 22 presents a histogram of this error, also for both configurations of reference stations.  
Figure 21: CDF related to absolute error \(\delta_{d}[m]\) of results of measurements made in the Gulf of Gdárásk.  
Figure 22: Histogram of the absolute error \(\delta_{d}[m]\) of the results of measurements made in the Gulf of Gdárásk.  
In addition, Table 3 lists errors \(ME_{\delta}\), \(MSE_{\delta}\) and \(RMS_{\delta}\), calculated on the basis of the obtained results of measurement tests carried out in the coastal sea area.  
Figure 24: CDF related to absolute error \(\delta_{d}[m]\) of the results of measurements made on the coastal sea.  
Figure 25: Histogram of the absolute error \(\delta_{d}[m]\) of the results of measurements made on the coastal sea.
# A Joining Procedure and Synchronization for TSCH-RPL Wireless Sensor Networks
## 7 Conclusions and Future Works  
The presented work analyses the common problems and obstacles for successfully deploying a WSN. This type of network is of interest for their many possible applications and versatility. However, to enable robust operation in industrial scenarios, which are much more demanding, the mechanisms and features that are provided by IEEE802.15.4e and its TSCH mode should be used, such as deterministic, scheduled traffic with channel-hopping possibilities. Nevertheless, different experiments and a deeper analysis have proven that, to achieve a more functional, practical, and optimized network deployment, some more thought has to be directed towards communication configuration parameters and synchronization mechanisms.  
Figure 14: The power consumption for different EB periods over 100 h.  
The recommendations and implementations that can be found in the standard and popular WSN software distributions fail to optimize critical factors, such as connection success probability, the connection time required, and even energy consumption. In this study, a new configuration, Custom Trickle Timer, has been proposed that introduces significant results and improvements in all three factors with respect to the common configurations.  
Still, this work shows interesting room for improvement. A start would be to tweak the variable EB period mechanism so that it changes dynamically in a wider range of values (not limited to 4 and 16 s), and is able to restart when needed by joining new nodes after a long period of operation.  
Formal analysis, S.S.-C.; Investigation, J.V.-P., D.T.-F., and S.S.-C.; Supervision, V.S.-P.; Validation, J.S.-B.; Writing (original draft), J.V.-P. and D.T.-F.; Writing (review & editing), J.S.-B. and V.S.-P.  
This work has been supported by the SCOTT Project (Secure Connected Trustable Things), (www.scottproject.eu), which has received funding from the Electronic Component Systems for European Leadership Joint Undertaking under grant agreement No 737422. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme, and from Austria, Spain, Finland, Ireland, Sweden, Germany, Poland, Portugal, Netherlands, Belgium, and Norway.  
The authors declare no conflict of interest.
## 4 Possible Methods for Computing Probability of Failure for Inspection Planning
### Direct Monte Carlo Simulation  
Using the direct MSC method, the probability of failure (\(p_{f,M}\)) can be computed as [26; 41]:  
\[p_{f,M}=\frac{1}{N_{s}}\sum_{i=1}^{N_{s}}I_{f}(\mathbf{x}_{i}) \tag{2}\]  
where \(N_{s}\) is the number of samples, \(I_{f}(\mathbf{x}_{i})\) is the indicator function of the random variable associated with the \(i^{\text{th}}\) sample, and \(I_{f}(\mathbf{x}_{i})\) is equal to one for \(g(\mathbf{X}_{i})<0\) and equal to zero otherwise. The direct MSC is a general and simple method for solving high-dimensional reliability problems. However, \(p_{f,M}\) cannot be obtained when the number of samples (\(N_{s}\)) is less than \(1/p_{f,M}\). Furthermore, additional samples and computational costs are required during the computation of \(p_{f,M}\). Therefore, it is inefficient in computing a low probability of failure.
# Development of Cryogenic Detectors for Neutrinoless Double Beta Decay Searches with CUORE and CUPID
###### Abstract  
Searching for neutrinoless double beta decay is a top priority in particle and astroparticle physics, being the most sensitive test of lepton number violation and the only suitable process to probe the Majorana nature of neutrinos. In order to increase the experimental sensitivity for this particular search, ton-scale detectors operated at nearly zero-background conditions with a low keV energy resolution at the expected signal peak are required. In this scenario, cryogenic detectors have been proven effective in addressing many of these issues simultaneously. After long technical developments, the Cryogenic Underground Observatory for Rare Events (CUORE) experiment established the possibility to operate large-scale detectors based on this technology. Parallel studies pointed out that scintillating cryogenic detectors represent a suitable upgrade for the CUORE design, directed towards higher sensitivities. In this work, we review the recent development of cryogenic detectors, starting from the state-of-the-art and outlying the path toward next-generation experiments.  
neutrinoless double beta decay; lepton number violation; rare event search; cryogenic detector +
Footnote †: journal: Physics Letters
# Optically Transparent Honeycomb Mesh Antenna Integrated into OLED Light Source
## 2 Material Characteristic  
This article proposes the study of a transparent antenna integrated into an OLED light source to provide better connectivity for wireless communications. The proposed antenna is designed using transparent materials deposited on a glass substrate. Firstly, the characteristics of the materials used in this design are studied. These characteristics are the optical and dielectric parameters of the glass substrate and the sheet resistance and transparency of the ITO used in antenna manufacturing.
