esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Income Model of Roadside Units
### Roadside RSU Coverage Negative Income
#### 3.2.2 Construction and Maintenance Cost  
The second part of construction and maintenance cost W is divided into two parts: the network cost and roadside RSU cost. Based on assumption (4), the network cost unit price required for the deployment of roadside RSU is known and fixed and will not change with the increase in the deployment length. Based on assumption (5), the roadside RSU are connected to each other by straight lines and located in the road with a linear distribution.  
In the calculation of the construction and maintenance cost \(C_{p}(\alpha)\), the part shown in Figure 4 is a calculation unit so the network cost of the unit can be divided into the following two cases according to the cluster length, as in Equation (24). When \(E(C)<R\), the information transmission between the vehicle clusters shown in Figure 4a cannot increase the communication distance of the roadside RSU and \(C_{p}(\alpha)\) is only related to \(R\). When \(E(C)>R\), the information transmission between the vehicle clusters shown in Figure 4b can increase the communication distance of the roadside RSU; \(C_{p}(\alpha)\) is related to \(E(C)\). In summary, the network cost can be obtained by multiplying the RSU coverage length and the single vehicle cluster length in different intervals by the network cost unit price. The total cost is the sum of the network cost and roadside unit RSU cost, as in Equation (25).  
\[E(C)=\frac{\lim\limits_{n\rightarrow\infty_{i=1}}^{n}\left[p(i) \sum\limits_{j=1}^{i}E(x|x<R_{v})\right]}{\left(e^{-a\lambda R_{v}}\right)^{2} \left(1-e^{-a\lambda R_{v}}\right)}=\frac{1-e^{-a\lambda R_{v}}(a\lambda R_{v}+ 1)}{\alpha\lambda e^{-a\lambda R_{v}}\left(1-e^{-a\lambda R_{v}}\right)} \tag{24}\]  
\[C_{P}(\alpha)=\left\{\begin{array}{l}2 Rh\gamma+h\eta,E(C)<2R\\ E(C)h\gamma+h\eta,E(C)>2R\end{array}\right. \tag{25}\]  
In the equation, \(E(C)\) represents the length of a single vehicle cluster; \(h\) represents the number of computing units; \(\gamma\) represents the unit cost of the network; and \(\eta\) represents the unit cost of the roadside RSU.
## 2 Background
### Summary of the Main Challenges  
As implied in Section 2.2, the GDPR seems to \"be aware\" of the relevant risks stemming from automated decision-making systems and puts (legal) safeguards to alleviate these risks. However, in practice, it is still challenging to implement the data protection principles stemming from the GDPR in such systems; for example, it is not clear how one can ensure and demonstrate that the data protection by design principle is in place in ML systems.  
In relation to the above, it should be pointed out that the advance of ML algorithms--and especially of deep neural networks--has resulted in a new ecosystem consisting of opaque decision systems involving a huge parametric space, thus yielding complex black-box models that are far from transparent [35]. This, in turn, leads to decisions that are not justifiable and do not allow explanations of the logic that the ML algorithm follows to be derived. This is inherently noncompliant with the provisions stemming from the GDPR, which promotes transparency; moreover, even if human intervention is a prerequisite for the final decision (as the GDPR determines), the individuals who make the final decisions need to have explanations with respect to the output of the model [36]. Apart from this, an understanding of a system can allow its amendment. Therefore, due to these issues, the notion of explainable artificial intelligence (XAI) has been introduced [37] in order to allow explainable ML models to be derived while maintaining a high level of output accuracy, as well as to enable individuals to understand the systems (which is prerequisite for a system's trustworthiness). XAI can be seen in terms of its desired goals as a vehicle to develop ML systems that can be considered compliant with respect to the requirement of the fulfillment of human rights. As explicitly stated in [38], _explainability implies that an AI system should provide plain and easy-to-understand information on the factors and decision processes that serve as the basis for its prediction, recommendation, or decision_. However, there is still a series of challenges for XAI that remain insufficiently addressed [35]. Apart from explainability, other similar notions also exist (i.e., explanation, justification, accountability, legitimacy, etc.); these are discussed and analysed in [39].  
