esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### The Construction Process of ASFC-RA
\(F_{j}\) is the opportunities for service facility \(j\), which can be the economic scale or building area of service facility \(j\) commonly. In consideration of there being no uniform scale of POI data, we set \(F_{j}\) to one. \[V^{\prime}_{ASFC-RA}(i)=\sum_{s}V_{ASFC-RA}(i,s,t) \tag{2}\]  
\(V^{\prime}_{ASFC-RA}(i)\) in Equation (2) is the accessibility value of all types of service facilities for residential area \(i\). \(V^{\prime}_{ASFC-RA}(i)\) reflects the quantity obtained by all service facilities, but it does not reflect the diversity of service facilities. Based on the existing research methods, the diversity of the service facility can be measured from the Shannon entropy index, which is shown as Equation (3):  
\[H_{i}=-\sum_{S}[P_{i}(s)\times lnP_{i}(s)]\text{among them},\ P_{i}(s)\ =V_{ASFC-RA}(i,s,t)/V^{\prime}_{ASFC-RA}(i) \tag{3}\]  
In Equation (3), \(H_{i}\) is the Shannon entropy of residential area \(i\), while \(P_{i}(s)\) is the ratio between \(V_{ASFC-RA}(i,s,t)\) and \(V^{\prime}_{ASFC-RA}(i)\). \[W_{i}=H_{i}/lnS \tag{4}\]  
Based on \(H_{i}\), the relative entropy \(W_{i}\) is the relative entropy weight in Formula (4), which ranges from zero to \(lnS\). \(S\) is the species number of service facilities. Multiple types of service facilities can have different effects on different residential areas. The modified cumulative opportunity measure formula based on the entropy weight index is shown in Equation (5):  
\[V^{\prime\prime}_{ASPF-RA}(i)=\sum_{s}V_{ASFC-RA}(i,s,d)\times W_{i} \tag{5}\]  
Normalization that transforms the dimensional expression into dimensionless expression is a way to simplify the calculation process. The values of ASFC-RA that have been max-min normalized is obtained by the normalization Equation (6). \(V^{\prime\prime}_{ASPF-RA}(max)\) indicates the maximum value of \(V^{\prime\prime}_{ASPF-RA}(i)\), and \(V^{\prime\prime}_{ASPF-RA}(min)\) indicates the minimum value of \(V^{\prime\prime}_{ASPF-RA}(i)\). \[V_{ASFC-RA}(i)=\frac{V^{\prime\prime}_{ASPF-RA}(i)-V^{\prime\prime}_{ASPF- RA}(min)}{V^{\prime\prime}_{ASPF-RA}(max)-V^{\prime\prime}_{ASPF-RA}(min)} \tag{6}\]
# Convolution Accelerator Designs Using Fast Algorithms
## 1 Introduction  
In recent years, convolutional neural networks (CNNs) have been widely used in computer analysis of visual imagery, automatic driving and other fields because of their high accuracy in image processing [1; 2; 3; 4]. Many embedded applications have limited performance and the power consumption [5; 6] and CNNs are computationally intensive and consume large amounts of processing time, making it difficult to apply them to these types of applications.  
At present, there are two solutions to accelerate the use of CNNs. One is to reduce the computational complexity of the neural network; many such methods have been proposed that maintain the accuracy, including quantification, tailoring, sparsity and fast convolution [7; 8; 9; 10; 11; 12; 13]. The other solution is to use a high-performance, low-power hardware accelerator. A graphics processing unit (GPU) is an outstanding processor, but its power consumption is very large [14]. Instead, a field-programmable gate array (FPGA) and the application-specific integrated circuit (ASIC) are energy-efficient integrated circuits. The FPGA has been extensively explored because of its flexibility [15; 16; 17; 18; 19].  
In general, the convolution operation occupies most of the computation time of the CNN. There are several fast algorithms that can reduce the computational complexity of convolutions without losing accuracy [19; 20]. Reference [21] evaluated the Winograd algorithm on FPGAs and proved its efficiency. We propose a faster algorithm based on the Strassen and Winograd algorithms reported in our previously published paper [22]. We propose several convolution accelerator designs using fast algorithms. The implementation results using an FPGA show that the design based on the Strassen-Winograd algorithm can reduce the power consumption by 21.3% compared to the traditional design.  
The rest of this paper is organized as follows: Section 2 provides a detailed description of the CNN and fast algorithms, including the Strassen, Winograd and Strassen-Winograd algorithms; Section 3 describes the architecture of the accelerator designs based on fast algorithms; Section 4 describes the implementation results; and Section 5 presents the conclusions of our study.
