esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Electrical Spectrum  
The purity of the electrical spectrum is evaluated by an electrical spectrum analyzer (ESA, ROHDE&SCHWARZ) with a resolution bandwidth of 3 kHz. The measured output spectrum of both the upconversion and downconversion is described in Figure 7a,b where the transmitted and received RF signals have a frequency of 0.8 GHz, 3 GHz, 6 GHz, 9 GHz, 12 GHz, 15 GHz, and 18 GHz, respectively. It can be seen that the target signals obtained are very pure and have spurious suppression of more than 40 dBc at the in-band 0.8-18 GHz. This is thanks to microwave pre-treatment of the upconversion and the spectrum locations of all the unwanted mixing spurs are over 18 GHz.  
The conversion efficiency is defined by the ratio of the output RF/IF signal power to the input HF signal power, which represents the power response of the proposed microwave photonic converter. The LO frequency is tuned from 21-38.2 GHz in order to measure the conversion efficiency shown in Figure 7c. Furthermore, the power of the HF and LO signals is fixed at 0 dBm, which is in the linear response region of the whole system. We can see that the conversion efficiency reduces with the increase in the LO frequency for both the upconversion and downconversion. Additionally, due to higher frequency of the HF signal in the downconversion process, a disparity emerges between upconversion and downconversion. To further analyze the influencing factors of conversion efficiency, the power responses of the electrical cable, electrical combiner, and the MZM are measured and described in Figure 7d. Combined with the response curves, we find that the transmission responses of all three drop at high frequencies and introduced microwave loss reaches 3.2 dB, 4.5 dB, and 5.8 dB, respectively, at 40 GHz, which coincides with the downtrend of the conversion efficiency. The microwave photonic converter with the high conversion efficiency is applied in practice. To realize this, in addition to the use of the components with greater bandwidth, a PD with higher gain, or an SOA and an EDFA in series, can also be considered.  
Figure 6: Measured optical spectrum before and after EDFA.
## 4 Sensing-Based SPS Scheduling with Lookahead
### SPS/LA Algorithm
```
1:procedureLA-SPS (\(RRI\), \(T1\), \(T2\), \(C1\), \(C2\), \(RC_{LA}\), \(N_{subCH}\), \(probResourceKeep\))
2:\(txSubCH\gets random(1,N_{subCH})\)\(\triangleright\) Initializations for \(txSubCH,txSubframe,RC\)
3:\(txSubframe\gets random(1,RRI)\)
4:\(RC\gets random(C1,C2)\)\(\triangleright\) This is the current time
5:\(\mathit{altframe}\gets 0\)
6:\(\triangleright\) This is the current time
7:\(LATxSubCh\), \(LATxSubframe\), \(LARC\gets 0\)
8:while True do
9:if subframe \(==txSubframe\)then\(\triangleright\) It is time to transmit
10:\(txPacket(txSubCH)\)\(\triangleright\) Transmit packet on the specified subchannel(s)
11:if\(RC\
eq 0\)then
12:if\(RC==RC_{LA}\)then\(\triangleright\) Time for early reselection; do not commit just yet
13:\(LARC\gets random(C1,C2)\)\(\triangleright\) Set the length of the next streak
14:if\(random(0,1)<probResourceKeep\)then\(\triangleright\) Next streak will stay here
15:\(LATxSubframe\gets txSubframe+RRI\times(RC+1)\)
16:\(LATxSubCh\gets txSubCH\)\(\triangleright\) Inherit the current resource coordinate
17:else\(\triangleright\) Must move from current location
18: call select_resource_lookahead()\(\triangleright\) Select resource using lookaheads+SPS
19:endif
20:endif
21:\(txSubframe\gets txSubframe+RRI\)\(\triangleright\) Schedule next transmission in the current streak
22:\(RC\gets RC-1\)
23:else\(\triangleright\) Time to switch to next streak
24:\(RC\gets LARC\)\(\triangleright\) Next streak becomes current streak
25:if check_lookahead() then\(\triangleright\) Double-check if lookahead is still available
26:\(txSubframe\gets LATxSubframe\)
27:\(txSubCH\gets LATxSubCh\)\(\triangleright\) Commit to the lookahead
28:else\(\triangleright\) Resource map changed; lookahead occupied
29: call select_resource_lookahead()\(\triangleright\) Select resource using lookaheads+SPS
30:endif
31:endif
32:else
33: call sensing_update()\(\triangleright\) Keep sensing and update resource use map; Section 3.1
34:endif
35:\(\mathit{subframe}\gets\mathit{subframe}+1\)\(\triangleright\) Push time
36:endwhile
37:endprocedure
```  
**Algorithm 2** Reselection in SPS/LA  
Notice that the SPS/LA algorithm schedules the resource in first-come-first-serve (FCFS). The UE that first sends its lookahead takes priority, and those that internally decided for the same location as the lookahead should change their decision when they see the first advertised lookahead. Unless two UEs decide on the lookahead in the same subframe, the UE that computes the lookahead later cannot pick the resource at an already planned location by other UEs. Therefore, there is no special resolution procedure for the case multiple UEs plan for the same resource location. In the case that UEs in the same subframe (but different subchannels) plans for the same resource location, the current lookahead scheme cannot resolve the collision (Figure 4). One potential solution to this problem is a third node that can hear these two UEs tell in its packet about the imminent collision. However, the probability should be small, so we defer such a solution to a future work.