The above challenges are further accentuated by the fact that, in practice, there also exist other specific data protection risks mainly related to personal data that are processed in the context of training an ML algorithm through a dataset, for example, how the data minimization principle is fulfilled for the process; which is the minimal amount of possible information that the model needs to be efficiently trained, taking into account that, if this information consists of personal data, appropriate safeguards for protecting these data need to be in place; and whether it would be acceptable in terms of personal data protection to use a training dataset that could allow identification of individuals. Such data protection risks, as well as the relevant mitigation measures, will be discussed next.
## 2 Data and Methods  
### Satellite Data  
We used data from the EUMETSAT MSG satellite. The MSG satellite carries the Spinning Enhanced Visible and Infrared Imager (SEVIRI), a radiometer that measures the Earth every 15 min with a footprint of about 3 km at nadir. MSG is positioned at 0\({}^{\circ}\) longitude over the equator and views KIT's four validation stations at low (25\({}^{\circ}\), Dahra site) to moderate satellite viewing angles (45\({}^{\circ}\), Evora site). LST was estimated in this study from TOA radiances of SEVIRI's 10.8 \(\upmu\)m channel. The standard calibration provided by EUMETSAT is applied in the study to generate TOA radiances and brightness temperatures.  
The LSA SAF team provided MSG 2 TOA 10.8 \(\upmu\)m brightness temperatures, the LSA SAF cloud mask, the LSA SAF surface emissivity and the LSA SAF generalized split-window (GSW) LST retrieval on a 3 \(\times\) 3 pixel window centered on the ground stations for the year 2010. The extracted time series had a temporal resolution of 15 minutes. We collocated satellite data and available KIT _in situ_ measurementsfrom the year 2010 and ran a PMW and an SMW model using the TOA 10.8-\(\upmu\)m brightness temperature together with the LSA SAF surface emissivity. We only considered satellite data that were classified as cloud free in the entire 3 \(\times\) 3 pixel window by the cloud masking. Overall, this analysis included about 60,000 collocated _in situ_ and satellite observations. The _in situ_ data, as well as the different LST models are described in detail in the following sections.
## 3 Algorithm and Platform
### Spark-Based Platform
The training algorithm at \(k\)-th node is described in Algorithm 1. ```
Algorithm Inputs: Training set \(RDD_{traini}\), size of reservoir \(N_{r}\), factors \(c_{1}\), \(c_{2}\), \(c_{3}\), \(r_{1}\), \(r_{2}\), \(r_{3}\), \(\lambda_{n}\) and \(\lambda_{p}\), maximum iterations number \(T\)
```  
**Algorithm Output:** Optimal output weights \(\mathbf{w}^{*}=\left[\mathbf{W}_{i}^{o},\mathbf{W}_{r}^{o}\right]^{T}\)  
```
1: Compute \(\mathbf{H}_{k}\) and \(\mathbf{d}_{k}\) from \(RDD_{traink}\). 2: for \(n\) from 1 to \(T\) do
3: Compute \(f(\mathbf{x}_{i}^{k})\) according to Equation (8). 4: Compute \(\mathbf{P}_{i}\), \(\mathbf{P}_{g}\) and \(\mathbf{P}_{u}\) according to Equations (9)-(11). 5: Update \(\mathbf{x}_{i}\) and \(\mathbf{v}_{i}\) according to Equation (14). 6: Check terminated criterion by Equation (15). 7: if (Equation (15) is satisfied or \(n\) equal to \(T\))
8: then break;
9: else save variables. 10: end if
11: end for
12: return \(\mathbf{P}_{u}\)
```  
**Algorithm 1** Training algorithm for PPSO-based ESN at \(k\)-th node  
In stage 3, we use optimal weight \(\mathbf{P}_{u}\) to calculate the error of the \(RDD_{testk}\), and then save it for future calculation and analysis.
# Harnessing the Cloud: A Novel Approach to Smart Solar Plant Monitoring
## 4 Our Proposed Architecture for Smart Solar Plant
### Cloud-Based Implementation
The AWS IoT can be combined with other services such as event-driven rules, lambda, DynamodB, S3, kinesis analytics, and AWS Cognito to offer a secure, low-latency, and cost-optimized solution, thus helping in fault identification and diagnostics, predictive maintenance, and performance analysis.
