esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Association Scheme for Handling Node Mobility in Cluster-Tree Wireless Sensor Networks
## 4 Simulation Assessment
### Results and Discussion
#### 4.2.1 Mobile Nodes at 1.4 m/s
It is important to remark that this metric can be affected by several factors, such as: wireless channel access mechanism, number of contention nodes, number of hops to sink node, and the average size of the queues (buffer size). Besides, for this work, all clusters have the same allocated active periods, i.e., it is not used any mechanism to proportionally allocate superframe duration size for CHs, as recently proposed in [35]. Figure 8: Average percentage of disconnect time at 1.4 m/s. As it can be observed in Figure 9, when using the proposed anticipation mechanism with a few active mobile nodes, the average end-to-end communication delay was very similar to the IEEE 802.15.4 standard mechanism. As the number of mobile nodes increased, it also increased this communication delay. In what concerns the IEEE 802.15.4 standard, the end-to-end communication delay seems to remain more or less constant up to 25 mobile nodes. Nevertheless, it cannot be underestimated the fact that, for a number of mobile nodes larger than 10, most parts of these nodes became disconnected from the network (Figure 8) and the percentage of packets that are lost is larger than 35% (Figure 7). Therefore, as only packets that are successfully delivered to the PAN coordinator are considered for the end-to-end delay average evaluation, the standard end-to-end delay, although lower, must be interpreted together with the packet delivery rate. In fact, as the proposed anticipation mechanism guarantees a higher packet delivery rate (Figure 7), there is a higher number of data packets travelling in the cluster-tree network towards the PAN coordinator. Considering that this work does not implement any optimized superframe duration allocation mechanism, data messages may experience greater end-to-end communication delays mainly due to the delay of the buffer queues and network congestion.
# Evaluation of Novel Preformed Particle Gel System for Conformance Control in Mature Oil Reservoirs
## 2 Results and Discussion
### Swelling Behaviour and Rheology
#### 2.3.1 Effect of Salinity  
The effect of water salinity on the swelling kinetics and PPG strength is shown in Figures 5 and 6. The gel sample prepared in distilled water is the fastest to reach the equilibrium swelling ratio, which is also the highest. Conversely, the gel prepared in seawater has the lowest equilibrium swelling ratio. For the first 10 min, the gel sample  
Figure 4: SEM images of (**a**) CMC, (**b**) dry CMC/PAMBA, and (**c**,**d**) swollen CMC/PAMBA in brine.  
prepared in distilled water has the fastest swelling rate, while the other samples have close swelling rate and ratio values. This phenomenon is attributed to the concentrated effect of Na\({}^{+}\), Ca\({}^{2+}\), and Mg\({}^{2+}\) in brine solutions. The presence of these ions reduces the osmotic pressure difference, which serves as the driving force for swelling between the PPG and the solution. Additionally, those cations can engage in ionic interactions with the -COO\({}^{-}\) groups present in the PPG structure. This interaction results in a shielding effect, where the cations replace the polymer-water interaction with a polymer-salt interaction. Consequently, this alteration diminishes the swelling ratio of the material. The combined influence of the osmotic pressure and shielding effect leads to a substantial reduction in water uptake by CMC/PAMBA when exposed to brine solutions, highlighting the intricate interplay between the material structure and the surrounding salt environment.  
Figure 5: Salinity effect on swelling kinetics.  
Figure 6: Salinity effect on gel strength.  
Rheology measurements were conducted to assess gel strength. Strength is a deterministic factor for the plugging efficiency of the gel, as weak gels are more prone to deformation under lower pressure gradients, potentially creating channels for post-water flow. Gels prepared with lower salt content displayed lower storage moduli compared to gel prepared in seawater. In general, increasing the swelling will reduce the gel strength.
