esa-sceva/satcom-chunk-collection huggingface dataset:
# A Tool for Pre-Operational Daily Mapping of Floods and Permanent Water Using Sentinel-1 Data
## 3 Methods
### The Floodwater/Permanent Water Mapping Algorithm
#### 3.2.2 The Buffer-from-Edge (BFE) Approach to Map Open Water
Figure 9: Histogram of the \(\sigma^{0}\) values of the subpopulations composed of the pixels belonging to the buffer zone included in the MCDWA (cyan dashed lines) and the buffer zone included neither in the MCDWA, nor in the CDWA (green dashed lines). Solid lines represent the Gaussian functions fitting the histograms. The EMSR359 case study is considered. The set of pixels selected through Equations (1) and (2) is denoted as the thresholding-derived water area (TDWA). The BFE approach combines the original CDWA and the TDWA to obtain the map of open water based on the following scheme:  
* pixels belonging to both the CDWA and the TDWA are classified as open water;
* pixels belonging only to the TDWA are classified as open water if they are located in a buffer zone of buffering distance 1 km created around the water area determined according to the previous point.
# Operator and Graph Theoretic Techniques for Distinguishing Quantum States via One-Way LOCC
## 3 Operator Structures and One-Way LOCC
: _Let \(\{U_{i}\}\) be a set of operators on \(\mathbb{C}^{d}\) and suppose the operator system \(\mathfrak{S}_{0}=\operatorname{span}\{U_{i}^{*}U_{j},I\}_{i/j}\) is closed under multiplication and hence is a \(C^{*}\)-algebra. Then, \(\mathcal{S}=\{(I\otimes U_{i})|\Phi\rangle\}\) is distinguishable by one-way LOCC if and only if \(\mathfrak{S}_{0}\) has a separating vector._  
The proof of the theorem starts with the observation that if \(\mathfrak{S}_{0}\) has a separating vector \(|\psi\rangle\), then the states \(\{U_{i}|\psi\rangle\}\) are linearly independent and [PERSON] can use this fact together with [PERSON]'s outcome to distinguish the states. As a straightforward application of the theorem, consider the following class of states. We recall that a set of matrices \(\{U_{k}\}\) have a _simultaneous Schmidt decomposition_ if there are unitary matrices \(V\) and \(W\) and complex diagonal matrices \(\{D_{k}\}\) such that for each \(k\), \(U_{k}=VD_{k}W\). **Corollary 1**. : _Any set of orthonormal states \(\{(I\otimes U_{i})|\Phi\rangle\}_{i=1}^{n}\), for which the matrices \(U_{i}\) have a simultaneous Schmidt decomposition, are distinguishable by one-way LOCC._  
The basic idea of the proof in this case is to note that the operator system structure satisfies \(\mathfrak{S}_{0}=\operatorname{span}\{U_{i}^{*}U_{j},I\}_{i\
eq j}=W^{*} \mathfrak{A}_{\Delta}W\) for some unitary \(W\). Since \(W^{*}\mathfrak{A}_{\Delta}W\cong\mathbb{C}^{d}\) has a separating vector, Theorem 3 applies. We note that the operator system \(\mathfrak{S}_{0}\) was studied in a similar context in [17]. **Remark 1**. : _Towards further applications, including those discussed below, note how the theorem gives a road map to generate sets of indistinguishable states based on these operator structures. Given the decomposition of the algebra generated by an operator system \(\mathfrak{A}=\operatorname{Alg}\left(\mathfrak{S}_{0}\right)\cong\oplus_{k} (M_{m_{k}}(\mathbb{C})\otimes I_{n_{k}})\), then \(\mathfrak{A}\) has a separating vector if and only if \(n_{k}\geq m_{k}\) for all \(k\). Thus, to find instances of sets of indistinguishable states, we can look for sets \(\{U_{i}\}\) such that \(\mathfrak{S}_{0}=\mathfrak{A}\) and \(m_{k}>n_{k}\) for some \(k\). Hence, we are led to consider sets of operators \(\{U_{i}\}\), such that the set is closed under multiplication, taking adjoints, and taking inverses (up to scalar multiples)._
## 4 Non-Ensemble-Based Methods
### Gradient Boosting Algorithm  
The parameters of the boosting-based model [65] are sub-categorized into three types, namely parameters specific to a tree structure, boosting specific parameters, and miscellaneous parameters. Since a decision tree is used as a default base learner, parameters specific to the tree structure are allied with each base learner, for example, the sample (minimum number) is requisite to split an interior node or the '\(max_{depth}\)' for each tree. '\(Learning_{rate}\)' and '\(number\)-\(of\)-\(tree\)' are the boosting parameters, directly related to the underlying boosting algorithm.