## 3 Algorithm Design  
### Vertex Protection Algorithm  
The satellite-to-Moon visibility consists of two aspects: the availability of the satellite signal is limited by the elevation angle (as deduced in Section 2.1), and the Earth may obscure the line of sight from the satellite to the Moon (as deduced in Section 2.2). The algorithm provided in Section 2.3 can calculate the maximum interpolation step while guaranteeing the fitting accuracy. As shown in Figure 3, under the low fitting accuracy requirement, when calculating whether the Earth obscures the line of sight between the satellite and the Moon, an intersection exists between the original visibility function curve and the threshold, but the fitted curve has no intersection with the threshold, which causes errors when calculating the Earth occultation period in one cycle (28 days). Such a problem is related to the threshold selection and fitting accuracy requirement.  
To avoid this problem, in this paper, the basic idea is to try to make the local extremal points of the multi-hump function the endpoints of the subinterval, where the local extremal points are the \"vertices\". This method not only avoids the visibility calculation error, but also has the advantage that it can directly determine whether there is a solution to the original function within that subinterval without constructing a cubic polynomial in each subinterval. The interpolation method must know the original function values of the two  
Figure 3: Conventional adaptive algorithm to fit the occlusion function.
# Latency Analysis of Blockchain-Based SSI Applications +
## 4 Evaluation Results
### Four-Node Measurements in a Public Cloud  
The detailed read and write latency measurements are provided in Table 1 (partly performed in our previous work [15]). We computed those measures for an Indy network  
Figure 1: Our proposed blockchain architecture based on Indy [15].  
Figure 3: Evaluation architecture in a private cloud.  
Figure 2: Evaluation architecture in the Google Cloud Platform.  
that consists of four nodes. We found that the size of the dataset stored on-chain does not affect the speed, as the ten measures we collected per scenario were very close to each other. However, we can see a proportional relationship between the TX arrival rate and the average latency for both read and write TXs. It is also worth noting that depending on the TX arrival rate, the average write latency fluctuates from 2.7 to 6.3 s per TX. On the other hand, the average read latency fluctuates from 0.088 to 1.56 s per TX, and also heavily depends on the TX arrival rate.
## 2 Materials and Methods
### Vulnerability Analysis of Coastal Flooding
#### 2.2.7 Coastal Factor/West Coastline Change Rate (Fc)  
Coastal management for a section of coastline (e.g., hard coastal defenses, sediment input to the beach, or controlled realignment) is a key aspect in determining the long-term response of the coast to the impacts of climate change, including sea-level rise. Coastal erosion and coastal flooding are related through high water levels and energetic wave conditions during storms. Coastal geomorphology plays a crucial role here, as different types of morphology exhibit varying vulnerabilities, in addition to providing the pathway for coastal inundation [36].  
The analysis of coastal evolution and morphological variations was carried out using GIS overlays, analyzing changes in the coastline over a 61-year period (1956-2017). This analysis is based on the overlay of aerial photographs from 1956 (coverage by American Flight--US Air Force) with a 1 m resolution and more recent orthophotopographs from 2004-2005 and 2017, which were obtained from the National Geographic Institute's National Aerial Orthophotography Plan (Figure 4). The positions of the coastlines were digitized and analyzed using the USGS-DSAS extension for ArcGIS v10.8, in terms of retreat or accretion rates (meters per year) during the mentioned periods (Figure 4). This tool allows for the analysis and quantification of changes in the coastline based on geospatial data and, in this case, is primarily used to study erosion and sediment accumulation in coastal areas [37].  
Figure 4: Analysis of coastal change. Vulnerability intervals: \(<\)\(-\)2.0, \(-\)2.0 to \(-\)1.0, \(-\)1.0 to 1.0, 1.0–2.0, and \(>\)2.0.  
