esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simplified Analysis Model for the Proposed Steel Modular Connection  
### Modular Beam-Column Connection Modeling  
As discussed in the introduction, a reasonable finite element model of the blind-bolted beam-column connection illustrated in Figure 2b is required for the design and analysis of the proposed modular structural system. Since frame elements are generally used in the finite element analysis of building structures, it is convenient to model the blind-bolted connection using only the frame elements, instead of other types of two- or three-dimensional finite elements, for simplicity of analysis. Based on this idea, we propose a simple, but effective connection model where the joints between the beam and column members are connected with each other by frame elements and their stiffness is determined such that it can represent the actual stiffness of the connection.  
Figure 17a,b show two possible connection models satisfying the aforementioned conditions, which are the partial hinge and rotational spring connection models, respectively. In both cases, the four frame elements connecting the beam-column joints have very high stiffness. In the case of the partial hinge model, the stiffness of the blind-bolted connection is represented by four internal hinges, and thus this model does not require any additional material parameters. Although this model is simple, it is questionable if its stiffness can accurately represent the actual stiffness of the connection. Furthermore, it may result in an asymmetric distribution of internal forces and moments even if both the geometry of the structure and boundary conditions are symmetric. In contrast, the stiffness of the rotational spring connection is represented by the use of eight identical rotational springs inserted into the connecting frames, as shown in Figure 17b, and their stiffness can be determined from the experimental results.  
Since all of the member geometry, force and support conditions are symmetric with respect to the center line of the double main columns in both of the specimens, the distribution of internal forces also must be symmetric. Figure 18 shows the linear elastic internal bending moment distributions of the structural analysis model for IC_OB_F specimen utilizing the two types of the connection model when the lateral load (\(P\)) of 20 kN is applied. The results in the figure indicate that the moment distribution of the analysis model with the rotational spring connection is symmetric with respect to the double main column centerline, while the moment distribution of the model with the partial hinge connection  
Figure 17: Modular beam-column connection modeling.  
is not. A similar result can be found from Figure 19, which shows the moment distribution of IC_OB_B specimen. Considering this result, the rotational hinge model is adopted to evaluate the structural performance of the proposed modular system in this study.
## 2 Materials and Methods  
### Review of Previous Works  
The domain of semantic modeling has been fundamental in the representation and understanding of knowledge for decades. Techniques such as the Resource Description Framework (RDF) and Ontological Web Language (OWL) have provided standardized means of representing information on the web [11]. RDF is a standard that describes relationships between entities in triplets, facilitating data representation in the Semantic Web. On the other hand, OWL defines ontologies, providing greater expressiveness and allowing concepts, properties, and their relationships to be defined in a specific domain [12].  
With the emergence of deep learning techniques in natural language processing (NLP), we have witnessed significant advances in various tasks [13]. Models based on recurrent neural networks (RNN) initially showed an impressive ability to handle sequences, with Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) being notable variants that solve problems inherent to traditional RNNs, such as forgetting in the long term [14]. However, introducing Transformers [4] and models such as Bidirectional Encoder Representations from Transformers (BERT) revolutionized the field, setting new performance standards across numerous NLP tasks. Despite their effectiveness, these models, in their original form, focus on syntactic and contextual patterns, often neglecting deep semantic nuances [15].  
The attention mechanism, first introduced in the context of neural networks for sequence-to-sequence tasks [16], has proven to be a critical component in models such as Transformers. Although this mechanism has allowed models to weigh different parts of an input according to their relevance, its application in the semantic field has been limited. However, there have been efforts to integrate semantic information into these mechanisms. For example, recent work has experimented with incorporating ontologies and knowledge bases into attention models to improve their understandability [17].  