# Adap-UIL: A Multi-Feature-Aware User Identity Linkage Framework Based on an Adaptive Graph Walk
## 4 Adaptive Graph Walk Based on Restarted Affinity Coefficients
### Adaptive Graph Walk Based on RAC  
The original graph is converted to a weighted graph based on RAC with edge weights between any two nodes in the graph:  
\[P_{\alpha}(j\mid i)=m_{i,j}\qquad(\alpha\
eq 0) \tag{13}\]  
where \(m_{i,j}\) is the RAC between node \(i\) and \(j\), that is, the final probability after convergence.  
The weighted graph walk based on RAC can be free from the constraints of the original neighbor matrix, but adaptively selects the next node according to the restart affinity coefficients, and each step statistically aggregates infinite-step transitions in the statistical sense, hence referred to as the Adaptive Graph Walk in this paper. The restart coefficient \(\alpha\) is able to adjust the strength of capturing neighborhood features during the walk process, and the Adaptive Graph Walk based on RAC can also capture higher-order features between nodes more directly. Figure 3 shows the differences among random walk, RWR, and Adaptive Walk, in which RWR strengthens the local features of node 1, while the Adaptive Graph Walk makes it possible to reach node 5 from node 1 even though they have no direct edge, such as node 5 and node 3. By making two otherwise distant nodes directly reachable with some probability, the Adaptive Graph Walk strengthens the higher-order features.  
The Adaptive Graph Walk algorithm described in Algorithm 1 consists of three main steps:  
1. Compute the RAC matrix \(M\) using the adjacency matrix \(A\) and restart coefficient \(\alpha\).
2. Traverse the graph using \(M\) as the transition probability matrix, where each node serves as a starting point to generate 50-node sequences.
3. Output the generated node sequences.  
```
1:Graph \(G=(V,E)\)
2:adjacency matrix \(A\)
3:restart coefficient \(\alpha\
eq 0\)
4:walk length \(L=50\) nodes
5:number of walks \(N=50\)
6:Node sequence set \(\mathcal{S}\)
7:Define affinity matrix \(M\)
8:Compute RAC matrix \(M\) from adjacency matrix \(A\) and \(\alpha\)\(\triangleright\) Step 1
9:for each node \(v\in V\)do\(\triangleright\) Step 2
10:for\(n=1\) to \(N\)do
11: Initialize current node \(u\gets v\)
12: Initialize walk sequence \(s\leftarrow[u]\)
13:for\(l=1\) to \(L-1\)do
14: Sample next node \(u^{\prime}\) from transition probabilities in \(M\) at node \(u\)
15: Append \(u^{\prime}\) to \(s\)
16:\(u\gets u^{\prime}\)
17:endfor
18: Add \(s\) to \(\mathcal{S}\)
19:endfor
20:Output \(\mathcal{S}\)\(\triangleright\) Step 3
```  
**Algorithm 1** Adaptive Graph Walk with Restart Coefficient \(\alpha\)
# Resilience in the Internet of Medical Things: A Review and Case Study
## 3 Background
### Network Resilience  
There are many definitions of resilience given in the literature; however, in the context of networks, ref. [10] provides one of the appropriate definitions as follows: \"Resilience is the property of any network that preserves the availability, reliability, security, and integrity of a network by the time it encounters faults and failure\".  
In the perspective of IoMT, the requirements of resilience are specifically as follows:  
1. There should be continuous monitoring of all the nodes within all existing layers [11]. Any incident or mishap should be observable in real-time.
2. The data traffic must be redirected and regulated after a failure occurs [12].
3. There should be a quick recovery after any failure [13].
4. Causes of failure and faults should be detected and diagnosed quickly.  
Here, some key technologies are elaborated upon which are used to make the IoMT network resilient in different solutions suggested by various authors.
# Real-Time Monitoring of Wind Turbine Bearing Using Simple Neural Network on Raspberry Pi
## 3 Experiment
### Experimental Results
#### 3.3.2 Speed  
In this part, the tested times are shown in Figure 8. From the five comparison methods, the fastest was the Narrow Neural Network, whose testing time was 0.72 ms. The slowest was the Wide Neural Network, whose testing time was 3.67 ms. For the proposed method, the testing time achieved was 0.06 ms.  
Figure 5: Data spectrum.  
