esa-sceva/satcom-chunk-collection huggingface dataset:
# Coupling and Trapping of Light in Thin-Film Solar Cells Using Modulated Interface Textures
## 2 Materials and Methods  
### ZnO:Al Front Contacts  
The ZnO:Al films that were used as front contact layers in this paper were deposited in a vertical inline sputter deposition system (_VISS 300_) by von Ardenne Anlagentechnik, Dresden, Germany. 3.3 mm thick float glass with a size of 10 \(\times\) 10 cm\({}^{2}\) (type Eurowhite(r) by Euroglas GmbH, Haldensleben, Germany) was used as substrate upon which a 120 nm SiO\({}_{x}\)N\({}_{y}\) thin-film was deposited via reactive sputter deposition [41] using a Sispa(tm) target [42; 43]. The SiO\({}_{x}\)N\({}_{y}\)-layer served as antireflection coating between the glass and the ZnO:Al and as barrier layer to prevent sodium diffusion from the substrate glass to the zinc oxide interface. The SiO\({}_{x}\)N\({}_{y}\) deposition conditions were the same for all samples. The ZnO:Al, as well as the SiO:Ny depositions, were dual magnetron assisted processes with mid-frequency excitation at 40 kHz. The deposition was conducted in a dynamic mode with the sample carrier moving in front of the target, ensuring a homogenous deposition. More details regarding the deposition system and dynamic deposition process can be found elsewhere [44]. Both processes used argon as sputter gas. The SiO\({}_{x}\)N\({}_{y}\) was sputtered at 4 kW discharge power using two 100 mm \(\times\) 750 mm planar Sispa(tm) cathodes supplied by W. C. Heraeus GmbH, Hanau, Germany, with a 10 wt. % Al content. Oxygen and nitrogen gas were supplied in the gas phase. The ZnO:Al was sputtered using two 750 mm long rotating tube targets consisting of 0.5 wt. % Al\({}_{2}\)O\({}_{3}\) in a ceramic ZnO target also supplied by W. C. Heraeus GmbH, Hanau, Germany. The discharge power was constant at 10 kW. The pressure and heater temperature were varied between the individual ZnO:Al depositions to yield different surface textures after etching. Details regarding the process conditions can be found in Table 1. The as-deposited thickness ranged between 880 and 1300 nm with the various deposition conditions. Thicknesses were determined from an etched step using a step profiler without considering volume overestimation of rough surfaces [45]. The sheet resistance was determined using a four-point probe setup. Carrier concentration and carrier mobility were measured by Hall effect measurements in van der Pauw geometry.
# Using GPS Trajectories to Adaptively Plan Bus Lanes
## 6 Conclusions  
To fill the gap related to the lack of an adaptive method for bus lane planning, this paper proposes a new method that uses bus GPS trajectories to plan bus lanes. The bus lane planning problem is formulized as a multiobjective optimization problem. The constraints include the basic attributes of road segments, traffic flows, planned construction budget, and bus lane connectivity. For problem solving, considering the conflict between the multiple objectives and the efficiency of the algorithm, an evolutionary algorithm based on NSGA2 for the bus lane planning model is proposed. Finally, we illustrate the usability of the model in actual case studies and discuss the adaptability and effects of the input parameters of the model.  
The main contribution of this article is the proposal of a GPS trajectory-driven bus lane planning method. Using GPS trajectories to obtain the traffic parameters is effective. Compared with the existing micromodels, the developed method can directly specify the traffic parameters for complex road networks at the city scale. Compared with the existing macromodels, the proposed model considers the traffic parameters for microscopic road segments, such as bus routes and the frequency and number of vehicles. Furthermore, in the objective function, a road utilization and bus punctuality function that is compatible with GPS trajectories is proposed, which provides the model with a better spatiotemporal resolution than traditional methods.  
The article uses GPS trajectories in a data-driven approach to plan bus lanes. However, there are still several issues, such as not considering the impact of other vehicles on planned road segments and other objective functions. These factors need to be addressed in future research.  
**Author Contributions:** Conceptualization: [PERSON] and M.W.; methodology: Y.S. and M.W.; software: Y.S. and H.L.; validation: Y.S., M.W., and H.L.; investigation: Y.S. and H.L.; resources: M.W.; data curation: M.W.; writing: Y.S. and M.W.; supervision: M.W.; project administration: M.W.; funding acquisition, M.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (41971417) and (41631175).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data and codes presented in this study are available from the corresponding author by request.  
The authors declare no conflict of interest.