Combining the symbolic power of ontologies with the contextual modeling capabilities of deep learning offers a promising path forward. Although there has been progress in this direction, there is still ample room to explore and optimize this symbiosis. Despite the advances made in NLP thanks to deep learning, effective integration of semantics remains an ongoing challenge. Although models like BERT accurately capture linguistic contexts and patterns, proper semantic understanding beyond the immediate context still eludes these systems [18]. Our proposal seeks to fill this gap, combining the rich semantic structure of ontologies with the power of deep learning, creating a model that recognizes linguistic patterns and understands and represents deep meanings. In a world where precision and understanding are essential, our proposal is relevant and necessary to take NLP to the next level [19].
###### Abstract  
The aim of the research was to identify the effect of 4 vs.4 small-sided games (SSGs) with goalkeepers (4 vs.4+GK), applied twice a week, for 8 weeks, on U18 football players' physical ability, compared to that of the athletes who performed specific aerobic endurance training. The research included 40 football players U18, divided into two groups: 20 in the experiment group (EG) aged 17.49 \(\pm\) 0.61 years and 20 in the control group (CG) aged 17.66 \(\pm\) 0.54 years. From the initial test (IT) to final tests (FT) performed on the parameters, maximum oxygen consumption (VO\({}_{\rm 2 max}\)) and 10 m and 20 m sprint and agility with and without a ball showed a significant increase for both groups. The first and last SSG training was monitored, using total distance (TD), high-intensity distance (HSR--high-speed running) and maximum intensity distance (VHSR--very-high-speed running). Comparative analyses of EG and CG parameters at the FT shows that the differences between the means of two groups are statistically significant in favor of the experimental group in terms of all parameters, except 10 m sprint (\(p\) < 0.05). Referring to SSGs, the experimental group had a 7.78% increase rate in TD (\(p\) < 0.0005), a 30.90% (\(p\) < 0.0005) increase rate with HSR and no significant differences (\(p\) > 0.05) with VHSR. The experimental training program of 4 vs.4+GK SSGs, applied to U18 football players, produced significant progress in the physical, and functional parameters.  
small-sided games; VO\({}_{\rm 2 max}\); agility; speed; physical fitness; skills; U18 football player; training program; 4 vs.4+GK
## 2 The Model for GNSS/INS/LEO/5G Integrated Navigation
### The Structure of Federated Filtering Algorithm
#### 2.1.2 GNSS/INS Sub-Filter
\right)_{x=x_{k}^{-}} \tag{14}\]  
The Kalman filtering algorithm is Formulas (15)-(19):  
\[\hat{x}_{k}^{-}=\Phi_{k-1}\hat{x}_{k-1}^{+} \tag{15}\]  
\[P_{k}^{-}=\Phi_{k-1}\Big{(}P_{k-1}^{+}+\tfrac{1}{2}Q_{k-1}^{\prime}\Big{)}\Phi _{k-1}^{T}+\tfrac{1}{2}Q_{k-1}^{\prime} \tag{16}\]  
\[K_{k}=P_{k}^{-}H_{k}^{T}\big{(}H_{k}P_{k}^{-}H_{k}^{T}+R_{k}\big{)}^{-1} \tag{17}\]  
\[\hat{x}_{k}^{+}=\hat{x}_{k}^{-}+K_{k}\big{(}z_{k}-H_{k}\hat{x}_{k}^{-}\big{)}= \hat{x}_{k}^{-}+K_{k}\hat{x}_{k}^{-} \tag{18}\]  
\[P_{k}^{+}=(I-K_{k}H_{k})P_{k}^{-} \tag{19}\]  
where \(\hat{x}_{k-1}^{+}\) is system state vector in the moment of \(k-1\). \(\Phi_{k-1}\) is the system transfer matrix in the moment of \(k-1\). \(P_{k-1}^{+}\) is the covariance matrix in the moment of \(k-1\). \(Q_{k-1}^{\prime}\) is system noise matrix. \(R_{k}\) measurement noise matrix. \(K_{k}\) is the Gain matrix. \(I\) is the unit matrix.
# A Blockchain Self-Sovereign Identity for Open Banking Secured by the Customer's Banking Cards
## 7 Discussion and Evaluation
### SSBI Evaluation  
In this section, we evaluate SSBI according to the stated design objectives, and in terms of the fulfillment of the requirements and features of the framework.  