For the compared methods, they had a similar accuracy at around 99.0%. However, the testing time of the Wide Neural Network was greater than that of the other methods at 3.67 ms. The main reason for this is that this network has a wide layer. The fastest comparison method was the Narrow Neural Network because of its narrow layer. The average accuracy of the proposed method was 99.8% and the testing time was within 0.06 ms under 5-fold cross-validation. Compared with the other methods, these values represent improvements of 0.8% and 0.66 ms, respectively. It is obvious from the Figure 8 that the proposed method had a better performance than the other methods.  
Figure 6: Comparison of accuracy of raw data.  
Figure 7: The loss curve of changes with training progress.
# Backscatter Assisted NOMA-PLNC Based Wireless Networks
### Motivation  
Although the optimal power allocation in NOMA-based wireless network will improve the sum capacity, the quality of the far user is still not healthy due to the poor signal strength. By considering this issue, the concept of ambient backscattering (ABS) [19] is applied in this paper which help to improve the signal strength of the far user. ABS is the technique which generally used in the low power device communication, and it operates without any specific RF transmitters however, it uses the existing ambient radio frequency (RF) signals for transmission. For instance, ambient backscatter transmitter (example, tag) can communicate with backscatter receiver (example, reader) using surrounding ambient RF sources examples TV towers, FM towers, cellular base stations and Wireless Fidelity (Wi-Fi) access points etc. [19]. Outage probability of an ambient backscatter system with pair of tag and reader is analyzed in [20]. Backscatter communication network model is proposed using WiFi, hence backscatter tag can be read by WiFi devices [21]. A wireless protocol is proposed using large scale backscatter devices and addressed timing, frequency synchronization and near-far problem [22]. A versatile MIMO backscatter is proposed to leverage the diversity features of MIMO, therefore it dramatically decreases bit error rate (BER) in [23]. A OFDM backscatter system is proposed, and it uses a sample-level  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**S.No.** & **Parameters** & **Uplink OMA and Downlink NOMA with PLNC** & **Uplink OMA and Downlink NOMA with PLNC** \\ \hline
1. & No of nodes & 4 & 4 \\ \hline
2. & Time slots & 5 & 3 \\ \hline
3. & SIC operation & 3 & 1 \\ \hline
4. & Complexity & high & low \\ \hline
5. & nodes connectivity & 2 for 3 time slots & 4 for 3 time slots \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of NOMA and NOMA with PLNC.  
modulation to avoid the phase offset created by a tag and hence improves the throughput in [24]. Similarly, LTE backscatter system is developed to leverage the continuous LTE ambient traffic for ubiquitous, high throughput and low latency in [25]. Recently, a few works have been proposed incorporating NOMA and backscattering. In [26], closed form expressions for the outage probability of the backscatter-NOMA is presented and studied how the cellular systems and IoT networks are working together. Outage performance, expected rates, and diversity-multiplexing tradeoff (DMT) of the backscatter cooperation NOMA is studied in [27]. Hybrid time division multiple access (TDMA)-based power domain NOMA is proposed in IoT sensors network in the presence of Backscatterer [28]. Similarly, the ergodic capacity and outage performance of the bidirectional NOMA-SWIPT enabled IoT wireless network is analyzed in [29]. In this paper, a novel 5G wireless network model is proposed using NOMA and PLNC schemes with ABS. It is assumed that the PLNC scheme is used in the uplink and the NOMA scheme used in the downlink.
# A Hybrid PAPR Reduction Method Based on SLM and Multi-Data Block PTS for FBMC/OQAM Systems
###### Abstract  
The filter bank multicarrier employing offset quadrature amplitude modulation (FBMC/OQAM) is a candidate transmission scheme for 5G wireless communication systems. However, it has a high peak-to-average power ratio (PAPR). Due to the nature of overlapped signal structure of FBMC/OQAM, conventional PAPR reduction schemes cannot work effectively. A hybrid PAPR reduction scheme based on selective mapping (SLM) and multi data block partial transmit sequence (M-PTS) methods is proposed for FBMC/OQAM signals in this paper. Different from the simple SLM-PTS method, the proposed hybrid algorithm takes into account the overlapping effect of multiple adjacent data blocks on its PTS process. From simulation results, it can be obtained that the proposed method can offer a significant PAPR reduction performance improvement compared with the SLM, PTS and SLM-PTS methods. The proposed method can effectively reduce the PAPR in FBMC/OQAM systems.  