## 4 Time Continuous Compactness Based _Rwta_
### Relative-Loss of Time Continuous Compactness (ReLoss-TCC)  
Wavelength-timeslot allocation on a path in QKD optical network will have a great impact on resource connectivity of this path as well as other paths that share links with it. Therefore, when evaluating the influence of RWTA schemes on network resource status, not  
Figure 4: (a) Time continuous compactness (TCC) on wavelength \(w1\); (b) TCC on wavelength \(w2\).  
only the TCC on the path but also the TCC on other paths sharing links with this path need to be considered. In QKD optical networks, in order to save costs, the quantum devices such as quantum signal generators, quantum measurement devices, etc., are deployed only at nodes that have security requirements, and the routes for services are determined in advance. This means that static routing scheme is suitable for the RWTA process. Suppose \(P(p)\) denotes the set of paths that share links with \(p\), then the TCC of \(P(p)\) in \(\varphi\) state is shown as follows.  
\[C_{\varphi}^{P(p)}=\ \sum_{p\in P(p)}C_{\varphi}^{p} \tag{3}\]  
We jointly consider \(C_{\varphi}^{p}\) and \(C_{\varphi}^{P(p)}\) to evaluate the influence of specific RWTA scheme on the connectivity of network resources in time dimension, and propose a relative-loss of time continuous compactness (ReLoss-TCC)-based RWTA strategy. ReLoss-TCC is used to describe the relative changes of TCC after wavelength-timeslot allocation, and the definition is shown in Equation (4). Here, \(\varphi\) denotes the network state before wavelength-timeslot allocation, while \(\varphi^{\prime}\) denotes the network state after wavelength-timeslot allocation.  
\[\textit{ReLoss-TCC}\frac{\left(C_{\varphi}^{P(p)}\ -\ C_{\varphi^{\prime}}^{P(p)} \right)\ +\ \left(C_{\varphi}^{P}\ -\ C_{\varphi^{\prime}}^{P}\right)}{C_{\varphi}^{P(p)}\ +\ C_{\varphi}^{P}}, \tag{4}\]
## 5 Conclusions  
This paper proposes a motion planning method to allow a bipedal robot to perform a jumping maneuver. It proves the possibility for bipedal robot driven by electric motors to perform a high jump. Using a simplified model without any elastic elements, we greatly increased the jumping height by commanding every motor as much as possible to yield the profile of the torque speed curve, which reflects the obstacle negotiation capability. During the push-off phase, linear programming is used to fine-tune the joint torques to ensure that the torque and ZMP constraints are satisfied. Finally, in simulations, the robot successfully jumped 0.477 (m) high, and the COM is increased by 0.83 (m). However, some limitations should be noted.  
* In this paper, the efficiency of harmonic reducer is not considered. Thus, we have to calibrate the torque speed curve of the motor on a professional motor test platform, with and without reducer.
* While we can obtain an increased jump height based on the premise of the torque and ZMP constraints, how to reduce the impact force and maintain landing equilibrium during the landing phase remains a significant problem.
* We only found that the jump direction \(\theta\) and the initial ankle angle \(q_{10}\) have an almost linear relationship. However, this relationship alone is not sufficiently reliable to specify jumping direction.  
Figure 16: Screenshot of the simulation.  
Figure 15: (**a**) shows the torque of each joint in the simulation; and (**b**) represents relationships between the motor torque and speed in the simulation.  
In the future, we will take the efficiency of harmonic reducer, flying and landing phase of jump into consideration. Future work should include development of a sequential jumping motion and a running motion to provide further improvements in the obstacle negotiation abilities of the bipedal robot. It is also necessary to design experiments to validate the theory in the future.  
This work was supported in part by the National Natural Science Foundation of China under Grant 91748202, Grant 61533004, and Grant 61703043, and in part by the Basic Research Program under Grant B132011 xx.  
[PERSON] conceived of the presented idea. [PERSON] and [PERSON] developed the theoretical formalism. [PERSON], [PERSON] and [PERSON] designed the simulation experiments and analyzed the data. [PERSON], [PERSON] and [PERSON] discussed the results and wrote the paper.  
The authors declare no conflict of interest.