## 1 Introduction
Concerning the polymer nanofillers, they improve its mechanical properties, swelling capacity, film-forming capability, rheological properties, and bioadhesion, without losing the inherent processability of the matrix [34; 35]. The use of PEO/clay nanocomposites as drug carriers, prepared via hot melt processes, has been studied previously. [PERSON] et al. used two types of nanoclay, Cloisite(r) 15A and 30B, to adjust acetaminophen's dissolution rate when mixed with PEO. As it was found, the drug recrystallization rate was increased, and the drug releasing rate was decreased with the addition of both types of clay. It was proposed that the drop of the release rate was due to decrease of wettability, while the drug's release mechanism changed from being erosion-dominant to diffusion-dominant [36]. The effect of clay addition on the drug release rate was reported in another study as well, where the ibuprofen release from a PEO matrix was retarded after the inclusion of montmorillonite, although PEO crystallinity was significantly reduced [27]. Appeitant (APR) is an antiemetic drug used for the treatment of chemotherapy-induced nausea and vomiting, as well as postoperative nausea and vomiting [37]. APR is characterized as a hydrophobic compound [38] and is categorized as \"low soluble\" and \"low permeable\" [39]. It is a white crystalline solid powder that is practically insoluble in water, with water solubility of 3-7 ug/mL, pH 2-10 [40]. This low aqueous solubility of APR is the rate-limiting step for APR's poor gastrointestinal absorption. However, it is well known that the preparation of drug solid dispersions in a polymer matrix can drastically enhance the dissolution rate of poorly water-soluble drugs [41; 42; 43; 44]. [PERSON] et al. studied the effect of solid dispersion systems, prepared using hot melt extrusion, in the dissolution profile of appreptant using carrier/platsisizer and additive mixtures such as Hydroxypropyl cellulose (HPC), Vitamin E-TPGS and PEG-4000. As it was found, the solid dispersions prepared by the hot melt extrusion process increased the dissolution rate of poorly soluble appreptant, by reducing the drug's crystallinity to an amorphous phase [45]. The application of the hot melt extrusion process to deliver APR as a hot-melt-extruded drug product with PVP polymers has been previously reported [46]. [PERSON] et al. found that solid dispersions prepared by hot melt extrusion of APR and Soluplus did in fact improve dissolution kinetics and resulted in a significant improvement in the drug release that was attributed to the amorphization of APR as well as the crystal growth inhibition effect of the Soluplus [47]. Similar results were obtained by [PERSON] et al., who reported that the dissolution rate of solid dispersions of APR with Soluplus were significantly increased and nearly five-fold faster than that of pure APR [48]. The aim of this study was to develop APR-loaded PEO/clay carriers with improved drug dissolution rates via melt mixing and investigate the effect of drug and clay loading on the drug release profile.
## 4 Results
### Explainability via SHAP  
We employed SHAP to interpret model behavior globally. Figure 7 displays the SHAP summary plot for the XGBoost model, highlighting the most influential features.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Class** & **Precision** & **Recall** & **F1 Score** & **Support** \\ \hline
0 (Fail) & 0.93 & 0.68 & 0.79 & 60 \\
1 (Pass) & 0.88 & 0.98 & 0.93 & 149 \\ \hline Accuracy & & 0.89 & & \\ Macro Avg & 0.91 & 0.83 & 0.86 & 209 \\ Weighted Avg & 0.90 & 0.89 & 0.89 & 209 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Classification metrics after threshold tuning (threshold = 0.25).  
Figure 6: Precision–Recall Curve for XGBoost Precision. The optimal threshold (0.25) is marked in red.  
Figure 7: SHAP summary plot for XGBoost: G2, G1, and absences dominate feature influence.  
As shown in Figure 7, the XGBoost model identified G2 (second period grade), G1 (first period grade), and absences as the most influential predictors of final student outcomes. Higher values of G2 and G1 are strongly associated with passing, while increased absences shift predictions towards failure. This further confirms the critical role of both prior academic performance and attendance in educational success.  
Figure 8 presents the SHAP summary plot for logistic regression, ensuring that figure citations are in the correct numerical order, as requested by MDPI.  
Interpretation: As shown in Figure 8, the most influential features for the logistic regression model are G2, G1, Fedu, and sex_M. Notably, higher second and first period grades (G2 and G1) strongly push predictions towards \"pass\", while some demographic and behavioral features (such as Fedu, sex_M, and absences) have smaller but still meaningful effects. This indicates that past academic performance is the primary driver for success in the logistic regression model.  
