esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Basic Principles of Cryptography
#### 2.2.3 Threshold Cryptography  
A very promising field of cryptography within a flat hierarchy is Threshold Cryptography. This follows a \((k,n)\)-threshold scheme. Here, \(n\) stands for the number of invoked parties and \(k\) for the minimum number which is necessary to decrypt a secret. To explain Threshold Decryption it is helpful to take a look to the principles of [PERSON]'s Secret Sharing [12]. [PERSON] requires one dealer, which is trusted by all parties, as he knows every part of the key and he has to distribute them to the parties. [PERSON]'s scheme relies on the construction of a polynomial following the pattern \(f(x)=s+a_{1}x+a_{2}x^{2}+...+a_{t-1}x^{t-1}\) where \(s\) represents the secret. Hence, all \(a_{i}\) values can be chosen unrestrictedly by the dealer. Afterwards, the dealer can generate \(n\) arbitrary value tuples \((x,f(x))\), where \(x\) can be disclosed, but as \(f(x)\) is a share of the key, it must be kept as a secret. At least \(k\) value tuples are required to calculate a polynomial and consequently also the secret \(s\).  
In Figure 1, a polynomial is shown where the \(y\)-value is marked blue at \(x=0\). This is equivalent to the secret \(s\) in [PERSON]'s principle. The red marked points are basic values which are distributed to every single party by the dealer. The \(x\)-value of the basic values are public, the corresponding \(f(x)\)-values are subject to non-disclosure and are known only by the respective party and by the dealer. If there are enough \((x,f(x))\)-pairs available, it is possible to solve the polynomial and calculate the secret \(s\) accordingly.
## 4 Discussion
### Strengths and Limitations  
The strengths of this study included the adequate sample size and many steps, as a result of the relatively long measurements in multiple conditions. The use of test-retest reliability makes the evidence more credible. The protocol of testing validity under different types of gait is important because those are the conditions used in a gait retraining session, which makes the results clinically meaningful.  
The findings of this study should, however, be interpreted in the context of two main limitations. First, all participants were healthy and were asked to walk with an increased toe-in or toe-out angle for the last two conditions. Thus, the accuracy may differ in people with a movement disorder, e.g., foot drop. Second, at the end of the 10-m-long path participants had to turn around each time, until a duration of 90 s was achieved. These turns were only recorded by the IMUs and were automatically deleted from the dataset by the APDM software, but these turns could have influenced the participants' gait during the few steps between the PSW and the turn. As described before, APDM and PKMAS have a different definition and measurement of the FPA. The line of progression is defined by APDM when the foot is in swing phase, and by PKMAS it is calculated on the basis of footprints. Besides the differences in the measurement precision, the difference in definition may also have been a source of the differences in FPA.
# Revising the Observation Satellite Scheduling Problem Based on Deep Reinforcement Learning
## 2 Problem Description
### Task Scheduling Problem  
#### 2.2.1 Scheduling Model  
In this paper, a time-continuous resource allocation model for the EOSSP is established. Continuous decision variables are introduced to represent the observation start time within each VTW and decision variables are defined to check whether tasks are scheduled or not.  
Figure 3 gives a task sequential execution description in one orbit, where \(TWS\) and \(TWE\) stand for the start and end time of the VTW of an observation task, respectively, \(d\) represents the observation duration time of a task, \(tranT\) and \(s\) represent slewing angle maneuver time and preparation time, respectively, and \(ObvS\) and \(ObvE\) are the observation start time and end time.  
Figure 3: Description of the observation task scheduling sequence.  
Figure 2: Illustration of clique partition for clustering graph.
## 2 Theoretical Analysis Model and Calculation Method
### Effect of Fastener Parameters on System Power Flow Characteristics  
The fastener, serving as the connecting component between the entire track bed and the track, holds a crucial role in governing the power flow transmission within the track structure. The stiffness and damping of the fastener have a significant impact on this process. Exactly based on the concept of control variables, two methods are employed in this study to analyze the influence of fastener parameters on the transmission characteristics of power flow within the system. The first method involves changing the stiffness of the fastener while maintaining its damping constant. The second method entails altering the damping of the fastener while keeping its stiffness constant. Through these approaches, the influence of deduction parameters on power flow transmission characteristics is thoroughly investigated.