Prevent financial data leakage in open banking: SSBI helps to prevent identity and financial data leakage during open banking operations. Third-party agencies can promote financial products and perform KYC services on behalf of banks via the trusted SSI platform where customers and agencies can verify each other. By providing trust in DIDs and VCs, this eliminates fraudulent calls and phishing.  
Trusted identity issuer: The proposed solution capitalizes on having banks as a trusted party. SSBI considers the active bank account customer, where the KYC process is performed in advance with a unique DID.  
High level of assurance: SSBI combines Veramo key management with a tamper-resistant secret key storage, which is a payment card, to prevent unauthorized access to an identity.  
Digital identity verification: The approach relies on Veramo SSI APIs to ensure identity proof, VC validity, and revocation status through the access to Ethereum Blockchain.  
Strong authentication and secure key handling: Multifactor authentication is enabled through the usage of a banking card for sensitive key operations and transaction signing. Moreover, banking cards use PIN verification and have recently begun to use embedded biometric fingerprint authentication.  
Identity ownership and SSI: The user is able to control several identity proofs and related attributes, request and store the VC and proofs from the identity issuer (banks in our case), and control the sharing of the VC or selective attributes with other parties.  
Ability to revoke the proofs: The identity owner is able to revoke the proofs and VC at any time, by calling the corresponding Veramo API to revoke the Ethereum smart contract.  
Seamless user experience: SSBI combines the trusted issued banking cards, which are used in day-to-day payments and money withdrawals, for identity cryptographic operations. It is more convenient for the customer to use modern form factors such as payment stickers and miniTags. The mobile app communicates through NFC to simplify the process.  
Key recovery: Identity and private key recovery are achieved by combining two key parts and performing the generation action, where the key part is saved by the issuing bank while the passphrase is memorized by the user.
## 5 Conclusions  
This paper proposes a novel approach for the diagnosis of nutrition-related geriatric syndromes: malnutrition, oropharyngeal dysphagia, dehydration, and eating disorders in dementia. This approach is based on the original XAI-based health risks assessment model, which was implemented in the CDSS prototype tested under real conditions at the geriatric department of Lithuanian University of Health Sciences Kaunas Hospital. The diagnoses provided by the CDSS were compared with the diagnoses of resident physicians and expert physicians. The results of this study, involving 83 patients, show that the proposed CDSS can efficiently diagnose nutrition-related geriatric syndromes with high accuracy: 87.95% for malnutrition, 87.95% for oropharyngeal dysphagia, 90.36% for eating disorders in dementia, and 86.75% for dehydration. This confirms that the proposed XAI-based CDSS is an effective tool, allowing even less experienced medical personnel to make more accurate decisions faster and easier when the decision should be made using incomplete, uncertain, and subjective data. The experimental evaluation of the proposed CDSS proves that in some cases, the system makes even a more accurate decision than junior and senior resident geriatricians. Therefore, the CDSS could serve as a screening tool in clinical family medicine practice. If the risk of the syndrome is identified, the patient would be referred for an outpatient geriatric consultation or an inpatient geriatric examination (e.g., a day hospital).  
Conceptualization, V.P., R.J., and G.D.; data curation, G.D., Z.M., J.D., and V.K.; formal analysis, V.P., R.J., and G.D.; funding acquisition, A.L. and V.L.; investigation, G.D., J.K., and R.B.-P.; methodology, V.P., R.J., G.D., and J.K.; project administration, A.L. and V.L.; resources, V.L., Z.M., and V.K.; software, Z.M., J.D., and V.K.; supervision, A.L. and V.L.; validation, G.D.; visualization, A.L. and A.J.; writing--original draft preparation, V.P., R.J., A.L., and A.J.; writing--review and editing, V.P., R.J., G.D., A.L., A.J., and V.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Research and Innovation Fund of Kaunas University of Technology (project grant no. PP34/2102) and the Research Fund of Lithuanian University of Health Sciences (project grant no. LSMU-08).  