## 4 Experimental Study and Results Analysis of the Device
### Experimental Study of the Driving Module
#### 4.2.2 Results and Analysis  
In order to facilitate analysis, the test data were plotted as curves, as shown in Figures 15-17. Figure 15 shows the curve of test pressure changing with time, Figure 16 shows the curve of rotation speed changing with time, and Figure 17 shows the curve of rotation speed and test pressure.  
As shown in Figures 15 and 16, both pressure and rotation speed are applied in two steps. In the first stage, the pressure is increased to 10 MPa and the rotation speed is increased to 60 r/min within 1 min, and the working condition is maintained for approximately 3 min. By observing the curve of test results, it is found that the pressure remains stable, and the rotation speed of the rotating shaft is basically unchanged. Therefore, under the condition of low pressure and low rotation speed, the test system can run normally, which preliminarily verifies the stability of the test system function.  
On this basis, the pressure is further increased from 10 MPa to 140 MPa and the rotation speed from 60 r/min to 150 r/min within 1 min, and the stable running time is 105 min under this condition. There is no leakage of flow or pressure in the whole test process. Therefore, under the extreme conditions of ultrahigh-pressure of 140 MPa and rotation speed of 150 r/min, the driving pump station can provide a stable power source, and the sealing structure has good sealing performance.  
Furthermore, the rotation speed was reduced from 150 r/min to 142 r/min and maintained for 15 min under the condition that the ultrahigh-pressure was maintained at 140 MPa. The test system can also run stably. The rotation speed was further reduced, as shown in Figure 17. No leakage of pressure or flow was detected during the test. Therefore, the driving module can operate safely and stably without pressure or flow leakage risk by reducing rotation speed under the condition of maintaining constant ultrahigh-pressure. It is suggested that the rotating speed should be reduced first and then the pressure should be reduced after the test with the in-situ deep simulation coring device.  
Figure 14: Ultrahigh-pressure dynamic system experimental equipment.  
Figure 16: Rotation speed vs. time.  
Figure 15: Test pressure vs. time.
## 3 Integrated User-Oriented Framework
### End-User Application
#### 3.3.1 Helping User to Select the Printing Material
Given that most 3D printing applications work with pre-established materials, changing to another paradigm with many different types of materials and qualities requires making changes to these applications. Open applications facilitate this adaptation. However, what is most important is that the process is almost transparent to the user. The integrated architecture and the implementation of a \"user guide process\" for material selection as presented in this section would fulfil the requirement of translating massive plastic qualities into standard printing services.
# MapReduce Algorithm for Variants of Skyline Queries: Skyband and Dominating Queries
## 4 Skyband and Dominating Query Processing
### Skyband and Dominating Objects Computation  
Each map worker takes the previous \(DC\) sets as inputs and performs domination checks between corresponding objects and \(DC\) sets to produce \((\mu/SB,Score)\) pairs, where the \(\mu\) score is the number of objects dominated by an object. In contrast, the \(SB\) score of an object is the number of dominant objects. However, to compute either a \(\mu\) score or \(SB\) score, our method does not need to perform a domination check with any objects outside the \(DC\) sets. This advantage stems from the following theorem.  
**Theorem 1**: _For two objects \(\{O,O^{\prime}\in DS\}\), if \(O^{\prime}\) is not in the \(DC\) set of object \(O\), then \(O\) cannot dominate object \(O^{\prime}\) (i.e., \(O\
ot\sim O^{\prime}\))._  
Let \(a_{s}\) be the worst rank attribute of \(O\). If \(O\) dominates \(O^{\prime}\), \(O^{\prime}\) must be in the \(DC\) set of \(O\) because \(O.a_{s}\leq O^{\prime}.a_{s}\). If \(O^{\prime}\) is not in the \(DC\) set of \(O\), it means \(O.a_{s}>O^{\prime}.a_{s}\). Therefore, \(O\) cannot dominate \(O^{\prime}\).  
Theorem 1 demonstrates that it is sufficient to perform a domination check between an object \(O\) and the corresponding \(DC\) set to determine whether or not \(O\) is in the query results. To analyze this result, recall object \(O_{2,1}\) and its corresponding \(DC\) set \(O_{2,2},O_{3,2}\). Because \(O_{2,1}\) has the worst rank of 5 for attribute \(a_{1}\), this means that this object has no possibility to dominate a higher rank object, such as \(O_{2,1},O_{1,1},O_{3,1}\), or \(O_{3,3}\). This means we need to perform a domination check only between \(O_{2,1}\) and its \(DC\) set \(\{O_{2,2},O_{3,2}\}\).  