High growth values (coastline moving seaward) indicate high vulnerability, while negative rates (coastline retreating inland) indicate low vulnerability, with the following intervals in meters per year: \(<\)\(-\)2 (value 1), \(-\)2 to \(-\)1 (value 2), \(-\)1 to 1 (value 3), 1-2 (value 4), and \(>\)2 (value 5) (Figure 4). In general, there are moderate values (between \(-\)1 and 1 m per year) for coastline retreat along the entire Ria of Arosa coast.
# A Technique of Recursive Reliability-Based Missing Data Imputation for Collaborative Filtering
###### Abstract
More specifically, we make the following contributions in this paper:  
* We propose an effective method for missing data imputation that improves the poor recommendation accuracy of CF caused by the data sparsity problem. The proposed method, k-RRI, first selects data with high reliability and then recursively imputes data with additional selection while gradually lowering the reliability criterion. Existing methods replaced the data with a small amount of real data at once; however, we replaced the data with real and reliable virtual data to alleviate the data sparsity problem. * We also propose a new similarity measure that weights common interests and indifferences between users and items. This enables us to determine the similarities between user and items more accurately. * We evaluated the performance of the proposed approach through experiments with state-of-the-art methods: EMDP and AutAI. The experimental results demonstrate that the proposed approach using a new similar measure significantly improves recommendation accuracy compared to those resulting from the state-of-the-art methods while demanding less computational complexity. The rest of this paper is organized as follows. Section 2 explains the preliminaries for our work. Section 3 discusses related studies that solved the data sparsity problem in CF. Section 4 describes the proposed method in detail. Section 5 presents the result of the performance evaluation. Section 6 summarizes and concludes the paper.
## 3 Proposed Schemes
### Delay Minimization Routing (DMR) Scheme
#### 3.1.2 Neighbour Identification and Path Setup  
This phase includes the DMR algorithm neighbours recognition and path establishment. Initially, the nodes are unaware about the information of physical distance and node IDs among the neighbours. For this purpose, every minor sink node broadcasts a hello packet that contains its ID and the time at which the hello bag is broadcasted. The nodes in vicinity of the minor sink nodes receive the hello packet and compute their physical distance from the time difference when they receive the hello bag and when it was initially transmitted by the sink node, assuming a constant speed of acoustic wave. After reception, the neighbor nodes broadcast the hello bag and put their IDs, time at which they broadcast the hello bag and the time it was originally broadcasted by the minor sink node. When other nodes receive this hello bag, they compute their physical distance from the minor sink node and also know about the physical distance of every node from which they receive the hello bag. This process goes on until all nodes have the information of their physical distance from the minor sink node and of their neighbor as well. The hello packet is shown in Figure 4. Minor sink nodes and other sensor nodes regularly share this information to keep updated about information of each other [25]. All the four minor sink nodes are differentiated from the ordinary sensor nodes by their unique IDs. This differentiation of the minor sink nodes is helpful in recognizing them by the sensor nodes when the sensor nodes have to advance packets.  
Figure 1: Network model.  
Figure 2: Minor sink \(S_{1}\) and \(S_{3}\) packet forwarding.
# Application of Areal Topography Parameters in Surface Characterization
## 1 Introduction and Terminological Framework
Atomic force microscopy (AFM), one of the most widely used scanning probe microscopy techniques, enables nanometer-resolution surface characterization and has become a standard method for acquiring areal topography data. Even when optical or stylus-based profilometry is used, AFM serves as a critical reference technique for verifying and complementing roughness measurements, especially when surface features approach the nanoscale. The use of areal topography parameters is particularly significant in the characterization of metals fabricated through additive manufacturing techniques such as selective laser melting (SLM) and laser powder bed fusion (LPBF) [20]. These processes often generate complex surface textures whose behavior cannot be adequately described using 2D metrics alone. Recent studies indicate that specific surface textures can be quantitatively characterized, revealing features closely related to process parameters such as laser inclination and energy input [21]. Furthermore, surface topography plays a crucial role in defining fundamental mechanical properties of components--such as friction and wear performance--which, in turn, have a direct impact on the operational efficiency and longevity of machine parts in industrial settings [22; 23]. The integration of surface characterization into broader materials processes reflects a multidimensional approach, enhancing our understanding of how surface attributes influence macroscopic performance. The cleaning, coating, and surface treatment of metallic components are essential in industries aiming to extend component lifespan and performance [24]. As surface metrology continues to evolve, the combination of areal topography parameters with scanning probe techniques remains central to the advancement of nanostructured materials and devices [25].