The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Regional Bioethics Committee at the Lithuanian University of Health Sciences (no. BE-2-106).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy and ethical reasons.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Potential of Homomorphic Encryption for Cloud Computing Use Cases in Manufacturing
## 2 Fundamentals
### Encryption  
#### 2.2.1 General Encryption Process  
Encrypting information, messages, or data has been established for decades. The oldest known encryption technologies were already used in Sparta or Rome for military communication [22]. Encryption aims to prevent unauthorized persons from accessing information, messages, or data. To achieve this goal, almost all encryption technologies are designed to provide maximum computational security. This means that the computational effort to decrypt data is not in proportion to the benefit of its reveal [23, 24].  
Regardless of the encryption technology, the general encryption process is identical and can be described based on the quintuple (P, E, K, C, and D) of cryptosystems, as shown in Figure 1. The information or data to be secured is given as plaintext (P). To turn this plaintext into an illegible form, encryption (E) is executed. Encryption is a mathematical transformation of the plaintext by combining an invertible encryption function with the corresponding key (K). This turns the readable plaintext into ciphertext (C), which is illegible for everyone but the intended recipient. To then make the ciphertext readable again, decryption (D) is executed, using the inverted encryption function in combination with the key to display plain text again [23, 24, 25, 26].  
In most cases, the encryption function is based on known algorithms [24]. However, the algorithms and functions of classical encryption technologies can only be applied to data in storage and data in transit (see Section 2.1) [27]. Calculations and analyses on data encrypted with classical encryption functions would either result in a wrong result or might even lead to errors due to the illegible ciphertext, which might not even be readable by the algorithms. Thus, calculations are not possible on the ciphertext, but only on plaintext. Data would need to be decrypted to be used for calculations and, thus, be readable by the person or company that is analyzing the data [27].  
However, so-called in-use encryption technologies allow calculations on encrypted data. The most promising technology is homomorphic encryption, which is presented in Section 2.2.2[18, 28].
## 3 Analysis and Experimental Verification
### Experimental Verification  
The SBFITS prototype is shown in Figure 7, which mainly includes a DWP, a 50 mm imaging lens L, a polarizer P, an analyzer A, and a CMOS detector (4024 \(\times\) 3036 pixels, with a single pixel diameter of 1.85 \(\upmu\)m). A typical interferogram captured by SBFITS is shown in Figure 8, which contains both intensity patterns and interference fringes.  
The air gap is controlled by a stepper motor and an optical switch, enabling direct adjustments to the wave offset and indirect adjustments to the spectral resolution. The relationship between the offset and \(s\) is shown in Figure 9. As \(s\) increases, the offset also increases. The offset is directly related to the OPD of the system. The larger the offset, the  
Figure 8: A typical interferogram.  
Figure 7: The SBFITS prototype.  
larger the OPD. Therefore, the OPD of the system can be controlled by controlling the air gap, thereby adjusting the spectral resolution of the system.  
Since the luminous flux was related to the system aperture, and the aperture was determined by the offset, it was necessary to verify the accuracy of the calculated offset, and the experiments were conducted to directly observe the offset amount. The Wollaston prisms were the same as the above calculation. The target (Figure 10a) was 1 m away from the lens. Since the target was not at infinity, when the target light was split by the DWP, two images were formed on the CMOS. The distance between the images was \(\alpha d_{total}\), where \(\alpha\) was the magnification of the lens, which could be determined by [25]:  
\[xx^{\prime}=ff^{\prime}, \tag{11}\]  
\[\alpha=-\frac{f}{x}, \tag{12}\]  
The width of a single pixel was \(l=3.45\) um; therefore, in the captured picture, the pixel spacing between the images should be \(d^{\prime}=\frac{ad_{total}}{l}\), as shown in Figure 10b-d, which display target images captured at different air gap thicknesses (\(s=0\) mm, \(5\) mm, and \(10\) mm). The pixel spacings were 31 pixels, 54 pixels, and 79 pixels in the horizontal direction, which  
Figure 10: Verification experiment of the beam splitting effect of DWP (**a**) Target; (**b**) Image of \(s=0\) mm; (**c**) Image of \(s=5\) mm; (**d**) Image of \(s=10\) mm.  