Figure 7 presents the skyband and dominating objects computation process. To compute \(K\)-skyband, if we set \(K=2\), the query returns \(\{O_{1,1}\), \(O_{1,2}\), \(O_{2,1}\), \(O_{2,2}\), \(O_{3,3}\}\) as the two-skyband objects set, since the \(SB\) score values for these objects is less than 2. Then, our method outputs objects \(O_{1,2}\) and \(O_{2,1}\) as the top-2 dominating result because both objects have the highest \(\mu\) score value.  
After performing a domination check, each map worker produces two types of keys: the \(\mu\) score for the corresponding object and the \(SB\) score for all objects. If an object dominates another object in the \(DC\) set, the \(\mu\) score value of the corresponding object and \(SB\) score value of the  
Figure 7: Skyband and top-\(k\) dominating computation.  
incremented by 1. Next, all of the \(\mu\) scores and \(SB\) scores are sent to the reduce workers. After applying a \"group-by\" operation, the reduce workers can provide \(K\)-skyband and top-\(k\) dominating query results.
# A Formal Analysis of the Mimblewimble Cryptocurrency Protocol +
## 4 Verification of Mimblewimble
### Privacy and Security Properties  
In blockchain systems the notion of privacy is crucial: sensitive data should not be revealed over the network. In particular, it is desirable to ensure properties such as confidentiality, anonymity and unlinkability of transactions. Confidentiality refers to the property of preventing other participants from knowing certain information about the transaction, such as the amounts and addresses of the owners. Anonymity refers to theproperty of hiding the real identity from the one who is transacting, while unlinkability refers to the inability of linking different transactions of the same user within the blockchain.  
In the case of MW no addresses or public keys are used; there are only encrypted inputs and outputs. Privacy concerns rely on confidential transactions, cut-through and CoinJoin. CoinJoin combines inputs and outputs from different transactions into a single unified transaction. It is important to ensure that the resulting transaction satisfies the validity defined in the model.  
The security problem of double spending refers to spending a coin more than once. All the nodes keep track of the UTXO set, so before confirming a block to the chain, the node checks that the inputs come from it. If we refer to our model, that validation is performed in the predicate _validate_ mentioned in Section 3.4.
## 1 Introduction
Based on these findings, it is evident that the success of integrating AI into enterprises as DSSs is closely linked to employees' trust in AI [24] and their perception of maintaining control over decisions. This also influences their willingness to accept and follow artificial recommendations [25]. Given the importance of user trust in AI, an important question we have considered is: _Does users' trust in AI-based DSSs depend solely on the algorithm's qualities? To what extent is it influenced by their trust in the overall service in which the AI tool is embedded?_  
An additional level of complexity arises when the development of these AI-based systems occurs within the context of investments in human capital (IHC), which refers to investments in intangible assets such as knowledge, skills, and abilities of individuals [26]. Precisely, IHC is not a unilateral investment but a reciprocal one, since it implies the will, motivations, and goals of each human actor involved [27]: unlike financial investments where investors are the sole decision-makers, IHC involves at least two classes of decision-makers, those who invest and those who seek investments. Examples of IHC include startup funding [27] and organizational development [28]. On the one hand, venture capitalists must choose which resources (startup teams) to invest in, and, similarly, HR managers and people managers have the task of selecting employees to invest in for organizational development. On the other hand, both the startup founders and the employees seeking development and training opportunities must make an important decision: how to invest their skills, ideas, time, and energy in the best way. For these reasons, we define this kind of DM as multi-actor decision-making (MADM), \"_since not a group is making decisions but different actors, or groups of different actors,who--starting from non-coinciding objectives--need to reach a mutual agreement and converge toward a common goal for the success of the investment\"_ ([27], p. 1). Hence, considering the numerous specific aspects of the case and the related issues that have emerged concerning both the interaction with technology and the type of DM, we found it useful to explore and establish a methodology that could support the design of AI-based DSSs with the following characteristics:  
* Multi-actor: They are able to facilitate negotiation practices among multiple actors with different goals and perspectives;
* User-centered: They are focused on the needs, desires, and limitations of prospective users;
* Transparent: They offer a clear and explainable functioning mechanism;
* Trustworthy: They instill trust and legitimacy in their users. There is also increasing evidence supporting the importance of including ecological criteria for designing technologies [29], to capture the complexity and contingency of real-life actions in specific situations [30]. To this aim, we believe we can benefit from design thinking (DT) as a user-centered approach to help bridge the trust gap toward AI-based services and systems and enable negotiation among multiple actors in the field of IHC.