## 3 System Model
### Channel Gain  
The system operates in a Rician fading channel, and the PDF for the channel gain can be calculated using Equation (4), where \(A^{2}\) is the power of the LoS components and \(2\sigma^{2}\) is the power of the NLoS components.  
\[p_{R}(r)=\frac{r}{\sigma^{2}}\cdot\exp\biggl{(}-\frac{r^{2}+A^{2}}{2\sigma^{2} }\biggr{)}\cdot I_{0}\biggl{(}\frac{Ar}{\sigma^{2}}\biggr{)} \tag{4}\]  
This can also be written using the Rician factor \(K\) as [28]:  
\[K=\frac{A^{2}}{2\sigma^{2}}. \tag{5}\]  
The average envelope of the distribution for \(R\) in Equation (4) can be given as \(E\{R^{2}\}=\overline{R}=A^{2}+2\sigma^{2}\); hence, we can rewrite the PDF for the channel distribution as follows:  
\[P_{R}(r)=\frac{2r(K+1)}{\overline{R}}\exp\Bigg{(}-K-\frac{r^{2}(K+1)}{ \overline{R}}\Bigg{)}I_{0}\biggl{(}2r\sqrt{\frac{K(K+1)}{\overline{R}}}\Bigg{)} \tag{6}\]
## 4 Experimental Analysis
### Ablation Study
#### 4.5.1 Effectiveness of Encoder  
In order to compare our proposed Window Grouping Attention (WGA) mechanism in the swin transformer encoder by comparing it with the standard window-based attention (W-MSA), we conducted an ablation study to evaluate the impact of our model. The results, which are shown in Table 4, demonstrate how well WGA works to enhance reconstruction performance for different input view counts. This efficiency suffers extensively when WGA is replaced with W-MSA, which handles each view separately without inter-view communication, especially when the number of views increases. For example, for three input views, W-MSA has an IoU of 0.7562, which is significantly lower than the WGA-enabled configuration, which reaches 0.7636. However, when increasing the performance difference, the number of input views increases. Finally, WGA's IoU was 0.7819 at 16 views, while W-MSA's IoU fell to 0.7783. At 20 views, WGA's IoU was 0.7827, 0.33% higher than W-MSA's.  
In contrast, these results show that WGA is essential for establishing inter-view communication, which improves the model's ability to represent inter-view dependencies. On the other hand, as the number of input views increases, a W-MSA that handles views independently will reach performance saturation. As an example of WGA's scalability, it significantly increases IoU performance across all view configurations while preserving model efficiency by focusing on grouped tokens. As a result of these findings, it can be determined that when dealing with a large number of input views, WGA plays an essential role in robust feature extraction in the encoder, thus enabling superior multi-view 3D reconstruction.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**WGA** & **W-MSA** & **3 Views** & **5 Views** & **8 Views** & **12 Views** & **16 Views** & **20 Views** \\ \hline \(\times\) & \(\checkmark\) & 0.7562 & 0.7679 & 0.7713 & 0.7758 & 0.7783 & 0.7790 \\ \(\checkmark\) & \(\times\) & **0.7636** & **0.7748** & **0.7786** & **0.7808** & **0.7819** & **0.7827** \\ \hline \hline \end{tabular}
\end{table}
Table 4: An ablation experiment examining the impact of Window Grouping Attention (WGA) and window multi-head self-attention (W-MSA). Based on ShapeNet, IoU metrics are used to evaluate the experiments.  
Figure 5: The results of single-view 3D reconstruction on the test set of Pix3D.  
Additionally, Table 5 presents IoU scores for token grouping parameters G of 36, 49, and 64 across multiple input views. A grouping parameter of 36 consistently produces lower IoU scores, indicating that it fails to capture essential feature interactions, especially at lower view counts. On the other hand, 64 shows stable performance, but the gains over 49 are marginal, suggesting diminishing returns and unnecessary computational complexity. In contrast, 49 provides the best balance, achieving the highest reconstruction accuracy without excessive computational cost. Therefore, 49 was chosen for its optimal performance and efficiency across varying numbers of views.
## 3 The Proposed Algorithm
### The Pseudo-Code of the Proposed Algorithm  
A scenario of training a dataset and classifying the outcomes into three classes will be shown as an example. The distance matrix for each class is constructed (\(\mathrm{D_{M}}\))\({}_{ci}\) and the summation of every row represents the similarity between this particular point (row) and the other points within the same class. The point that achieves the highest similarity or has the minimum sum is selected as a core for the class (ci), and thus it is the best representation of that particular class. The test points will be classified accordingly to satisfy the similarity with these cores instead of using K nearest neighbors, as in the KNN algorithm.  
Consider S as a given dataset which consists of n numbers of points S: = p1, p2,..., pn which are classified into three classes, and each point has a form of pi = (xi,ci), where xi denotes the feature vector of the point pi and ci denotes the class that pi belongs to.  
Suppose that x is a point that needs to be classified to an appropriate class using CCA (Algorithm 1).  
1. Calculate distance matric for the three classes (\(\mathrm{D_{M}}\))\({}_{c1}\), (\(\mathrm{D_{M}}\))\({}_{c2}\), (\(\mathrm{D_{M}}\))\({}_{c3}\).
2. Find the core for each class (Cor)\({}_{c1}\), (Cor)\({}_{c2}\), (Cor)\({}_{c3}\).
3. Calculate the distance between x and all cores to obtain three values: (dis1, dis2, dis3).
4. X will be classified to the class with minimum distance within (dis1, dis2, dis3).  
These steps are shown in pseudocode below and notations in Table 1:  
```
1:Input: in
2:Output: out
3:Initialisation:
4:loop process
5:for i = 1 to length of test_ds do
6:find the dis between test_ds [i] and (Cor)\({}_{cx}\)
7:if dis (1) is minimum
8: Classify test_ds[i] to class (1)
9:else if dis (2) is minimum
10: Classify test_ds[i] to class (2)
11:else
12: Classify test_ds[i] to class (3)
13:end loop
14:return Class (x)
```  
**Algorithm 1** Algorithm for CCA.