# The Integration of AI and Metaverse in Education: A Systematic Literature Review
## 4 Results and Discussion
### Rq3: What AI Techniques Are Utilized Within the Metaverse for Education?
Q-learning is a reinforcement learning approach and appeared in two papers, where it was suggested as useful for developing adaptive learning paths. Recurrent neural networks (RNNs) were cited in five papers, and are suitable for sequential prediction tasks that are relevant to educational analytics. Fuzzy logic was documented in two papers, and aids decision-making in uncertain contexts, potentially benefiting educational assessment. Computer vision was presented in five papers, indicating significant engagement with visual data for interactive learning. Support vector machines (SVM) were mentioned in one paper for classification purposes. Linear regression appeared in one paper, where it was discussed as useful for predicting educational outcomes based on various inputs. Deep Q network was not referenced in any papers, reflecting the limited exploration of advanced deep reinforcement learning techniques in education to date. Random forests were similarly not found in the reviewed papers, indicating a potential gap in the research in the area of ensemble learning methods. Hierarchical clustering was also absent, which may suggest there is a preference for simpler clustering techniques in the literature. The gap between the number of papers discussing algorithms and the total number of source papers indicates that general mentions of techniques exceed specific algorithm applications. Consequently, the ranking of techniques can be updated as follows: deep learning (19 mentions) leads, followed by unsupervised learning (11 mentions), supervised learning (10 mentions), natural language processing (NLP) (5 mentions), computer vision (5 mentions), and finally, reinforcement learning (2 mentions) (see Figure 7). Figure 6: AI algorithms integrated in the metaverse for education. Figure 7: AI techniques used in the metaverse for education, focusing on related algorithms. Overall, this analysis illustrates a landscape in which deep learning and its related algorithms dominate AI applications for education within the metaverse, as driven by the demand for interactivity, adaptability, and user engagement. Table 5 summarizes the AI algorithms and their proposals as previously identified in the eligible papers. In response to Question 3, \"What AI techniques are utilized within the metaverse for education?\", this study provides a scientific overview and classification. The findings, highlighted in Figures 5-7, clarify that most of the reviewed papers concentrate on unsupervised learning, reinforcement learning, and deep learning.
# CAD-Based Feature Recognition for Process Monitoring Planning in Assembly
## 2 Literature Review
### Features in Process Planning  
Several approaches used to improve the efficiency of decision support systems for process planning use features, both in manufacturing and assembly [22; 23; 24]. Features can be extracted from CAD models and describe assembly knowledge for planning processes. Product features are divided into low-level features (shape features) and high-level features (application-specific features, e.g., assembly features such as joining surfaces) [25]. Low-level features describe geometric and topological units of parts and shapes such as holes, chamfers, notches, and slots. High-level features describe application-specific features in terms of functionality and use. An assembly feature, as a high-level feature, can be the connection between two shape features of different parts in an assembly group. Different definitions of high-level or assembly features can be found [21; 26; 27]. These definitions differ in depth and application. In general, a high-level feature is significant to the process. In assembly applications, this can be specified by describing the geometric, topological, and process parameters. The study in [27] describes joining features as representing the entities involved in the connection, the joining method, constraints, and geometric shapes (e.g., groove, chamfer). Features of the joining path, tolerances, and gripping positions also belong to this category [20]. In this paper, assembly features relevant to process monitoring are defined as assembly knowledge. Thus, geometric, topological, functional, and process-relevant features are included in this category.
# An Adaptive Moving Window Kriging Based on K-Means Clustering for Spatial Interpolation
## 2 Theoretical Background
### K-Means Clustering  
K-means clustering is an unsupervised machine learning algorithm used for grouping data points into clusters based on similarity. The method has become a vital tool in statistical data analysis and is exploited in various disciplines, including information retrieval, pattern recognition, and machine learning. Some of the benefits of K-means clustering are its straightforward implementation, simplicity, and ability to handle large datasets with low computational complexity. The approach iteratively assigns data points to the nearest centroid based on a distance metric, commonly the Euclidean distance, and updates the centroids until convergence. The pseudocode of the K-means clustering algorithm is presented in Algorithm 2.  