# Metachrylate-Based Polymeric Sorbents for Recovery of Metals from Aqueous Solutions
## 5 Adsorption of Metal Ions on Polymeric Sorbents--General Remarks
### Adsorption Thermodynamics
In the case of positive enthalpy change, the reaction is endothermic, meaning adsorption efficiency rises with the temperature increase. When the \(\Delta H^{0}\) value is in the range 2.1-20.9 kJ/mol, the adsorption process is physical. On the other hand, the \(\Delta H^{0}\) value in the range of 80-200 kJ/mol suggests chemisorption [85].
# Calibration Method of Orthogonally Splitting Imaging Pose Sensor Based on General Imaging Model
## Materials and Methods
### Calibration
#### 2.2.2 Experimental Apparatus  
The experimental apparatus consists of a target, a peripheral component interconnect (PCI) controller (independent research and development) of the target, a motorized stage, a mechanical controller of the motorized stage, and an orthogonally splitting imaging pose sensor. Among them, the PCI controller of the target controls the LEDs, which are lightened up at different times, and the mechanical controller of the motorized stage controls the movement of the motorized stage. The experimental apparatus is shown in Figure 4. The main apparatus parameters are shown in the Table 1.
# Hypergraph-Driven High-Order Knowledge Tracing with a Dual-Gated Dynamic Mechanism
## 2 Related Work
### Hypergraph Neural Networks  
Traditional knowledge tracing methods (e.g., BKT, IRT) and existing deep learning models (e.g., DKT, SAKT) typically rely on graph structures or sequential modeling to capture knowledge concept correlations. However, constrained by the binary relationship representation capability of ordinary graphs, they struggle to explicitly model high-order correlations and hierarchical dependencies between exercises and knowledge concepts. Recently, hypergraph neural networks (HGNN) [29; 30; 31] have gradually been introduced into educational data mining due to their superiority in modeling many-to-many complex relationships. Unlike conventional graphs, hypergraphs utilize hyperedges to connect multiple nodes simultaneously, making them inherently suitable for capturing multidimensional collaborative effects among knowledge concepts. For instance, in knowledge tracing scenarios, a single exercise may involve multiple knowledge concepts (forming a hyperedge), while different exercises can establish high-order correlations through shared subsets of knowledge concepts. Although existing studies have applied GNN [9] to KT tasks (e.g., the GKT model [10]), they remain limited to low-order neighborhood information aggregation and fail to capture multi-hop collaborative influences across exercises. The proposed HGKT model innovatively employs a dual-layer hypergraph convolutional architecture: the first layer models exercise-knowledge concept membership relationships, while the second layer discovers high-order exercise correlations based on shared knowledge concepts, thereby explicitly deconstructing the collaborative evolution mechanism of knowledge states. This approach transcends the pairwise relationship constraints of traditional graph models, providing finer-grained interpretable representations for knowledge tracing.
## 2 Background and Related Work  
In general, compliance aims to ensure that the operations in an organization are in alignment with the governing rules, policies, standards, regulations, and laws originating from within the organization or from external (regulatory) bodies [20]. In the last two decades, the necessity for compliance has been augmented by the increasing number of laws and regulations issued in several domains, such as finance, banking, environment, and healthcare [21]. For instance, the de Sarbanes-Oxley Act on financial record keeping and reporting for corporations was issued in 2002 [22].  
With regard to the timing, organizations employ different strategies to ensure compliant operations [23; 24]. The compliance assessment can take place (i) at _design-time_, that is, at the early stage of process (or operation) design; (ii) at _run-time_, that is, when the processes are running, and (iii) _ex-post_ (offline), that is, after execution of the processes with audits examining the traces (logs) of executed processes. While the design- and run-time assessments are typically preventive, i.e., aim at detecting violations before they occur, ex-post assessments or audits are detective and focus on possible violations and trends [21].  