## 2 Fundamental Concepts
### Wiretap Channel Models
#### 2.3.5 Relay Wiretap Channels  
Cooperative relaying is one of the multi-antenna techniques that have been presented to improve the PLS by increasing the quality of channel capacity between the source node and the destination receiver. In a secure relay network, the source and a relay node cooperate to improve the system security while preventing eavesdroppers. The two commonly adopted examples of relaying protocols are the AF (amplify-and-forward) [56; 57; 58] and DF (decode-and-forward) [59; 60; 61] schemes. Examples of typical relaying networks are shown in Figures 6 and 7, which consist of a source ([PERSON]), an intended receiver ([PERSON]), the cooperative relay node and an eavesdropper ([PERSON]). The relay node functions in a DF mode.  
It is assumed that [PERSON], [PERSON], the relay node and [PERSON] are equipped each with \(\textbf{N}_{A},\ \textbf{N}_{B}\), \(\textbf{N}_{R}\) and \(\textbf{N}_{E}\) antennae, respectively. In the first case, [PERSON] sends the \(\textbf{N}_{A}\times 1\) information signal vector \(x_{s}\) to the relay node. Then, the received signals at [PERSON], the relay node and [PERSON], respectively, are given as [62]:  
\[y_{B(1)}=\textbf{H}_{AB}x_{s}+\textbf{n}_{B} \tag{9}\]  
\[y_{R}=\textbf{H}_{AR}x_{s}+\textbf{n}_{R} \tag{10}\]  
\[y_{E(1)}=\textbf{H}_{AE}x_{s}+\textbf{n}_{E} \tag{11}\]  
Figure 5: Secure communication using an interference channel model.  
Figure 6: A system model for a wiretap relay channel in the first time slot.  
Figure 7: A system model for a wiretap relay channel in the second time slot.  
where \(H_{AB}\in\mathbb{C}^{N_{B}\times N_{A}}\), \(H_{AR}\in\mathbb{C}^{N_{R}\times N_{A}}\) and \(H_{AE}\in\mathbb{C}^{N_{E}\times N_{A}}\) are the matrices from Alice to the [PERSON], the relay node and [PERSON], respectively. Obviously, \(n_{B}\), \(n_{R}\) and \(n_{E}\) denote the AWGN vectors at [PERSON], the relay node and [PERSON], respectively. The relay node decodes the original message signal and forwards it on to [PERSON].  
In the second case, the relay node transmits a new version of \(x_{s}\) with a weighting vector \(x_{r}\in\mathbb{C}^{N_{R}\times 1}\) of the encoded signal. Hence, the signals that are received at [PERSON] and Eve are described as:  
\[y_{B(2)} =H_{RB}x_{r}+n_{B} \tag{12}\] \[y_{E(2)} =H_{RE}x_{r}+n_{E} \tag{13}\]  
where \(H_{RB}\in\mathbb{C}^{N_{B}\times N_{R}}\) and \(H_{RE}\in\mathbb{C}^{N_{E}\times N_{R}}\) are the matrices from the relay node to Bob and Eve, respectively. Without loss of generality, we investigated the decode-and-forward (DF) cooperative channel model. Another type of cooperative relaying protocol is the AF channel scheme, which has a great impact on the capacity of physical layer secrecy. An extensive investigation of such an AF scheme for secure relay communication is presented in [63; 64].
# Identifying Influential Nodes of Complex Networks Based on Trust-Value
## 3 Experiments and Discussion
### Evaluation  
#### 3.2.1 SIR Spreading Model  
The node identification method should produce influential nodes related to the actual propagation process as much as possible. However, most complex networks lack the influence ability label of nodes. Thus, we employ a mature epidemic transmission model, the SIR [16] model, to evaluate the performance and effectiveness of TPR; the SIR model has also been used as a criterion in other relevant papers [30; 31]. There are three states in SIR: (1) susceptible: nodes in this state are infected by other nodes; (2) infected: the infected nodes infect other nodes with a probability of \(\alpha\) and return to the recovered state with the probability of \(\beta\); (3) recovered: recovered from infected status, does not have the ability to infect other nodes, and cannot be infected. In the experiment, one node is selected as a seed node, and then \(t\)-step infections occur. The experiment will iterate many times, and the average numbers of infected nodes and recovered nodes after infection are taken as the propagation ability of the node, denoted as \(F(t)\). The influence value \(K_{i}\) of node \(i\) defined as  
\[K_{i}=F(t)=\frac{1}{N_{ite}}(n_{I}+n_{R}), \tag{8}\]  
