esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting Academic Performance Using an Efficient Model Based on Fusion of Classifiers
## 4 Experiments and Evaluation
### Experiments with Base Classifiers and Ensemble Base Classifiers  
The three base classifiers including MLP, J48, and PART were applied after the data preprocessing stage. The evaluation results showed that among these three base classifiers, MLP outperformed the other classifiers, achieving greater accuracy (i.e., 88.52) as shown in Figure 2. The figure has two parts; in the first part, the bar chart presents the performance of classifiers in terms of accuracy, precision, recall, and F-score. The second part presents the performance of classifiers in tabular form through the same measures. The MLP classifier also performed better in terms of other measures such as precision, recall, and F-score. The time complexity of the MLP classifier is O(emnk), which is a composition of interconnected neurons, whereas J48 and PART have a time complexity of O(mn2), which works on the If and Then rule until the predicted class has not been obtained.  
Furthermore, three different ensemble classifiers including bagging, multiboost, and voting were built. Among these three ensemble classifiers, multiboost outperformed the other classifiers, achieving higher accuracy (i.e., 95.7) as shown in Figure 3. The figure comprises two parts; in the first part, the bar chart shows the performance of classifiers in terms of accuracy, precision, recall, and F-score. The second part indicates the performance of classifiers in tabular form through the same measures. The classifier also performed better in terms of other measures such as precision, recall, and F-score. The time complexity of bagging is (O(klogn), where k is the number of bags.
## 7 Challenges and Limitations
### Environmental Variability  
Studies by [PERSON] et al. [24] and [PERSON] et al. [36] pointed out challenges related to handling varying lighting conditions and image quality in real-world settings, which can hinder model performance and adoption.
# Oral Health and Cognitive Decline: A Systematic Review of the Periodontitis-Alzheimer's Connection
## 1 Introduction
### Environmental Factors  
Mounting evidence underscores a significant mechanistic link between PD and AD, mediated through systemic inflammation, microbial dysbiosis, and direct pathogen invasion. Chronic PD elevates pro-inflammatory cytokines (IL-1\(\upbeta\), IL-6, TNF-\(\upalpha\)), which traverse the blood-brain barrier, activate microglia, and drive neuroinflammation--a hallmark of AD pathology. Critically, periodontal pathogens like _Porphyomonas gingivalis_ invade the brain, where their virulence factors (gingipains) promote A\(\upbeta\) aggregation, tau hyperphosphorylation, and neuronal damage. This is corroborated by biomarkers: elevated plasma p-Tau and A\(\upbeta\)1-40 levels, distinct oral microbiome shifts (increased Firmicutes, decreased Bacteroides), and salivary metabolites like galactinol all correlate with cognitive decline. Clinically, periodontal treatment reduces systemic inflammation, stabilizes cognition, and mitigates brain atrophy, underscoring PD as a modifiable AD risk factor. Severe PD withtooth loss increases AD risk by 6-16%, amplified by pro-inflammatory diets and vitamin D deficiency. Beyond these pathways, environmental factors in the oral milieu during PD accelerate protein misfolding and fibrillogenesis, which are pivotal in AD pathogenesis. Saliva in PD patients exhibits elevated concentrations of divalent cations (Ca\({}^{2+}\), Zn\({}^{2+}\), Fe\({}^{2+}\)). These ions neutralize negative charges on A\(\beta\) peptides, reducing electrostatic repulsion and promoting hydrophobic collapse into neurotoxic fibrils. Concurrently, increased ionic strength in PD saliva--driven by inflammatory exudate and microbial byproducts--compresses the electric double layer around A\(\beta\), further accelerating aggregation. This creates a permissive environment for A\(\beta\) fibrillization even before systemic dissemination, potentially initiating or exacerbating neurodegeneration [203; 204].
## 2 Materials and Methods
### Feature Extraction  
We applied the proposed pipeline on the 82 patients provided by the Department of Diagnostic and Preventive Medicine of the IRCCS Policlinic Sant'Orsola-Malpighi of Bologna. The radiomic features extraction was performed on the identified GGO areas. The extracted features include morphological and texture-based scores:  
* Texture;
* Gray Level Distribution;
* GGO Shape;
* Bilaterality (distribution of GGO between left and right lung);
* Peripherality;
* GGO volume  
For the scores classification, we included the patient's age as informative features.  