```
0: dataset of points \(X=\{x_{1},\ldots,x_{n}\}\); number of clusters \(k\)
0:\(k\) clusters \(C_{1},\ldots,C_{k}\) with its centroid \(c_{1},\ldots,c_{k}\)
1:repeat
2: randomly initialize \(k\) centroid \(c_{1},\ldots,c_{k}\)
3:for\(i=1\) to \(n\)do
4:for\(j=1\) to \(k\)do
5: assign \(x_{i}\) to closest centroid:
6:if\(||x_{i}-c_{j}||<||x_{i}-c_{i}||,j\
eq l\) and \(1\leq j,l\leq k\)then
7:\(C_{j}=C_{j}\cup\{x_{i}\}\)
8:endif
9:endfor
10:endfor
11:for\(j=1\) to \(k\)do
12: centroid update \(c_{j}=\frac{1}{|C_{j}|}\sum_{x\in C_{j}}x\)
13:endfor
14:until stopping criteria is met
```  
**Algorithm 2** K-means clustering algorithm  
In the process of clustering, an essential initial step is identifying the number of clusters, \(k\). Several techniques of cluster number selection have been put forward, such as the elbow method [34], the silhouette index [35], the Calinski-Harabasz index [36], and the gap statistics [37].  
This study uses the Calinski-Harabasz index or variance ratio criterion to determine the optimal number of clusters. It is defined as the ratio between the group sum of squares (BGSS) and the within-group sum of squares (WGSS). To obtain well-separated and compact clusters, BGSS is maximized and WGSS is minimized. Therefore, the suitable \(k\) number of clusters is indicated by the maximum value for Calinski-Harabasz. The BGSS and WGSS are defined as  
\[\text{BGSS}=\sum_{j=1}^{k}n_{j}||c_{j}-c||^{2}, \tag{8}\]\[\text{WGSS}=\sum_{j=1}^{k}\sum_{x\in C_{j}}||x-c_{j}||^{2}, \tag{9}\]  
where \(k\) is the number of clusters, \(n_{j}\) is the number of observations in cluster \(j\), \(c_{j}\) is the centroid of cluster \(j\), \(c\) is the overall mean of the data points, \(C_{j}\) are the set of points in the cluster \(j\), and \(||\cdot||\) is the Euclidean norm. The definition of Calinski-Harabasz index (CH) is given as  
\[\text{CH}(k)=\frac{\text{BGSS}}{\text{WGSS}}\times\frac{n-k}{k-1}, \tag{10}\]  
where \(n\) is the number of observations.
# A Deep Learning Method for Lightweight and Cross-Device IoT Botnet Detection +
## Anomaly Detection Method  
### Background on Deep Autoencoders  
Our cross-device method is based on the use of deep autoencoders, i.e., a specific type of neural network where the _input layer_ has the same length as the _output layer_. The middle, hidden, layer of an autoencoder is also known as the _bottleneck layer_, and its dimension is lower than the input/output layer. An autoencoder consists of two parts: **encoder** and **decoder**. Let **x** be a vector of \(n\) real numbers [\(x_{1}\),\(x_{2}\),...,\(x_{n}\)], such as the records representing IoT traffic for the dataset used in the experiments. The encoder maps **x** to a code vector--or hidden representation--**y** at the bottleneck layer. On the other hand, the decoder transforms **y** into a vector of \(n\), i.e., the same size of **x**, real numbers \(\textbf{z}=[z_{1},z_{2},...,z_{n}]\). Figure 1 represents an autoencoder with three hidden layers. Encoding-decoding formulas are given in Equations (1) and (2). They represent the case of an autoencoder with only one hidden layer:  
\[\textbf{y}=\sigma(W\textbf{x}+b) \tag{1}\]  
\[\textbf{z}=\sigma^{\prime}(W^{\prime}\textbf{y}+b^{\prime}) \tag{2}\]  
where \(W\), \(W^{\prime}\), \(b\) and \(b^{\prime}\) are weight matrices and bias vectors, while \(\sigma\) and \(\sigma^{\prime}\) are activation functions. An autoencoder compresses the input into a lower-dimensional representation at the _bottleneck_ layer, and then it reconstructs the output from the representation. Deep learning can be applied to autoencoders. In particular, multiple hidden layers can be used to provide depth: the resulting network is known as a _deep_ or _stacked autoencoder_[32].  