# Cyber-Security Threats and Side-Channel Attacks for Digital Agriculture
## 3 Threats to Digital Agriculture
### Commercially Sensitive Information  
Data theft leads to the extraction of commercially sensitive information, risking small- and large-scale trade relations (farmer to a service provider or international trade). Commercially-sensitive information can be classified [30] as:  
* Competitors use production efficiency statistics in their trading decisions, putting primary producers at a competitive disadvantage. Further, growth statistics lead to targeted research and IP theft attacks.
* Land valuation data, pricing data (logistics, supply chain, invoices, etc.), trading volume, sale trends, and growth statistics provide an insight to competitors for a better bargaining edge.
* Poorly defended small agriculture businesses and farms can be targeted to steal invoice information and banking details. These poorly secured businesses become weak links that enable unauthorised access to a large-scale network.
# Automatic Extraction of Material Defect Size by Infrared Image Sequence
## 4 Data Post-Processing
### Primary Detection of Defects  
#### 4.2.1 Blob Analysis  
The blob analysis is used in the binary image, Figure 11b, by the blob analysis package in MATLAB. Defect regions are divided as blobs marked as red rectangles in Figure 12a. Features such as area (i.e., the number of pixels), center of gravity, or the orientation of a blob or blobs are calculated. Each blob contains one defect that is abstracted as a whole or portion (see the white line in Figure 12b). Moreover, a blob and a defect are one-to-one correspondence, so we can deal with blobs in turn equaling the process defects one by one. At last, the edges are abstracted by a canny operator for the further localization of the sensitive region.
###### Abstract  
The values of primary stresses are not allowed for as a criterion in the selection of roof bolting systems in mining excavations located at various depths in Polish copper one mines. Therefore, in order to ensure enduring and safe operation of excavations, in particular, those driven in unfavourable geological and mining conditions, this problem has required solutions based on numerical methods. This article presents an example of applying numerical simulations to the evaluation of the stability of headings in Polish copper ore mines. The analyses included mining excavations located at various depths in the rock mass. This issue is of great importance, as safety regulations are prioritised in mining excavations which remain in operation even for several decades. The stability of the headings was evaluated with the use of the RS2 specialist numerical simulation software. This computer program uses the finite element method (FEM) for calculations. The rock parameters used in the numerical models have been determined on the basis of the Hoek-Brown classification. For that purpose, the RocLab 1.0 software was used. The parameters of the stress field were identified from the profile of the GG-1 shaft with the assumed hydrostatic state of stress. The numerical modelling was performed in a triaxial stress state and in a plane strain state. The numerical analyses were based on the Mohr-Coulomb failure criterion. The rock medium was described with the elastic-plastic model with softening (roof and walls) and with the elastic-plastic model (floor). The results of the numerical analyses served to provide an example of the application of a roof bolting system to protect headings located at the depths of 1000 m b.g.l. and 1300 m b.g.l.  
stability of excavations; roof bolting systems; finite element method (FEM); numerical simulations; Polish copper ore mines
## 4 Statistical Analysis
### Assessing Means of Two Samples Groups with ANOVA
#### 4.1.2 Levene's Test  
The assumption for this test is that samples have a normal distribution. So, this test is applied after [PERSON]'s test. It can be confirmed with [PERSON]'s test that the variances of different samples are homogenic. The null hypothesis assumes homogeneity of variances for all analyzed samples.  
\(H_{0}-\)the variances of all samples are homogenic  
\(H_{1}-\)the variance of at least one sample is not homogenic with the variance of other samplesFigure 14: Sequence of calculations leading to ANOVA.  
This test is similar to the one-way ANOVA described below, but instead of values that belong to two (or more) samples, the absolute values of the differences between the values and mean in each group are considered. The statistics \(W\) for this test is presented in [53] and widely discussed, e.g., in [54; 55].  