# Aggregatable Subvector Commitment with Efficient Updates
## 1 Introduction
### Related Work  
Vector commitment is a new cryptographic primitive first proposed by the authors of [1; 15; 16] as an alternative to Merkle trees [18]. It has several attractive properties, such as compactness, constant-size proofs, and the ability to aggregate multiple proofs into a single constant-size proof. [PERSON] et al. [2] constructed two vector commitment schemes based on the CDH and RSA assumptions. Although both can achieve the basic functions of commitment and proof, the RSA-based scheme still has room for improvement in aggregation efficiency. [PERSON] et al. [8] further studied the new features of vector commitments and proposed subvector commitments (SVCs) that support opening commitments on arbitrary subsets of positions, and its proof size is related to the number of opened elements. In order to make SVCs more efficient and suitable for distributed systems, [PERSON] et al. [9] proposed a new feature of vector commitments based on batch opening, namely incremental aggregation, that supports unlimited proof aggregation. This method allows the already aggregated proofs to continue to aggregate. They designed two implementations based on unknown-order groups, which are similar to the scheme proposed by [PERSON] et al. [10], and both implement constant-size public parameters, commitments and proofs, but the authors of [10] only support single-hop aggregation. [PERSON] et al. proposed the aSVC [3] scheme based on KZG [1], which uses a polynomial method to calculate the aggregation proof, and can simultaneously realize batch opening and aggregation of proofs, but the aggregation efficiency needs to be improved. Hyperproofs proposed by [PERSON] et al. [5] is also based on polynomials, but its aggregation algorithm uses the IPA [19] proof system, and the actual aggregation overhead is large. In order to improve the efficiency of aggregation, [PERSON] et al. [6] proposed BalanceProofs, which takes aSVC [3] as input and outputs a scheme with higher aggregation efficiency, but the aggregation efficiency of this scheme is still not ideal. In order to further optimize the efficiency of the aggregation algorithm, this paper proposes an improved scheme based on the polynomial calculation aggregation proof method, and combines the idea of the Karatsuba algorithm to implement an efficient aggregation algorithm.  
In addition, the previous polynomial commitment scheme [1; 3; 5; 6; 13] will incur a lot of computational overhead if the data set is to be dynamically increased. In order to meet the above requirements, this paper proposes a new vector commitment scheme, which uses a combination of Newton interpolation and the Karatsuba algorithm to construct an efficient and aggregatable vector commitment scheme for dynamic data sets.
## 2 Modelling Tools  
Figure 1 shows schematically the engine used in this work as an exhaust gas generator. This way, different representative boundary conditions of the exhaust gas flow useful for the analysis of DPF active regeneration strategies are defined concerning dwell time, exhaust temperature and O\({}_{2}\) concentration. The main characteristics of the engine and the ATS, which was composed by a close-coupled DOC and DPF brick, are summarised in Tables 1 and 2 respectively. In particular, a 1D gas dynamics model was built in GT-POWER software. To do that the engine was tested coupled to an asynchronous dynamometer for engine speed and torque control. The main characteristics of the instrumentation used to measure the air and fuel mass flow as well as the flow properties in the intake and exhaust lines are summarised in Table 3. The gas composition at the DOC inlet and outlet was measured by a Horiba Mexa 7100 gas analyser and AVL 439 opacimeter. A detailed calibration of the engine model is provided in [31].  
The predictive capabilities of the engine model were improved by specific sub-models. This way, the DI-Pulse (direct-injection diesel multi-pulse mode) model available in GT-POWER was used to simulate combustion process and the raw pollutant emissions from the injection rate profile [30]. The fuel mass is tracked as it is injected, evaporates, mixes with surrounding gas and finally burns. To do that, each injection event is treated as an injection pulse that is tracked separately from all the pulses, without a distinction between pilot, main or post-injection being that the injected fuel is added to the spray unburned zone. Therefore, the timing and the fuel mass rate define the late fuel post-injection to predict the effect that the modification of the injection profile has on the combustion and the pollutants formation. Concerning the last, the CO concentration was calculated based on the thermodynamic equilibrium whilst the raw HC concentration was determined as the difference between the injected and burned fuel mass. The amount of soot was calculated applying the Hiroyasu model [30].  
On the other hand, the turbocharger and ATS sub-models available in GT-POWER were replaced by dedicated user-functions available in OpenWAM [32], which is an open-source gas dynamics software developed by CMT-Motores Termicos [33]. The turbocharger sub-model was applied aimed at a more precise simulation of the heat transfer [34] and the mechanical losses [35] in the turbocharger thus contributing to improving the prediction of the temperature at the turbine outlet [31].  