In the autoencoder terminology, **z** is called the **reconstruction** of the input vector **x**. The \"quality\" of the reconstruction is summarized by the **reconstruction error** (RE), which measures the difference between the output **z** and the originating input **x**:  
\[RE=\frac{1}{n}\sum_{i=1}^{n}(z_{i}-x_{i})^{2} \tag{3}\]  
where \(z_{i}\) and \(x_{i}\) (with 1\(\leq\)\(i\)\(\leq\)n) denote the components of the output and input vector, and \(n\) is the dimensionality.
## 1 Introduction
[23] examined the impact of perceived control and access to control options in occupants' health and comfort, through 64 office buildings in Europe. The link between perceived comfort and control parameters such as temperature, ventilation, shading from the sun, light, noise was investigated. The analysis showed that occupants feel more comfortable when the perceived control over temperature, ventilation and noise is high. No significant correlation was found between comfort and the different types of access to control like operable windows, type of thermostats, etc., except for solar radiation. In a recent study, [PERSON] et al. [24] tried to identify the relationship between the level of personal control and users satisfaction within offices. They found that higher controllability leads to morethermal and visual satisfaction, while the results revealed the psychological impact on the users' satisfaction by indicating differences among the available control types. The psychological aspect in personal control was also raised by [PERSON] and [PERSON] [25] and [PERSON] [26][PERSON]  
The physiological and psychological aspect of IEQ satisfaction [27] and more specific thermal satisfaction makes it harder to control indoor environment conditions and provide optimal results for everyone in a given space. The ASHRAE standard 55 [27,28] or ISO 7730 [29,30] tries to give a solution in this complex relation by stating the appropriate conditions that should be met in order to establish comfort levels in offices. To achieve that, these standards consider both personal factors, such as metabolic rate and clothing level, and environmental factors, such as air temperature, mean radiant temperature, air speed and humidity. Apart from thermal comfort, other factors that can influence comfort levels are usability of a space, acoustics, ventilation, daylight and energy use in a building. Literature review highlights the role of personal control on the IEQ in office buildings. The objective of this paper is to provide an updated overview of the personal control in office buildings and the association with occupants' perception. To the best of our knowledge, this is the first time that European employees have participated in a questionnaire survey covering simultaneously in detail records of comfort, control and health perception in office buildings. This large-scale survey was performed under the framework of the European FP7-funded project OFFICAIR [31] and included eight widely distributed across European countries (Finland, France, Greece, Hungary, Italy, Portugal, Spain, The Netherlands) with different characteristics (e.g., geographical location, climate, socio-economic status). More specifically, the aim of this study is threefold: (i) to describe the degree of personal control over indoor environments in office buildings as reported by occupants (perceived personal control) and the association with access to available controls; (ii) to investigate the associations between perceived control and building characteristics; (iii) to study the associations between perceived control and perceived comfort and health of the occupants.
# Subspace Detours Meet Gromov-Wasserstein
## 2 Background
### Subspace Detours and Disintegration  
[PERSON] and [PERSON] [14] proposed another OT problem by optimizing over the couplings which share a measure on a subspace. More precisely, they defined subspace-optimal plans for which the shared measure is the OT plan between projected measures.  
**Definition 1** (Subspace-Optimal Plans [14] Definition 1).: _Let \(\mu,\
u\in\mathcal{P}_{2}(\mathbb{R}^{d})\) and let \(E\subset\mathbb{R}^{d}\) be a \(k\)-dimensional subspace. Let \(\gamma_{E}^{*}\) be an OT plan for the Wasserstein distance between \(\mu_{E}=\pi_{\#}^{E}\mu\) and \(\
u_{E}=\pi_{\#}^{E}\
u\) (with \(\pi^{E}\) as the orthogonal projection on \(E\)). Then, the set of \(E\)-optimal plans between \(\mu\) and \(\
u\) is defined as \(\Pi_{E}(\mu,\
u)=\{\gamma\in\Pi(\mu,\
u)|\;(\pi^{E},\pi^{E})_{\#}\gamma=\gamma_ {E}^{*}\}\)._  
In other words, the subspace OT plans are the transport plans of \(\mu,\
u\) that agree on the subspace \(E\) with the optimal transport plan \(\gamma_{E}^{*}\) on this subspace. To construct such coupling \(\gamma\in\Pi(\mu,\
u)\), one can rely on the Gluing lemma [18] or use the disintegration of the measure as described in the following section.