We measured the texture properties by computing the Haralick features (Energy, Inertia, Entropy, Inverse Difference Moment, Cluster Shade and Cluster Prominence) from the Gray Level Co-occurrence Matrix (GLCM), computed on the whole identified area [63]. For each identified GGO area, we computed its elongation and roundness [64], obtaining the corresponding distribution for each patient. We computed the distribution of the distances between the lesion and lung centroids, normalized to the semiaxis of the equivalent ellipsoid as a measure of the GGO peripherality. Each distribution was characterized by the minimum, maximum, median, interquartile range (25-75), skewness,and kurtosis. We estimated the bilaterality distribution using the Matthews coefficient (MCC) defined as follows:  
\[MCC=\frac{LGV\cdot LLV-RGV\cdot RLV}{(LGV+RGV)(RLV+LGV)(LLV+RGV)(LLV+RLV)}\]  
being \(RGV\) = Volume of GGO in the right lung, \(LGV\) = Volume of GGO in the left lung, \(LLV\) = Left Lung Volume, and \(RGV\) = Right Lung Volume. The volume of GGO was normalized according to the total lung volume to overcome possible issues related to anatomical differences between patients.
## 5 Conclusions  
This paper presents a microwave/optical synergistic method to estimate the soil moisture in a grassland area based on the IEM and modified WCM. To estimate the soil moisture using the IEM in a vegetated area, the vegetation contribution to the signal is removed with the use of the modified WCM. Then, the soil moisture over an alpine prairie was estimated from Radarsat-2 data.  
Most of the previous studies on backscattering modeling of natural vegetation and agricultural crops assume that the vegetation is unified [44]. Therefore, it was difficult to use those models on a wide area where the vegetation ranged from fully to relatively sparse cover. In this method, the advantages of optical and microwave remote sensing methods were synthesized; the latter was used as the main method and the former as a supplementary method. The parameters retrieved by the optical model were the inputs to the microwave remote sensing model. The method uses vegetation coverage derived from optical data for calculating the contributions of the fractions of vegetation and bare soil at the pixel level to better represent the difference in the scattering mechanisms between vegetation and bare soil. The results show that the proposed approach can successfully predict backscattering from the alpine prairie. Furthermore, the capability of estimating soil moisture with this method is proven by quantitative comparisons between the measured and predicted values in the study area. These results demonstrate that the proposed method has a large potential for soil moisture retrieval of soils under vegetation because it can accurately estimate the soil moisture in areas that are either fully or sparsely covered by vegetation. Finally, the errors associated with the soil moisture estimation using this method were analyzed. It was found that the data preprocessing, model simulation and inversion could introduce errors into the soil moisture retrieval.  
The estimated soil moisture information can be used to monitor the drought conditions of the prairie, which can guide the management of the grassland. In the near future, this model will be used to monitor the annual soil moisture changes in Ruoergai Prairie to study the grassland degradation and land desertification.
## 4 Cellular Component of the CRC microenvironment
### Tumor-infiltrating Lymphocytes (TILs)
The group of TFH lymphocytes, in the case of non-lymphocytic tumors, supports the immune response and enhances the effectiveness of therapy. TFH cells participate in the maturation and activation of B lymphocytes through the chemokine CXCL-13 and can support the infiltration of both normal and malignant B lymphocytes. However, the activation of negative B lymphocytes by TFH is mainly associated with lymphomas. In the case of solid tumors such as CRC, TFH supports the development of anti-tumor responses. These functions are mainly explained