where \(N_{ite}\) is the number of iterations, and \(n_{I}\) and \(n_{R}\) represent the number of infected and recovered nodes, respectively. We set \(N_{ite}=100\). Due to the presence of randomness in the SIR model, the whole process needs to run many times, each time requiring multiple iterations. Thus applying the SIR model over the large-scale network is time-consuming [31; 32]. However we can still use it as a performance metric.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Network** & \(|V|\) & \(|E|\) & \(<k>\) & \(k_{max}\) & \(CC\) \\ \hline Kite & 10 & 18 & 3.6 & 6 & 0.578 \\ Karate & 34 & 78 & 4.5888 & 17 & 0.588 \\ Email & 1133 & 5451 & 9.622 & 71 & 0.254 \\ Euorroad & 1174 & 1417 & 2.414 & 10 & 0.02 \\ Power Grid & 4941 & 6594 & 2.669 & 19 & 0.107 \\ Protein & 3133 & 6149 & 3.925 & 129 & 0.106 \\ PDZBase & 212 & 242 & 2.283 & 21 & 0.013 \\ Propro & 1870 & 2277 & 2.356 & 56 & 0.153 \\ Pollbooks & 105 & 441 & 8.4 & 25 & 0.488 \\ Football & 115 & 613 & 10.661 & 12 & 0.403 \\ Dolphins & 62 & 159 & 5 & 12 & 0.259 \\ Hamster & 2426 & 16631 & 13.711 & 273 & 0.231 \\ Elegans & 453 & 4596 & 20.291 & 639 & 0.124 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Data sets. \(|V|\) and \(|E|\) indicate the number of nodes and edges, respectively; \(<k>\) and \(k_{max}\) represent the average degree and the maximum degree, respectively; and \(CC\) is the average clustering coefficient.
## 3 Proposed Methods
### Online Prediction
#### 3.4.2 Course Degree Prediction  
The goal of course degree prediction is to provide a reference for the courses of interest within the suggested course list. A learner might obtain a set of courses of interest generated by the course suggestion operation, but is confused when making a final choice. Hence, they can perform the course degree prediction to understand the estimated degree. Conceptually, the estimated degree can be viewed as the potential performance, which provides helpful information for making a final course decision. Algorithm 2 depicts the algorithm for course degree prediction. In this algorithm, the top \(k\) relevant users have to be determined first using the user similarity matrix \(SM\). Note, this step can be completed efficiently because the user similarities have been calculated offline. Next, the degrees can be computed in Steps 2 to 8, which are defined in Definition 3. Finally, the resulting degree is returned to the active user.  
```
Input: A user-similarity matrix \(SM\), a user-to-course degree matrix \(DM\), an active user \(a\), an unknown course \(c\); Output: The degree of \(c\); Algorithm: Degree_Prediction()
1. determine the top \(k\) relevant users from the \(SM\);
2. for each relevant user \(u\)do
3. retrieve the referred degree \(d_{u,\,c}\) of \(c\) from \(DM\);
4. retrieve the referred similarity \(s_{u,\,a}\) between \(u\) and \(a\) from \(SM\);
5. \(sum_{d}=sum_{d}+(d_{u,\,c}\,^{*}s_{u,\,a})\);
6. \(sum_{s}=sum_{s}+s_{u,\,a}\);
7. endfor
8. \(pd_{c}=sum_{d}/sum_{s}\);
9. return\(pd_{c}\).
```  
**Algorithm 2**Course degree prediction.  
**Definition 3**.: _Given a user similarity matrix \(SM_{u\,\,\rightarrow\,u}\), a user-to-course degree matrix \(DM_{u\,\rightarrow\,c}\), and an active user \(a\), assume that the system retrieves the most \(k\) relevant users for the active user \(a\). The degree of an unknown course \(c\) for user \(a\) is defined as_  
\[pd_{c}=\frac{\sum_{0<i\leq k}d_{i,c}\times s_{i,a}}{\sum_{0<i\leq k}s_{i,a}} \tag{2}\]  
where \(d_{i,\,c}\) indicates the degree of course \(c\) for user \(i\) and \(s_{i,\,a}\) indicates the similarity between user \(i\) and active user \(a\).  
Table 2 provides an example of a user-to-course degree matrix and Table 3 provides an example of similarities between user 5 and all users. Based on 2, the three most relevant users are users 1, 3, and 4, where \(k=3\). Because the degree of course 2 for user 2 is unknown, user 2 is excluded from the relevant user set in this example. Assume the degree to predict is course 2 for user 5. From Tables 2 and 3, the related degree is \((3\times 0.75+3\times 0.75+3\times 0.5)/(0.75+0.75+0.5)=3\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **User1** & **User2** & **User3** & **User4** & **User5** \\ \hline user5 & 0.75 & 0.75 & 0.75 & 0.5 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Example of similarities between user 5 and all users.