PAPR; FBMC/OQAM; hybrid; SLM; PTS Article  
A Hybrid PAPR Reduction Method Based on SLM and Multi-Data Block PTS for FBMC/OQAM Systems
# Effect of Heat Input on LMHMW Joint of Carbon Steel
###### Abstract  
Laser-MIG hybrid multi-layer welding (LMHMW) technology has been employed in paraxial configuration with laser leading for the welding of 20 mm thick Q235 carbon steel plates to exploit the hybridization effect that addresses the shortcomings of the individual process as well as to compliment their merits. The bilateral effects of arc augmented laser welding have resulted in complete joint penetration, process efficiency, stability and gap bridge ability. Samples welded under varying heat inputs in multiple passes have been analyzed for their microstructure evaluation using an optical microscope followed by tensile and Vickers hardness testing in various regions of the weld zones. This process was conducted to characterize the effect of heat input on the mechanical properties of the welded joints. The experimental results illustrate that different heat inputs have significant effects on the microstructure, heat affected zone width and mechanical properties of welded joints. The microhardness near the fusion line decreases dramatically due to the influence of the phase transformation process, and the highest microhardness value is obtained in the center of the weld seam. By using reasonable process parameters, the strength of the welded joint can obtain 458.5 MPa.  
laser-MIG hybrid multi-layer welding; Q235 steel; heat input; microstructure; mechanical property +
Footnote †: journal: Applied
## 2 Background
Offline learning hyper-heuristics derive knowledge from a training set of problem instances, aiming to generalize this knowledge to unseen instances. Online learning hyper-heuristics, in contrast, learn continuously during the problem-solving process, adapting to the specific characteristics of the current instance. This online learning capability is particularly valuable in dynamic environments. It is crucial to distinguish between the hyper-heuristic methodologies and the specific problem domains they are applied to. While a hyper-heuristic can be designed to operate across various problems (e.g., scheduling, routing, packing), its underlying high-level heuristic for selecting or generating low-level heuristics remains independent of the problem instance. For example, a selection hyper-heuristic might employ a reinforcement learning strategy as the high-level heuristic to choose between different low-level heuristics. This reinforcement learning strategy is the core of the hyper-heuristic and is distinct from the specific scheduling or routing problem tackled by the low-level heuristics. Hyper-heuristics provide a general framework for managing and applying low-level heuristics that often deal with the problem domain, giving context and evaluation criteria. This separation allows for the development of versatile hyper-heuristics that can be readily adapted to new and diverse optimization contexts. Therefore, when analyzing hyper-heuristics research, it is essential to consider the performance of both high-level and low-level heuristics when applied to specific problem domains. This distinction allows for a more nuanced understanding of the strengths and weaknesses of different hyper-heuristic approaches. The crucial distinction between metaheuristics and hyper-heuristics lies in their level of operation. Metaheuristics manipulate solutions directly, while hyper-heuristics operate on a higher plane, managing and selecting heuristics. This higher level of abstraction empowers  
Figure 1: A classification of hyper-heuristic approaches according to [8]. hyper-heuristics with greater flexibility and adaptability across diverse problem domains, including vehicle routing, scheduling, and combinatorial optimization. Furthermore, the integration of machine learning with hyper-heuristics opens exciting new avenues for creating truly intelligent and adaptive optimization systems. This synergy is driving the current wave of innovation in the field and promises to deliver increasingly powerful and versatile optimization tools for the future.
# Biomethane Potential Test: Influence of Inoculum and the Digestion System
## 3 Results and Discussion
### Impact on Specific Methane Yield and Methane Percentage
#### 3.2.3 Effect of the Degassing  
The effect of degassing on specific methane yield and methane percentage can be seen by the differences between inocula I-WWP7 and I-WWP14 (Figure 3). I-WWP7 and I-WWP14 were statistically significantly different for all substrates except S-MCC. I-WWP7 had a 7 to 15 LCH4 kg\({}^{-1}\)\({}_{\text{{\tiny{ODM}}}}\) higher methane yield (CV 0.3%-4.8%) compare to I-WWP14. These results differ from literature, where no effect of the degassing time was shown [12; 14]. One reason might be that within I-WWP7 a smaller amount of substrate was used for the growth of the microorganisms, because of higher activity of microbiological populations by shorter degassing time. A longer degassing time resulted in a higher abundance of members of the phylum _Firmicutes_, which degrade complex material. This resulted in a higher methane yield of substrates with high fiber content like S-MCC and S-HAY. The German protocol VDI 4630 recommends pre-incubation for at least seven days. Based on our results a longer pre-incubation time should be recommended.