Once flow properties were defined at the ATS inlet, specific DOC and DPF models were applied to assess the impact of the active regeneration strategy on the soot oxidation. For the sake of completeness, these models are next briefly described.
## 3 Results  
We implemented a proof of concept with eight healthy subjects and five SCI patients to envision the capabilities of the system. As stated in Section 2, the proof of concept provides information about the system functionality to prepare it for a future pre-clinical study. Nonetheless, average and standard deviation results in relation to the system performance for each relevant variable within the games are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Healthy Subjects (_n = 8_)} & \multicolumn{2}{c}{**SCI Patients (_n = 5_)} \\ \hline  & **Average** & **Standard Deviation** & **Average** & **Standard Deviation** \\ \hline
**Maze** & & & & \\ ROM X (mm) & 362.86 & 14.93 & 369.33 & 23.52 \\ ROM Y (mm) & 231.79 & 18.10 & 277.34 & 40.84 \\ Duration (s) & 36.62 & 9.44 & 49.50 & 14.47 \\ Points & 8.50 & 0.76 & 6.90 & 1.82 \\ Errors & 10.69 & 7.33 & 16.70 & 8.58 \\ Error no feedback & 12.12 & 7.61 & 22.80 & 12.93 \\ Error feedback & 9.25 & 7.09 & 10.60 & 5.64 \\
**Path** & & & & \\ ROM X (mm) & 288.65 & 22.61 & 282.86 & 9.72 \\ ROM Y (mm) & 330.04 & 17.52 & 319.98 & 10.94 \\ Duration (s) & 38.62 & 11.39 & 54.40 & 6.57 \\ Points & 13.94 & 2.76 & 14.1 & 3.38 \\ Max value sent & 114.31 & 58.86 & 90.3 & 36.34 \\ Errors & 7.75 & 4.77 & 10.10 & 4.05 \\ E. NO feedback & 7.85 & 5.54 & 11.00 & 4.64 \\ E. feedback & 7.62 & 5.40 & 9.20 & 3.63 \\
**Grab** & & & & \\ ROM (mm) & 95.92 & 12.10 & 106.80 & 13.08 \\ Points & 10.88 & 3.09 & 6.40 & 2.99 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average and standard deviation results for the three games with healthy and SCI groups.  
Figure 6: Example of a healthy trial with the thimble placed on the right hand.  
The results of the arithmetic mean between the data obtained by each user in both trials (with and without feedback) are shown.
# On Analyzing Routing Selection for Aerial Autonomous Vehicles Connected to Mobile Network
## 1 Introduction
The main contributions of this paper are: (1) A practical solution based on real measurements of UAV using mobile network, (2) the introduction of two-phase Machine Learning (ML) tool that makes use of Utility Theory for coherent connection of both ML phases. Two-phase ML connected through a Utility formula can be extended to many other applications, and (3) the introduction of a combination of different prediction algorithms for better estimation of the resources in mobile networks. In the Section 2, we present the related work including the principles of multi-criteria selection, whereas in Section 3 we show the motivation of our system for routing selection. We performed several tests in UAV connected through LTE and we argue that one connection (through one network operator) risks to fail in any moment during the connection if this is not monitored. We propose our solution based on two-phase multi-criteria selection of routing path. In Section 4 our system is formally described based on Utility theory. Section 5 presents the measurements taken on the network as well as the machine learning training and validation. At last, the paper is concluded in Section 6.
## 4 Experiment and Analysis
### Ablation Study  
#### 4.5.1 Modality Ablation Experiment  
To show the degree of influence of different modal combinations on the final sentiment task prediction, modality ablation experiments are conducted in this section on the dataset CMU-MOSEI. Table 5 illustrates the outcomes of the experiments with separate combinations of unimodal, bimodal, and trimodal modes. It should be noted that the acoustic, visual, and language unimodality in Table 5 refer to a branch in the left half of Figure 1 (multimodal task) and the corresponding unimodal branch in the right half of Figure 1 (unimodal task). For example, the check mark \"Acoustic modal\" in the second row of the table means that it contains branch 1 in the left half of Figure 1 and branch 1 in the right half of Figure 1 (acoustic unimodal subtask).  