\[W=\frac{n-k}{k-1}\times\frac{\sum_{i=1}^{k}n_{i}\left(\overline{z}_{i}- \widetilde{z}\right)^{2}}{\sum_{i=1}^{k}\sum_{j=1}^{n_{i}}\left(z_{ij}- \overline{z}_{i}\right)^{2}}\]  
where:  
\(n\) is the total number of cases in all samples  
\(k\) is the number of different samples to which the sample cases belong  
\(n_{i}\) is the number of cases in \(i-th\) samples  
and  
\[z_{ij}=\left|x_{ij}-\overline{x}_{i}\right|\]  
where  
\(x_{ij}\) is the \(j-th\) value in \(i-th\) sample  
\(\overline{x}_{i}\) is the mean value in \(i-th\) sample  
\(\overline{z}_{i}\) is the mean value of \(z_{ij}\) in \(i-th\) sample given by the following formula:  
\[\overline{z}_{i}=\frac{1}{n_{i}}\sum_{j=1}^{n_{i}}z_{ij}\]  
\(\widetilde{z}\) is the mean of all \(z_{ij}\) given by the formula  
\[\widetilde{z}=\frac{1}{n}\sum_{i=1}^{k}\sum_{j=1}^{n_{i}}z_{ij}\]  
As the test statistics \(W\) is approximately \(F\) distributed it is compared to \(F\) value read from [PERSON]'s tables based on confidence level \(\alpha\), and degrees of freedom, i.e., \(k-1\) and \(n-k\). The [PERSON] test rejects the null hypothesis (variances of all samples are equal) if \(W>F\ value\). Similarly to [PERSON]'s test, Statistica software, instead of giving the value of comparison of \(W\) to \(F\ value\) returns the \(p\) value. If \(p<\alpha\) the null hypothesis should be rejected.
# An Analysis of Commercial Noni Juice: LC-MS/MS Phenolic Profiles and Cytotoxic Activity
## 2 Materials and Methods
### HPLC Analysis of Ascorbic Acid  
To analyse the ascorbic acid content of the noni juice samples, we used a high-performance liquid chromatography (HPLC) method previously published by our laboratory [18]. Prior to analysis, the juice samples were 0.45 \(\upmu\)m syringe filtered, with no dilution required. The ascorbic acid content was measured using an Agilent 1100 HPLC system with an external ascorbic acid standard calibration. Results were expressed in mg L\({}^{-1}\) of the undiluted juice.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Brand (Abbreviation)** & **Country of Origin** & **Label Description** & **Typical Shelf Life** \\ \hline Tahitian Organic Noni (TON) & Tahiti & \begin{tabular}{c} Never reconstituted—always \\ fresh. No preservatives, colouring \\ agents, or sugars. \\ \end{tabular} & 3.5 years \\ \hline Cook Island Noni juice (CIN) & Cook Island & \begin{tabular}{c} 100\% _Morinda citrifolia_ fruit \\ extract. Non-Pasteurised \\ \end{tabular} & 3 years \\ \hline Dynamic Health Noni (DHN) & Tahiti & \
\begin{tabular}{c} Contains no added sugar, \\ artificial colour or preservatives. \\ Due to the pure nature \\ ingredients in this product, taste, \\ colour and consistency may vary. \\ \end{tabular} & 2 years \\ \hline Fijian Noni (FN) & Fiji &
\begin{tabular}{c} Made from pure fruits (wild \\ collection). No additives added. \\ Pasteurized for optimum quality. \\ \end{tabular} & 2 years \\ \hline Life Health Noni (LHN) & New Zealand & 100\% noni fruit juice. & 4.5 years \\ \hline \end{tabular}
\end{table}
Table 1: Label descriptions of the noni juice samples included in this study.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Parameter** & **TON** & **CIN** & **DHN** & **FN** & **LHN** \\ \hline Energy (K) & 70 & ND & 70 & 88 & 104 \\ Protein (g) & ND & ND & ND & 0.8 & 0.4 \\ Fat (g) & ND & ND & ND & \textless{}0.1 & 0.1 \\ Saturated Fat (g) & ND & ND & ND & \textless{}0.1 & \textless{}0.1 \\ Carbohydrates (total) (g) & \textless{}3.3 & ND & 0 & 3.1 & 5.50 \\ Sugars (g) & \textless{}3.3 & ND & ND & 2.5 & 3.10 \\ Sodium (mg) & ND & & ND & 8 & 16 \\ Potassium (mg) & 163.33 & ND & 100 & 220 & ND \\ Calcium (mg) & ND & ND & ND & 10 & ND \\ Magnesium (mg) & ND & ND & ND & 10 & ND \\ Vitamin C (mg) & 16.67 & ND & ND & ND & ND \\ Iron & 6.67 & ND & ND & ND & ND \\ \hline ND = no data. & & & & \\ \end{tabular}
\end{table}
Table 2: Nutritional panel information for the commercial noni juice samples. Values are provided per 100 mL of juice.