## 4 Experiment
### Benchmark Set  
Our methods are proposed to enhance the performance by overcoming problems in dynamic situations. To see the contributions of our methods, we construct a robot environment dataset and evaluate the performance according to the MOT16 [22] benchmark method. A Turtlebot v2 is used for mobility and the recording camera is a Galaxy S8 + 12 MP mounted on the robot. The video is taken following the scenario where the robot interacts with the user or patrols inside the building, and the ground truths are made by labeling humans' bounding boxes with a handcraft according to the manner of the construction of the MOT16 benchmark set. Table 1 lists the details of our benchmark sets.  
The Interaction-1 and Interaction-2 benchmark sets are based on the robotic guidance scenarios. The guidance robot interacts with the person and moves to its service destination. During the process, the users follow the robot with nonlinear movements that are difficult to predict. Interaction-1 and Interaction-2 include a small number of people and a relatively large number of people, respectively.  
The Patrol benchmark set is based on a robotic patrol scenario. If the robot has no current purpose in progress, it patrols the lobby until a new command is received from the user. A large number of people appear in the scenes, and the robot repeats the actions of getting close to and away from the people to patrol.  
Our dataset contains more dynamic scenes than the MOT16 dataset because it is based on images taken from a robot in service. To validate the degree of dynamic movements, we quantify them with Laplacian variance, taking advantage of the fact that a dynamic movement inevitably produces motion blur. Figure 8 represents the Laplacian variances in our dataset in comparison with the MOT16 datasets.  
All three datasets used in our experiments are found to show lower Laplacian variances compared to the MOT16 datasets. This indicates that the edge detection by the Laplacian kernel is difficult due to the dynamic movements.  
We are concerned about whether the reason for the low Laplacian variances is the camera characteristics or not. To address this concern, we additionally constructed an image set, Robot-Stop, which observes moving people in the stationary condition of the robot. The experimental results demonstrate that the low Laplacian variances of our datasets are not given by the camera characteristics because the second highest result was observed amongst all the datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Interaction-1** & **Interaction-2** & **Patrol** \\ \hline Frame & 2700 & 1242 & 2868 \\ Bounding box & 5814 & 6201 & 9913 \\ ID & 38 & 68 & 133 \\ Explanation & destination & destination & patrol \\  & guidance 1 & guidance 2 & \\ FPS & 30 & 30 & 30 \\ Image size & 1920 \(\times\) 1080 & 1920 \(\times\) 1080 & 1920 \(\times\) 1080 \\ Length(sec) & 90 & 41.4 & 95.6 \\ Crowd & low & middle & high \\ \hline \hline \end{tabular}
\end{table}
Table 1: The details of benchmark sets built on three different scenarios.
## 3 Results and Discussion  
The first objective of this work was to rank the CIs of the vibration signal in the time domain using the proposed methodology. In order to fulfil this objective, the process detailed in Section 2.2.3 was carried out. By developing the procedure detailed in phase 1, the \(\lambda\)-value mtry = 11 was determined for the RF classifier. The top 10 CIs were selected because the variability in classification accuracy reduced for the four types of failures, as detailed in Figure 6. Subsequently, after assigning the weighting, Table 1 was obtained for breaking (B), cracking (C), pitting (P), and scuffing (S) failures. Figure 6: Classification accuracy and standard deviation (SD) by number of CIs and failure. (**a**) Breaking (B). (**b**) Cracking (C). (**c**) Pitting (P). (**d**) Scuffing (S). In order to determine the CIs weighting by failure types, phase 2 was carried out, and the results presented in Table 2 were obtained. The total weighting and count per failure types in the CIs exposed in phase 3 are detailed in Table 3. The top 7 CIs were selected because they have the highest weighting and are present in all failure types. This procedure reduces the dimensionality of the DBs. The CIs selected in the ranking were Temporal moments higher order (TMHO), Mean, Skewness, Zero crossing, Slope sign change (SSC), Energy operator and Kurtosis. The calculation equations of the CIs are detailed in Table 4. With the selected CIs, new DBs with 900 observations and these 7 CIs were constructed. These DBs were used to compute the accuracy and AUC metrics for the RF and k-nn algorithms.
## 6 Frequency Domain Optimization  
This section begins with a discussion about how the lidar measurement quality metrics introduced in Section 5.1 can be directly computed via frequency domain calculations using a frequency domain wind field model. Next, the frequency domain calculation procedures are used to optimize the parameters for the scan patterns presented in Section 5.2 for each measurement quality metric.