by the production of IL-21, which activates B lymphocytes [184; 185]. Regulatory T cells (Treg) are a subpopulation of CD4+ lymphocytes. They are responsible for regulating the immune system and contribute to silencing inflammation, suppressing immune responses, and impeding immune surveillance, thereby promoting tumor progression. In many types of tumors, high Treg infiltration has been characterized and shown to be driven by the presence of chemokines such as CCL-17, CCL-22, CCL-1, CCL-28, CCL-9, CCL-10, and CCL-11, which are associated with poor patient prognosis. Therefore, treatment strategies targeting Treg and their products hold promise for immunotherapy [177, 186, 187, 188, 189, 190]. B cells can exhibit dual functions in the tumor microenvironment. As antigen-presenting cells, cytokine producers, or participants in direct killing, they may demonstrate anti-tumor activity. On the other hand, regulatory B cells (Bregs), a subpopulation acting immunosuppressively through the secretion of cytokines (IL-10, TGF-\(\beta\), and IL-35), regulation of anti-tumor functions of immune cells, and direct interaction with tumor cells, can exert pro-tumor effects [191, 192, 193]. Some results indicate that the infiltration of CD20+ B cells correlated with an improvement in patients' conditions and additionally activated the anti-tumor activity of T lymphocytes [194, 195, 194]. However, this largely depends on the stage of cancer. While initially the action of B cells was considered to have anti-tumor effects, in advanced stages, infiltrations of unfavorable Bregs were also observed [196]. Immunotherapy associated with T lymphocytes in CRC is primarily based on supporting the action of cytotoxic lymphocytes. It involves manipulating immune checkpoints, such as PD-1/PD-L1, CTLA-4/B7, and MHC I/TCR (major histocompatibility complex I/T-cell receptor), using specific antibodies that block either negative stimulatory receptors on T cells or their ligands on the surface of tumor cells. This approach prevents the exhaustion of cytotoxic cells and inhibits the apoptosis induced by cancer cells [170]. In CRC immunotherapy, the blockade of PD-1, PD-L1, and CTLA-4 has been found to be effective [197]. As reported by the FDA (Food and Drug Administration), it is possible to use immune checkpoint inhibitors, e.g., nivolumab (targeting PD-1) and ipilimumab (targeting CTLA-4) in CRC treatment [198, 199, 200].
# Analysis of MTF in TDI-CCD Subpixel Dynamic Super-Resolution Imaging by Beam Splitter
## Appendix A Derivation Details of Function (2)  
Wigner-Seitz (WS) cell as the following:  
\[w(x)=\left\{\begin{array}{ll}\frac{1}{p},&\mbox{for $x$ inside the WS cell}\\ 0,&\mbox{for $x$ outside the WS cell}\end{array}\right. \tag{1}\]  
According to Equation (1), the Fourier transformation of \(w(x)\) is:  
\[W_{2}(f)=\int_{-\frac{p-a}{2}}^{\frac{2}{2}}\frac{1}{p}\exp(i2\pi fx)dx+\int_{ -\frac{a}{2}}^{\frac{p-a}{2}}\frac{1}{p}\exp(i2\pi fx)dx \tag{2}\]  
The real part of \(W_{2}(f)\) is:  
\[\mbox{Re}(W_{2}(f))=\frac{p-a}{p}\sin c[\pi f(p-a)]+\frac{a}{p}\sin c(\pi fa) \tag{3}\]  
In order to preserve the convenience of the transfer function analysis method, [PERSON] [28] and [PERSON] [29] deduced the pixel function independently. This is determined by the process of discrete sampling in the CCD array through different methods, with the resultant pixel function being:  
\[s(x)=\frac{1}{p}rect(\frac{x}{p})=\left\{\begin{array}{ll}\frac{1}{p}&|x|< \frac{p}{2}\\ 0&elsewhere\end{array}\right. \tag{4}\]  
The Fourier transform of \(s(x)\) is:  
\[S(f)=\sin c(\pi fp) \tag{5}\]  
From Equations (3) and (5), the average modulation transfer function (MTF) of the sampling results from two linear CCD is:  
\[\begin{array}{ll}MTF_{2}&=F\{s(x)*w_{2}(x)\}\\ &=S(f)\cdot\mbox{Re}(W_{2}(f))\\ &=\sin c(\pi fp)\{\frac{p-a}{p}\sin c[\pi f(p-a)]+\frac{a}{p}\sin c(\pi fa)\} \end{array} \tag{6}\]  
In Equation (6), \(*\) represents the revolution; \(*\) represents the product; \(F\{\}\) represents the Fourier transformation and \(\sin c(x)=\frac{\sin(\pi x)}{\pi x}\).