# Improved Short-Term Clock Prediction Method for Real-Time Positioning
## 3 Experiment Results
### Accuracy Verification of Short-Term Clock Prediction
#### 3.1.1 Experiment Using IGS Final Product
The accuracy of the GPS clock prediction for 1-h data modeling in Figure 6b is lower than that of 2-h and 3-h data modeling for two reasons. First, the use of 1-h observation for fitting is insufficient compared with the prediction length. Second, the aforementioned periodic signal model is more suitable for 2-h or 3-h data modeling. Compared with that in Figure 6a, the accuracy of 3-h observation prediction in Figure 6b is generally better than that of 2-h observation prediction. Furthermore, the accuracy of most of the satellite clocks is better than 0.5 ns due to the periodic correction. The defects and deficiencies of using 1-h observation based on the improved model are obvious in Figure 6c. From the analysis of all the rubidium clocks, except for a few satellites (G28), the accuracy of clock prediction with 3-h data is better than 0.7 ns. The overall accuracy of Block IIF is better than 0.5 ns. It is worth noting that the performance of G13 significantly improved, which can be helpful for predicting the clock aging. The following conclusions can be drawn from the comparison of Figure 6. First, the newest Block IIF on-board AFS has the best prediction accuracy among Block IIF, Block IIR, and Block IIR-M satellites equipped with rubidium atomic clocks; this result is consistent with the previous studies of short-term stability. Second, the accuracy distribution of each satellite in the three graphs is similar, which may be related to the clock sequence trend. Third, the relative optimal prediction accuracy can be obtained by using 3-h observation modeling, whether it is 1-h, 2-h, or 3-h clock correction forecast. The prediction accuracy of 1, 2, and 3 h is better than 0.45, 0.6, and 0.85 ns, respectively. Figure 6: Comparison of 1-h prediction (**a**), 2-h prediction (**b**) and 3-h prediction (**c**) results based on different lengths of observations.
## 2 Background Theory
### Recovery of Optical Parameters  
Characteristic optical properties of tissue are encoded in the boundary optical measurements (SRR data). A typical optical inverse problem is to reconstruct from a set of SRR measurements, the spatial distributions of the underlying optical parameters such as \(\mu_{a},\mu_{s},\mathcal{g}\) etc. that produced them, i.e., the unknown parameters in the map:  
\[\Lambda_{\mu_{a},\mu_{s},\mathcal{g}}:S_{x}(\overline{r})\mapsto\mathcal{R}_{ meas}(\overline{r}_{d})_{\mid\{\overline{r}_{d_{1}},-\overline{r}_{d_{M}}\}} \tag{3}\]  
where \(S_{x}(\overline{r})\) denotes the excitation source and \(\mathcal{R}_{meas}(\overline{r}_{d})_{\mid\{\overline{r}_{d_{1}},-\overline{ r}_{d_{M}}\}}\) represents the SRR measurements at M detectors. Over the past three decades, various optical inverse problems have been extensively explored. These problems are non-linear in nature and typically solved using non-linear optimization techniques. There is a plethora of research work involving the point-wise reconstruction of optical parameters using RTE [35; 36; 37; 38; 39; 40] as well as DA-RTE [41; 42; 43; 44; 45; 46]. These techniques are computationally expensive and need a lot of time to reconstruct the maps of desired optical parameters. Moreover, such techniques also demand a huge amount of experimental data, which is impractical when probing internal organs since the site available for observation would usually have a small area.  
Optical parameters recovered from the epithelial and stromal layers have been known to reflect changes due to carcinoma [4; 15; 25; 26; 27; 47; 48]. However, with point-wise reconstruction, there is a high probability of missing valuable information from this layered structure. Instead, their layer-wise extraction would be more efficient and accurate. This reduces the number of unknowns tremendously and consequently the small number of measurements would yield accurate recovery of the optical parameters.  
For our work, we have utilized a MC-LUT based scheme [26; 27; 28; 29; 30] to extract the optical parameters of the phantoms. Multilayered MC program [34] has been used to generate synthetic SRR data at detectors. The synthetic SRR data obtained by varying an optical parameter are tabulated into a two dimensional matrix (known as the MC-LUT). For recovery of a set of unknown optical parameters, the experimental SRR values are compared with the MC look-up table and the minimizer of the root-mean-square-error is the reconstructed set of optical parameters.