Figure 9: Spatial distribution of displacement with different \(s\).  
corresponded to the z-direction. The calculated results were in good agreement with the experimental results, demonstrating the accuracy of the calculated offset of the SBFTIS.  
The theoretical calculations based on Formulas (10)-(12) demonstrated a linear relationship between pixel spacing and air gap thickness. The comparison between experimental and theoretical values indicated good agreement, as shown in Figure 11. The red dots in the figure were the experimental results.
# Lithium Battery SOH Estimation Based on Manifold Learning and LightGBM
## 5 Instance Validation
### SOH Prediction Based on LightGBM Model
Here, MSE, MAE, and \(R^{2}\) are characterized by the following formulas, respectively:  
\[MSE=\sum_{i=1}^{N}\frac{1}{N}(f(x_{i})-y_{i})^{2} \tag{21}\]  
\[MAE=\frac{1}{N}\sum_{i=1}^{m}\lvert f(x_{i})-y_{i}\rvert \tag{22}\]  
\[R^{2}=1-\frac{SS_{residual}}{SS_{total}} \tag{23}\]  
In the above equation, \(f(x_{i})\) is the true value; \(y_{i}\) is the predicted value; \(i=1,2,\ldots,N\), \(N\) is the sample size; \(R^{2}\) is a measure of the overall fit of the regression equation, which takes values between [0, 1]; \(SS_{residual}\) represents the regression sum of squares; \(SS_{total}\) represents the total sum of squares. AE in Table 2 indicates the absolute value of the difference between the actual RUL and the predicted RUL of the cell. Figure 13 characterizes the prediction error distribution of batteries 5, 6, and 7, the horizontal axis represents the error between the predicted value and the true value, and the vertical axis is the frequency of the occurrence of this error interval. Combined with  
Figure 13: SOH prediction error distribution of dataset A. (**a**) Battery 5 test set prediction error. (**b**) Battery 6 test set prediction error. (**c**) Battery 7 test set prediction error. Figure 12 and Table 3, for \(R^{2}\), the effect of all three groups of batteries was above 0.98, with a mean value of 0.9892; for AE, an index for evaluating the predictive effect of RUL, the error of both battery 5 and battery 7 was 0, while battery 6 showed an error of one cycle from the true RUL. The error intervals of the three batteries are basically concentrated between 0 and 0.05, especially for battery No. 5, whose error is basically below 0.02 Ah. It can be proved that the feature engineering and LightGBM model established in this paper still have greater advantages in the field of SOH estimation.
# An Efficient Task Autonomous Planning Method for Small Satellites
## 3 Methods
### The Overall Process of the Algorithm  
* Step 1: The length of a rolling window is T. Look ahead for T and update the tasks information in the rolling window. We also define a selected time \(t\) from the starting time of a rolling window as a planning unit. As shown in Figure 2.
* Step 2: Plan the tasks autonomously, the specific rules of the autonomous task planning are introduced in Section 3.2.  
* Step 3: Check if there are still tasks to be arranged in the rolling window, and if so, return to the fourth step; if there is no task, then directly carry out the fifth step.
* Step 4: If the tasks are urgent, insert the tasks into the plan of the current rolling window based on the re-planning algorithm. If not, the sequence of tasks remains unchanged in the planning unit of the current rolling window, and the new tasks are planned in a new rolling window.
* Step 5: Execute the task sequence in the planning unit \(t\), and scroll into the next round.  
The process of autonomous task planning is shown in Figure 3.  
Figure 3: The process of autonomous task planning.  
Figure 2: The rolling process.