To complement the SHAP summary plots, Figure 9 provides a global feature importance bar chart, which quantifies the mean absolute SHAP values for each feature. The feature failures emerged as the most influential predictor, followed by absences and paid_yes.  
Figure 8: SHAP summary plot for logistic regression.
# Research on Design, Simulation, and Experiment of Separation Mechanism for Micro-Nano Satellites
## 4 Analysis
### Kinematic Analysis
#### Ejecting velocity  
To ensure separation safety, the MF satellite's ejection velocity should be 0.5 m/s-2.0 m/s. Figure 13 illustrates the velocity changes that occur during the satellite's separation process. The satellite's final separation velocity is 1.45 m/s, which meets the design requirements, and it takes approximately 0.51 s for the satellite to completely separate from the compressed state. The separation velocity of the satellite can be adjusted according to the specific requirements of different rockets by adjusting the elastic coefficient of the separation spring.
## 6 Conclusions  
The analyses show that the first two stages, i.e., selection of an appropriate method of TLS point cloud conversion to the raster form and selection of an appropriate algorithm, considerably influence the completeness of the entire process and the accuracy of data orientation.  
In the case of the orientation of scans processed into the raster forms, angles under which analysed surfaces are measured should be considered; this is reflected in the quality and accuracy of finding tie points. If scanner stations are located close to one another, similar deformations in the form of \"distortion\" appear in raster images in the spherical and Mercator projections. When the blob and the corner detection algorithms are used, detection of characteristic points should not highly influence their number and distribution. If scans from highly diversified positions of the scanner stations are oriented, the issue concerning the unequal influence of \"distortion\" in raster images in the spherical and Mercator projection appears. This influences the explicit identification of the same characteristic points and their correct distribution. Therefore, it is recommended to convert the point cloud to the orthoimage form. As a result of the elimination of the discussed deformations, it is possible to achieve high accuracies of data orientation and correct locations of tie points, since detectors have no problems with their explicit identification.  
Before the TLS data orientation process starts, it is recommended to divide point clouds according to the method of conversion (to the raster form in the spherical/Mercator projection or to orthoimages) and the use of point detectors or blob algorithms to detect points.  
The following are available online at [[http://www.mdpi.com/2076-3417/9/3/509/s1](http://www.mdpi.com/2076-3417/9/3/509/s1)]([http://www.mdpi.com/2076-3417/9/3/509/s1](http://www.mdpi.com/2076-3417/9/3/509/s1)), Table S1: Quality assessment on natural control points, Table S2. Quality assessment on natural and check points.  
[PERSON] designed the main idea and manuscript, designed the proposed methods and algorithms, wrote the manuscript and performed experiments, [PERSON] discussed the main idea; co-wrote the Results and Discussion.  
The authors would like to thank the Museum of King Jan III's Palace in Wilanow for cooperation.  
The authors declare no conflict of interest.
Figure A2: Distribution of points used in the TLS data orientation process for the pair of scans for the scanner stations for test sites I: “The Queen’s Bedroom”-the **worst** distribution of the control (blue) and check points (red). Points detected by FAST (**a–c**) and SURF (**d–f**) detectors on raster images: (**a/d**) in the spherical projections, (**b/e**) in the orthoimage and (**c/f**) in the Mercator projection.
# Efficient Data Structures for Range Shortest Unique Substring Queries +
###### Abstract  
Let \(\mathsf{T}[1,n]\) be a string of length \(n\) and \(\mathsf{T}[i,j]\) be the substring of \(\mathsf{T}\) starting at position \(i\) and ending at position \(j\). A substring \(\mathsf{T}[i,j]\) of \(\mathsf{T}\) is a repeat if it occurs more than once in \(\mathsf{T}\); otherwise, it is a unique substring of \(\mathsf{T}\). Repeats and unique substrings are of great interest in computational biology and information retrieval. Given string \(\mathsf{T}\) as input, the Shortest Unique Substring problem is to find a shortest substring of \(\mathsf{T}\) that does not occur elsewhere in \(\mathsf{T}\). In this paper, we introduce the range variant of this problem, which we call the Range Shortest Unique Substring problem. The task is to construct a data structure over \(\mathsf{T}\) answering the following type of online queries efficiently. Given a range \([\alpha,\beta]\), return a shortest substring \(\mathsf{T}[i,j]\) of \(\mathsf{T}\) with exactly one occurrence in \([\alpha,\beta]\). We present an \(\mathcal{O}(n\log n)\)-word data structure with \(\mathcal{O}(\log_{w}n)\) query time, where \(w=\Omega(\log n)\) is the word size. Our construction is based on a non-trivial reduction allowing for us to apply a recently introduced optimal geometric data structure [[PERSON] et al., ICALP 2018]. Additionally, we present an \(\mathcal{O}(n)\)-word data structure with \(\mathcal{O}(\sqrt{n}\log^{\epsilon}n)\) query time, where \(\epsilon>0\) is an arbitrarily small constant. The latter data structure relies heavily on another geometric data structure [[PERSON] and [PERSON], SWAT 2012].  