From Table 5, it is obtained that when the model contains all the branches (i.e., when acoustic, visual, and language modality tasks are used simultaneously), the model has the closest sentiment prediction performance to the labeled values and the best fusion performance. When fusing bimodal information, the performance of the experimental evaluation metrics is the next best. The performance is worse when the model contains only unimodal branches. It can be concluded that multimodal information fusion helps to capture the consistency and difference information between modalities; providing global feature information helps the accuracy of model sentiment task prediction.
## 3 Trends in Operational USVs
### Dry Weight and Payloads  
The dry weight distribution of USVs exhibits a heavily right-skewed pattern, with the majority of vehicles concentrated in the lower weight range (Figure 6a). Most USVs fall below 1500 kg, with frequency gradually decreasing as weight increases. This trend suggests that lightweight USVs dominate the field, likely due to operational efficiency, cost-effectiveness, and ease of deployment. However, a small number of vehicles reach significantly higher weights, extending beyond 10,000 kg, potentially representing specialized or industrial-grade USVs designed for heavy-duty applications such as offshore operations or military use. The presence of these outliers underscores the diversity in USV applications, where both compact and large-scale systems coexist to meet varying operational demands.  
Concerning the payload distribution, it follows a similar right-skewed pattern, with the majority of USVs capable of carrying relatively low payloads, often below 300 kg (Figure 6b). This pattern aligns with the dry-weight distribution, suggesting that most USVs are designed for lightweight applications such as environmental monitoring, small-scale survey missions, and autonomous navigation research. However, a minority of USVs support payloads exceeding 1000 kg, indicating a specialized subset engineered for transporting heavier equipment, cargo, or complex sensor arrays. The correlation between dry weight and payload capacity is evident, reinforcing the idea that larger USVs tend to accommodate greater payloads. These distributions collectively highlight the balance between size, weight, and functionality in USV design, emphasizing the trade-offs between agility and carrying capacity in different operational contexts.
# Electrospun Porous PDLLA Fiber Membrane Coated with nHA
## 3 Results and Discussion
### Features of nHA/PDLLA Composite Fibrous Membranes with Different nHA Content
#### 3.4.2 Water Contact Angles of PDLLA/nHA Composite Fibrous Membranes  
Surface wettability can affect the practical application of scaffold materials in tissue engineering. The strong hydrophobicity of the scaffolds would not be conducive to cells adhesion and crawl quickly [38,39]. From Figure 6-Control, non-porous PDLLA fibrous membrane obtained by electrospinning possesses a water contact angle (WCA) of 118\({}^{\circ}\). This means that the mat showed high surface hydrophobicity [40]. The introduction of hydrophilic nHA particles could significantly improve the hydrophilicity of the fibrous membrane. The relationship between the nHA particles content and the WCA of the PDLLA/nHA composite fibrous membranes is shown in Figure 7A-E. The water droplet could drop into the composite fibrous membranes' content of nHA particles at 10.6 wt % and 11.2 wt %, in a time span of two seconds, and the resulting WCA reached equilibrium was at 0\({}^{\circ}\). They showed the excellent hydrophilicity because lots of nHA particles adsorbed on the surface of composite fibers. When the nHA content was reduced to 8.8 wt %, the WCA increased to 67\({}^{\circ}\) (Figure 7C). The WCA further increased to 89\({}^{\circ}\) and 104\({}^{\circ}\) for the composite fibrous membranes with 5.5 wt % and 4.1 wt % nHA, respectively (Figure 7D,E). It follows that the composite fibrous membranes with 10.6 wt % and 11.2 wt % nHA provided good hydrophilicity, which would help cells adsorbing quickly. The mechanical properties of PDLLA/nHA composite fibrous membranes were further investigated.