The studies on compliance assurance of processes typically focus on one of the above-mentioned stages and concentrate on specific process concerns (e.g., control flow, resource, temporal, or data related) [21]. The literature reports on pattern-based approaches to aid in the formal specification of compliance rules that can be used for design-time, run-time or offline assessments (e.g., [25; 26; 15; 27]). Using execution traces residing in enterprise information systems is also introduced as an alternative approach that can be employed for auditing operations in the organization [28; 29]. However, despite the vast research in the process compliance research field [21], very few articles study the measurement or quantification of the compliance level. In the remainder of this section, we discuss the existing approaches, focusing particularly on those applied in the healthcare domain. In this context, we consider a clinical protocol as a domain specific process model/specification [30]. Moreover, in our work, we focus mostly on the ex-post compliance assessment.
# Radiation Hardened Read-Stability and Speed Enhanced SRAM for Space Applications
## 3 Simulation Results and Analysis
### Soft Error Robustness Analyses  
To evaluate the robustness of the proposed cell against single event transient (SET) or soft errors, we examined its tolerance to soft errors by introducing a current source into the vulnerable nodes. The double exponential current is expressed as follows:  
\[\mathrm{I(t)}=\frac{\mathrm{Q}}{\tau_{\alpha}-\tau_{\beta}}\left(\exp\left(- \frac{\mathrm{t}}{\tau_{\alpha}}\right)-\exp\left(-\frac{\mathrm{t}}{\tau_{ \beta}}\right)\right) \tag{1}\]  
\[\mathrm{Qc}=\int_{0}^{\mathrm{t_{flip}}}\mathrm{I(t)dt} \tag{2}\]  
The constants \(\tau_{\alpha}\) and \(\tau_{\beta}\) represent the time constants for the initial ion track formation and are set to 200 ps and 50 ps, respectively [7; 18]. The robustness of the SRAM is measured by the critical charge (Qc). Qc refers to the minimum amount of charge required to flip the stored value in the cell when a sensitive node is affected by an SET. It is measured based on the node with the smallest Qc among the various nodes in the SRAM. Qc is expressed as follows, where \(\mathrm{t_{flip}}\) denotes the time at which the cell's stored value is flipped.  
The Qc values of the existing and proposed SRAM cells are listed in Table 1. Cells where the value of the storage node did not flip within the measurement range are marked with a Qc of 300 or higher. Except for RHSCC-16T and DNUCTM, the remaining cells have finite Qc values because they are not fully immune to SNU or DNU. The proposed cell is immune to all cases of SNU and, in conjunction with layout techniques, is also immune to all possible cases of DNU and MNU. Therefore, it is highly suitable for environments exposed to radiation.  
Figure 11: Comparison results of the hold power of the proposed RHBSE-20T with previous works.
## 3 Localization Algorithm
### BLE Based Algorithm
The set of equations making this phase is as follows:  
\[K_{k}=P_{k(-)}H_{k}^{T}\left(H_{k}P_{k(-)}H_{k}^{T}+R_{k}\right)^{-1} \tag{5}\]  
\[\hat{x}_{k(+)}=\hat{x}_{k(-)}+K_{k}\left(z_{k}-h_{k}(\hat{x}_{k(-)})\right) \tag{6}\]  
\[P_{k(+)}=\left(I-K_{k}H_{k}^{T}\right)P_{k(-)} \tag{7}\]  
\[z_{k}=\left[RSS_{1}\quad\cdots\quad RSS_{n}\right] \tag{8}\]  
\[h_{k}(x_{k})=\left[RSS_{1}(x_{k})\quad\cdots\quad RSS_{n}(x_{k})\right] \tag{9}\]  
where:  
* \(z_{k}\) is the measurement vector containing RSS (\(RSS_{n}\)) measurement results,
* \(h_{k}(x_{k})\) is the sensor model used to calculate measurement values which would be obtained for the predicted tag localization,
* \(H_{k}\) is a linearization of the sensor model,
* \(K_{k}\) is the Kalman gain,
* \(R_{k}\) is the measurement covariance matrix. In the proposed algorithm, the power levels received by the anchor (sensor model) were calculated using the exponential path loss model:  
\[RSS_{n}(x_{k})=RSS_{0}-10\gamma\log_{10}\frac{d(x_{k})}{d_{0}} \tag{10}\]  
where:  
* \(RSS_{n}\) is the signal power received by the anchor \(n\),
* \(d\) is the distance between the anchor and predicted tag localization,
* \(RSS_{0}\) is the received power at the reference distance from the tag \(d_{0}\),
* \(\gamma\) is the path-loss exponent. The prediction updated with the measurement results is the final result of the algorithm iteration and is used as the input for the next EKF iteration.