shortest unique substring; suffix tree; heavy-light decomposition; range queries; geometric data structures +
Footnote †: journal: arXiv:1908.03901
# A Robust Controller for Upper Limb Rehabilitation Exoskeleton
## 3 Controller for Trajectory Tracking
### PID Type Control with Trajectory Tracking  
To achieve the desired control response, only active joints are considered, and the following PID type controller is proposed.  
\[\tau_{i}=J_{e}\bigg{[}\hat{q}_{id}-k_{d}(\hat{q}_{i}-\hat{q}_{id})-k_{p}(q_{i} -q_{id})-k_{i}\int{(q_{i}-q_{id})dt}\bigg{]} \tag{1}\]  
where \(J_{e}\) represents the equivalent moment of inertia of the link where the torque is applied with the other joined links in which it performs the movement. The desired trajectory is denoted by \(q_{i},q_{i}\) and \(\tilde{q}_{id}\). The proportional, integral and derivative gains of the controller are \(k_{p},k_{i}\) and \(k_{d}\), respectively. By assigning poles, the controller's gains were determined, which remained as:  
\[k_{p}=212,k_{i}=800,k_{d}=12.\]
# Resilience in the Internet of Medical Things: A Review and Case Study
## 3 Background
### Machine Learning  
IoMT networks carry confidential information about patients' identities and their chronic diseases. These sensitive data must be secured from different types of cyber-attacks. Machine learning works well to handle security and privacy issues in IoMT networks. ML technologies, such as support vector machine (SVM), decision tree, Naive Bayes, etc., are used for anomaly detection and provide the information about the presence of intruders in the IoMT networks [22]. It also detects malicious activity (active or passive attacks) carried out by the intruder [23]. There are a number of cyber-attacks that can be handled by using different types of ML algorithms with promising results [24]. The most common cyber-attack categories are denial-of-services (DoS), zero-day attacks, and man-in-middle attacks [25]. Nodes or sections of a network that are compromised from these attacks cannot be trusted to transfer sensitive data. ML predicts this information of failed or malicious nodes that are affected by the attacks and informs the administrator so that an alternate route can be determined for continuing the secure transmission of data [26].
## 2 Related Work
The authors of [25] presented the structure of an iterative turbo equalizer incorporating a channel assessment for high-frequency (HF) channels and multistate modulations making use of the RLS algorithm guided by the LLRs coming out of the decoder and training sequences. A new method of turbo equalization was proposed in [26]. The receiver level performs processing using the LMMSE detector and the LS estimator. The recommended approach manages as a priori information the LLRs produced by the LDPC decoder to accomplish equalization and channel estimation. In [27], the authors proposed a method for estimating the communication channel about forward error correction codes. Their technique is based on the Wiener equalizer and utilizes information produced by the decoder to raise the estimation performance. The authors used in [28] a nonlinear equalizer and a forward error correction (FEC) decoder. The decoder gained from the improved channel estimation involving more accurate log-likelihood ratios (LLRs). To minimize the interferences, the author chose an iterative approach to manage the feedback information from the decoder. An extended study related to the concept of faster-than-Nyquist (FTN) transmission with maximum a posteriori (MAP) equalization and LDPC or Bose, Chaudhuri, and Hocquenghem (BCH) decoding was proposed in [29]. A considerable reduction in channel interferences and precise information recovery was achieved in [30] by transferring information among the detector and the LDPC decoder.