# An Improved Ciphertext-Policy Attribute-Based Encryption Scheme in Power Cloud Access Control
## 4 Our Proposed PCAC Scheme
### Detail Procedures in PCAC  
#### 4.2.1 Init Phase  
**Init**: The initialization phase of the system is executed by PC, where the public key PK and the master secret key MSK are obtained through the system parameter \(k^{\prime}\). Firstly, the PC determines a large prime \(p\) by the system parameter \(k^{\prime}\). Then, it chooses two linear groups \(G_{0}\) and \(G_{T}\), with prime order \(p\) and \(g\) as the generator of the linear group \(G_{0}\). Then, a pairing is chosen, \(\ell:G_{0}\times G_{0}\to G_{T}\), and a collection of attributes is defined, \(S~{}=~{}\{s_{1},s_{2},\cdots,s_{m}\}\in Z_{p}\), where all attributes existing in the system are contained by \(S\). A unique public key is chosen \(\{Sn_{1},Sn_{2},\cdots,Sn_{n}\}\in G_{0}\) for each KS, which is used in secret-sharing. Finally, the PC chooses \(\alpha,~{}\beta\in Z_{p}\), randomly, to generate the PK and MSK. PK is:  
\[\textbf{PK}~{}=~{}\left\{G_{T},G_{0},g,g^{\beta},e(g,g)^{\alpha},S,Sn_{1},Sn_{2 },\cdots,Sn_{n}\right\}\]  
And MSK is:  
\[\textbf{MSK}~{}=~{}\{\alpha,\beta\}\]  
All agencies in the system can get the PK, but the MSK needs to be kept confidential to the other agencies in the system.  
In the initialization phase, in order to distinguish between the different DSs, the PC needs to send a pair of public key and private key \((Pk_{DS},~{}Sk_{DS})\) to each DS. PC randomly chooses \(DS_{id}\in Z_{p}\) as the identification of DS, and \(Pk_{DS}~{}=~{}g^{DS_{id}}\), \(Sk~{}=~{}DS_{id}\). All agencies in the PCAC model can achieve the DS's public key, and DS's private key is only owned by the DS itself, which is used to verify the DS's identity. Like DS, for each U, the PC generates a pair of public key and private key \((Pk_{U},~{}Sk_{U})\), and \(Pk_{U}=g^{U_{id}},Sk=U_{id}\), where \(U_{id}\in Z_{p}\) is randomly chosen. In general, during initialization phase, all DSs and Us can obtain a pair of public key and private key \((Pk,~{}Sk)\) from the PC, and the proof of identification of every DS and U is \(Pro~{}=~{}g^{id}\).
# High-Performance Speed Control of PMSM Using Fuzzy Sliding Mode with Load Torque Observer
## 3 Controller Design  
The system adopts a double closed-loop structure; the outer loop is sliding mode control; the inner loop is feedback decoupling PID control. The control block diagram of the system is shown in Figure 1. This double-loop structure itself can be considered a form of symmetry in control design. The inner loop focuses on the current control, while the outer loop manages the speed control, and they work in coordination with each other to achieve the desired goals.
## 3 Mission Analysis
### Mission Scenarios  
The diagram overview of the five modes was selected as the operation modes for these proposed missions of nanosatellites and is presented in Figure 10.  
**Initialization mode:** once the nanosatellite is ejected from the deployer, the status of the nanosatellite is checked in the early stage of nanosatellite operation; the antenna is deployed; the communication and power systems are initialized.  
**Common mode:** The nanosatellite orbits with no mission or communication. Therefore, in this mode, only the OBC and EPS are activated.  
**Safe mode:** once the power supply is insufficient, the nanosatellite is operated with minimum functions and minimum power, where only the EPS is activated.  
**Mission mode:** The nanosatellite performs its missions, such as receiving data of the specified areas using a mounted payload based on AIS. In this mode, the nanosatellite  
Figure 10: Proposed missions’ modes for the nanosatellite.  
Figure 9: Performance results of each constellation for the forest in the Chinese mainland.  
accomplishes its missions by exploiting its payload with the full activation of OBC and the function of ADCS.  
**Communication mode:** The data obtained from the mission are communicated to the ground station. Telemetries data are also sent to the ground station and remote commands are received from the ground station.
## 4 Hyperconverged TPM
### Anchoring of the Persistent State
#### 4.1.3 Management System  
In order for the management VMs to make appropriate decisions regarding where to perform remote anchoring, they must share the same state. This state includes information about all the nodes equipped with TPM chips, along with the metrics that enable each m-VM to compute the anchoring cost.  
Possible metrics include the number of m-vTPMs anchored by the m-VM, the transmission time of the anchoring data, the resource utilization rate, the nature of the services hosted on the users' VMs, and the criticality of these services in terms of security, among other factors.  