## 3 Communication and Channel Modeling
### Channel Modeling
#### 3.1.1 Deployment of UAV-BS and RISeUAV
: _In the channel gain of the CLoS link, the product of two Euclidean norms appears in the denominator, which makes the channel gain formula nonconvex, notably, when the optimization problem is the RISeUAV trajectory design. Notice that in (9), the RIS position \(p^{\mathrm{RIS}}\) is determined by the UAV position. Handling this issue is the contribution of research works that are studied in the optimization solution Section (Section 6)._  
**Remark 4**. : _Although the product of the two Euclidean distances associated with the incident path to and reflection path from the RIS reduces the channel gain, the accumulated signals reflected by the RIS elements significantly enhance the channel gain, making it M times stronger, which compensates for the challenges of CLoS links. This notable improvement is achieved by using RIS without consuming energy or requiring complex processing. Since the phase shifts of RIS elements are passive, RIS presents a promising, sustainable solution to enhance the performance of future WCNs. However, the RISeUAV may need to fly at lower altitudes to increase channel performance, which introduces potential obstacles such as skyscrapers and urban canyons. Therefore, the trajectory optimization method must account for an obstacle-free path to avoid collisions._  
**Remark 5**. : _The rationale behind the phase shift design in (8) is that perfect channel state information (CSI) is available, which is not practical in UAV-enabled communication, particularly for mobile IoVs. Therefore, the possible cost-effective solution is directional phase shift/beamforming,  
Figure 4: Schematic of RISeUAV-assisted communication for channel modeling: (**a**) geometry of system in 3D coordinates; (**b**) UPA of the RIS in XY plane; \(v^{RU}\) and \(u^{RU}\) denote UAV’s horizontal and vertical linear velocities, respectively; \(\omega^{RU}\) denotes the UAV’s horizontal rotational velocity and \(\theta^{RU}\) denotes the UAV heading (angle) with respect to the X-axis. The UAV motion is studied in Section 4.  
considering that the localization information of the UAV-BS, RISeLAV, and UEs are available [42]. It is not an unwarranted assumption as the mmWave localization has been studied in the literature, which is discussed as the integrated localization and communication inSection 7.
# Study of Partial Discharge Inception Voltage in Inverter Fed Electric Motor Insulation Systems
## 2 Literature Review
### Parallel-Plate Capacitor Model  
[PERSON] [59] proposed a PDIV prediction model for twisted pairs based on two well-known equations. On the one hand, it simplifies a twisted pair as a parallel-plane capacitor with two copper conductors separated by an enamel-air-enamel layer. It assumes that the thickness of the enamel (in \(\mu\)m) is significantly lower than the diameter of the copper wire (in mm), so its curvature can be neglected. Therefore, the series capacitor simplified insulation model from Equation (9) proposed by [75] can be used. On the other hand, bearing in mind that the PDIV is the minimum voltage at which the voltage across the air gap is equal to the breakdown voltage of the air, defined using [PERSON]'s law [25] (Equation (2)), it concludes that the PDIV is the lowest total voltage \(V\), where \(V_{\mathrm{air}}=V_{\mathrm{B}}\) for different air gap sizes (Equation (10)).  
\[V=\bigg{(}\frac{V_{\mathrm{air}}}{d_{\mathrm{air}}}\bigg{)}\bigg{(}d_{ \mathrm{air}}+\frac{t_{\mathrm{ins1}}}{\varepsilon_{\mathrm{r1}}}+\frac{t_{ \mathrm{ins2}}}{\varepsilon_{\mathrm{r2}}}+\dots\bigg{)} \tag{9}\]  
\[\mathrm{PDIV}=min\bigg{[}\bigg{(}\frac{V_{\mathrm{B}}}{d_{\mathrm{air}}} \bigg{)}\bigg{(}d_{\mathrm{air}}+\frac{t_{\mathrm{ins1}}}{\varepsilon_{ \mathrm{r1}}}+\frac{t_{\mathrm{ins2}}}{\varepsilon_{\mathrm{r2}}}+\dots\bigg{)} \bigg{]} \tag{10}\]  
where \(V\) is the voltage between conductors, \(V_{\mathrm{air}}\) is the voltage in the air gap, \(d_{\mathrm{air}}\) is the thickness of the air gap, \(V_{\mathrm{B}}\) is the air breakdown voltage defined by Equation (2), and \(t_{\mathrm{ins1}}\) and \(\varepsilon_{\mathrm{r}i}\) are the thickness and the relative permittivity of each insulation layer, respectively.  
As [PERSON]'s law is used to develop Equation (10), the limitations described in Section 2.2.1 are directly derived in this model. Firstly, as mentioned, the author assumes that the twisted wire can be simplified to a parallel plane capacitor neglecting the curvature of the enamel and, thus, the air gap distance is defined as the straight distance between electrodes. Secondly, [PERSON]'s criteria are used, with difficulties adjusting the \(\gamma\) (as mentioned by the authors). Finally, the temperature dependence of the Paschen's curve is not considered.