# A Survey of Handover Management in Mobile HetNets: Current Challenges and Future Directions
## 3 Background
### Handover Challenges
#### 3.5.3 Centralized Handover Optimization  
Many studies have focused on analyzing handover self-optimization at the base station level without considering individual user behaviour. This is because handover decisions are typically based on the overall network state and the signal strength of neighbouring cells rather than on individual user characteristics such as traffic patterns or mobility behaviour. However, it is becoming increasingly clear that individual user behaviour can play a significant role in handover performance. Factors such as user mobility, traffic patterns, and service requirements can all have an impact on the handover process. This implies that the centralized handovers are causing a significant challenge to the communication system's performance. As they impose higher complexity and higher signalling overhead that can cause a bottleneck or a single point of failure in the network [95; 140; 141; 142].  
In recent years, there have been some studies that have looked at optimizing handovers on a per-user basis. These studies have found that by considering individual user behaviour, it is possible to improve handover performance by reducing call drops, increasing handover success rates, and reducing call setup times. Such approaches can include techniques like analyzing the user's traffic pattern, Mobility prediction and using machine learning algorithms to learn the user behaviour and proactively trigger handover. However, these studies are still in the investigative stages, and more research is needed to fully understand the impact of user-centric handover optimization on network performance [143; 144; 145]. It's also important to note that the implementation of user-centric handover optimization may come with some complexity, such as the need for more information from the mobile devices and more computation and decision-making on the network side.
# Efficient Tree Policy with Attention-Based State Representation for Interactive Recommendation
## 3 Method
### Learning Process  
The learning process of the proposed method is illustrated in Figure 4. Firstly, we use the historical user-item matrix to extract the features for each item and users. Specifically, we use Funk SVD [45] to decompose the original user-rating matrix into an item matrix and a user matrix. The item matrix and user matrix are used as item features and user features, respectively. Then clustering is performed to separate the items into subclasses, in order to simplify the learning of the tree policy. As we use the complete tree to represent the recommender policy, the clustering tree can be constructed by applying a hierarchical clustering algorithm that can associate the leaf node with one item. We adopt a K-means-based clustering algorithm to build the clustering tree, which is depicted in Algorithm 2. Based on the clustering result, the complete tree policy is constructed, with each leaf node corresponding to a certain item id. Recommendations are provided to the user by mapping the output action of the policy to the items. During the interaction with the user, the tree-structured policy is trained through a policy gradient method.  
The learning of the tree policy can utilize any policy gradient method; here, we use the REINFORCE [46] algorithm to illustrate the learning process. Denoting the overall tree policy as \(\pi_{\theta}\), since the learning objective is to maximize the expected discounted total rewards, the loss function can be written as:  
\[J(\theta)=\mathbb{E}_{\pi_{\theta}}[\sum_{t=0}^{T}\gamma^{t}r_{t}] \tag{3}\]  
Based on policy gradient theorem, the gradient with respect to parameter \(\theta\) can be written as:  
\[\
abla_{\theta}J(\theta)\propto\mathbb{E}_{\pi_{\theta}}[Q^{\pi_{\theta}}(s,a) \
abla_{\theta}\log\pi_{\theta}(a|s)] \tag{4}\]  
where \(\pi(a|s)\)is the probability of taking action \(a\) at state \(s\) and \(Q^{\pi_{\theta}}\) is the expected discount rewards after taking action \(a\) under state \(s\). During learning, \(Q^{\pi_{\theta}}\) can be obtained by sampling the trajectories under policy \(\pi_{\theta}\) from either historical user data or online data. The whole learning algorithm is depicted in Algorithm 3.  
Figure 4: Learning process of the tree policy.  
```
0: Complete tree policy \(\pi_{\theta}\), learning rate \(\alpha>0\), discount factor \(\gamma\).
0: The learned policy \(\pi_{\theta}\).
1: Initialize: Policy parameter \(\theta\).
2:repeat
3: Sample an episode \(\{(s_{1},a_{1},r_{1},s_{2}),\ldots,(s_{n},a_{n},t_{n},r_{n+1})\}\) from historical user data or online data.
4:for\(t=1\) to \(n\)do
5:\(Q^{\pi_{\theta}}(s_{t},s_{t})=\sum_{i=t}^{n}\gamma^{i-t}r_{i}\)
6: Calculate \(\pi_{\theta}(a_{t}|s_{t})\) based on Algorithm 1.
7:\(\theta\rightarrow\theta+\alpha\gamma^{t}Q^{\pi_{\theta}}(s_{t},s_{t})\
abla_{ \theta}\log\pi_{\theta}(a|s)\)
8:endfor
9:until Converge
10: Return:\(\pi_{\theta}\)
```  
**Algorithm 3** Learning the complete tree policy