To enable state sharing among virtual machines (VMs), we consider the adoption of a consensus algorithm. Consensus algorithms are fundamental mechanisms in distributed systems, ensuring that multiple nodes reach agreement on a given value or state, even in the presence of failures or communication delays [55]. In the analyzed context, one or more VMs that are part of the management system propose a state value to be accepted. Then, the VMs exchange messages to verify and validate the proposal. Finally, after gathering sufficient information, the VMs converge on a single value, which is subsequently recorded as definitive.  
The state of an m-VM is modified when, for example, it receives the anchoring of an additional m-vTPM. When the leader receives an indication of a state change, it reports this update to the other m-VMs, ensuring that all the VMs maintain the same set of information.  
When a new node is added, its m-VM must also communicate with the leader to indicate whether it has a TPM and to receive the leader's updated state. All the communication between m-VMs is carried out via the management API. This API receives requests, authenticates them, and validates the received state information. For this purpose, we use mTLS (Mutual Transport Layer Security), a security protocol that ensures mutual authentication between two parties in a communication [56].  
Figure 8: Anchoring VTPM hosted on a machine without TTM.  
Each m-VM makes its own decision regarding where to anchor its m-vTPM. To do so, based on the metrics available in its state, the m-VM calculates the anchoring cost. This cost is computed as the sum of the normalized values for each considered metric. The TPM that results in the lowest cost is selected for anchoring. If multiple TPMs yield the same cost, the decision is made randomly among the candidates with equivalent costs.
## 2 Description of the System
### Working Media
#### 2.2.1 Liquid Salt  
Liquid salt is the name given to salt compounds that are used above their melting point. The most widely used liquid salts for thermal storage in the industry are the nitrate salts Hitec and Solar Salt. Hitec is a ternary liquid salt (53% KNO\({}_{3}\), 40% NaNO\({}_{2}\), 7% NaNO\({}_{3}\)) with a melting point of approx. 142 \({}^{\circ}\)C [18]. It can be operated up to 535 \({}^{\circ}\)C. At temperatures above 535 \({}^{\circ}\)C, it decomposes and becomes unusable for further use. Solar Salt is a binary nitrate salt (60% NaNO\({}_{3}\), 40% KNO\({}_{3}\)). It can be operated from approx. 220 to 585 \({}^{\circ}\)C.  
Due to the large temperature range in which liquid salts are used, they are particularly suitable for storing thermal energy. Compared with thermal oil, they are also cheaper, more readily available, and less harmful to the environment, Moreover, although thermal oil and molten salt have almost the same specific heat capacity, the density of molten salt is about twice as high. Therefore, with the same storage capacity, thermal oil storage tanks accordingly have a volume that is twice as large. The main difficulty with using molten salt is the risk of solidification. If the temperature falls below the melting point, the salt solidifies and the volume contracts, causing irreversible destruction of the system [19].  
It must therefore be ensured at all times that the salt is constantly kept above its melting point at all points in the system.
# Distance Assessment by Object Detection--For Visually Impaired Assistive Mechatronic System
## 5 Discussion
### RPLIDAR and HQ Camera Subsystem  
The analysis of the datasets obtained experimentally in the real environment and centralized in Table 2 shows that, starting with a distance of 1.00 m, the 6 mm fisheye lens no longer recognizes the test object, and at a distance of 1.50 m, only the 16 mm telephoto lens correctly recognizes the test object based on images stored in GoogLeNet. At the same time, we noticed that at a distance of 0.50 m, the experimental results in the real environment with both types of objectives are very good, with the identification and recognition times being less than one second.  
The 16 mm telephoto lens is preferred for 0.50 m distances due to the accuracy of the recognized classes, to the detriment of the 6 mm lens, regardless of the database used. The lowest average identification and recognition time is recorded with the 16 mm telephoto lens and the GoogLeNet database, but the accuracy of the test object classes is not very good.  
The error of measuring the distance at which the test object was fixed is calculated as an absolute value (the ratio of the difference between the two values indicated by the laser scanner and the distance measured manually) and is relatively small, between 1.00% and 4.20%. This is due to the slight, involuntary change in the position of the mobile mechatronic system, the porosity of the object surface, and the vibrations induced by the rotational speed of the laser head of the RPLIDAR sensor.  
We also noticed large errors, over 3.40%, in cases where the test object was not recognized.  
The two methods studied for distance assessment through object detection and recognition, as well as their corresponding experiments performed in a real environment, generated two sets of useful data (see Tables 1 and 2) for the design and prototype of an assistive mechatronic system.