esa-sceva/satcom-chunk-collection huggingface dataset:
# Speech Communication System Based on Piezoelectric Electret Mechanical Antenna
## 3 Results
### Transmitter Side  
In this case, the end-to-end communication process is divided into distinct phases, with the first three phases (\(t1\)-\(t3\)) located at the transmitter side, and the remaining three phases (\(r1\)-\(r3\)) located at the receiver side. In our measurement environment, the operating frequency was set to 167.34 kHz based on the resonant frequency of the PZT-42, and the transmission distance was set to 0.5 m. The modulation method was ASK, and the frequency of the modulation was fixed at 10 Hz in order to facilitate the observation of the measured data. With such experimental parameters, our communication system is capable of transmitting ten bits per second, which can become higher as the modulation frequency increases:  
* \(t1\) Message Input: The user says \"hello\" to the microphone, and the transmitter terminal recognizes the user's speech and converts it into text;
* \(t2\) Message Processing and Encoding: This message \"hello\" is processed and converted to a binary code. If the user has defined a method to process the data, the data is processed first. Otherwise, the message will be encoded directly. For example, the process of the original message \"hello\" without extra processing is as follows: 1. Obtain the ASCII code of \"hello\", expressed in hexadecimal as \"0x68656C6C6F\"; 2. Append control information such as the frame header and frame tail to get the complete transmitting sequence: \"0 xFF0168656C6C6F20\" in hexadecimal; 3. Convert to binary code to get the final binary string to be transmitted;
* \(t3\) Message Transmitting: As shown in Figure 7a, the binary string obtained in the previous step is used as the source signals. The transmitter terminal controls the AFG and modulates the signals. This system utilizes ASK modulation to modulate binary 1 as a sine wave at 167.34 kHz with 16,734 repeated cycles, while binary 0 is represented by the absence of any signal transmission. Figure 7b depicts the message's final modulated signals. These signals are amplified and fed directly to the mechanical antenna, which then radiates into free space.  
Figure 6: A case of an end-to-end communication process.
###### Abstract  
The increasing incorporation of power electronics and other non-linear loads, in addition to their energy advantages, also implies a poor power quality, especially as regards harmonic pollution. Different solutions have been proposed to measure harmonic content, taking the International Electrotechnical Commission (IEC) standards as a reference. However, there are still some issues related to the measurement of the harmonic, and especially, interharmonic content. Some of those questions are addressed in this work, such as the problem derived from the instability of the values obtained by applying the discrete Fourier transform to each sampling window, or the appearance of local peaks when there are tones separated by multiples of the resolution. Solutions were proposed based on time aggregation and the overlapping of windows. The results demonstrate that aggregation time, window type, and overlapping can improve the accuracy in harmonic measurement using Fourier transform-based methods, as defined in the standards. The paper shows the need to consider spectral and time groupings together, improving results by using an appropriate percentage of overlap and an adaptation of the aggregation time to the harmonic content.  
Harmonic analysis; interharmonics; spectral leakage; rectangular window; Hann window; time aggregation; overlapping; power quality Article  
A Study of the Effects of Time Aggregation and Overlapping within the Framework of IEC Standards for the Measurement of Harmonics and Interharmonics  
[PERSON] _et al._ (2019), [PERSON] _et al._ (2019), [PERSON] _et al._ (2019) and [PERSON] _et al._ (2019), 24 October 2019
# Vendor-Agnostic Reconfiguration of Kubernetes Clusters in Cloud Federations
## 5 Reconfiguration Tactics
### API Server Configuration  
Due to the extensibility of Kubernetes, we can extend the Kubernetes API server with new API resources through custom resource definitions (CRDs) or aggregated API servers. The main difference is that with CRDs, the API server is responsible for handling requests for newly introduced APIs, while with an aggregation server, the API server forwards requests for the new API to a user-implemented API server. Additionally, since K8s v1.16, dynamic admission webhook [44] has been a stable feature in Kubernetes to create custom object admission logic for API servers. When the API server receives a resource object, it will call back the relevant webhook servers deployed externally to the control plane. The mutating webhook server can modify the resource object, and the validating webhook server decides whether to allow the resource object into the cluster. These assets can be used to bring in missing API resources and built-in admission controllers  
We illustrate the tactic for the PodPreset feature. As stated in Section 4.1, in order to meet the OpenShift community's demand for this feature, Red Hat Communities of Practice (Redhat-COP) has reactivated the API using a CRD and a mutating admission webhook server [9]. The new CRD defines the exact same API resource format as the original built-in PodPreset. The mutating admission webhook server listens for newly created pods and matches them with PodPreset objects according to the pod's labels and the label selectors of PodPreset objects. Once matched with a PodPreset object, it injects the running information from the PodPreset object into the pod manifest. To register the webhook server with the API server, a MutatingWehookConfiguration is created to instruct the API server to pass all newly created pods to the webhook server. Figure 3 describes the interaction between the API server and the webhook server. The webhook server is deployed in a worker node. Whenever the API-server receives a pod creation request, it will send the pod object in an AdmissionReview request to the webhook server. The webhook server injects information into the pod object and returns it in an AdmissionReview response. The returned pod object is used to complete the pod creation process in the API server. In this figure, we suppose that there are no other admission steps and that the API server sends the pod resource object directly to the scheduler.
## 7 Risk Analysis
### Edge Gateway
#### 7.6.3 Information Disclosure  
1. **Component**  
_Security risk_: The vulnerability of information disclosure of the edge gateway presents a significant high-risk scenario. In this context, the edge gateway lacks encryption forthe information it receives, rendering it susceptible to man-in-the-middle attacks. This exposes a critical weakness in the system's security architecture, allowing attackers to intercept and potentially steal sensitive data during transmission. The absence of encryption heightens the risk of unauthorized access, potentially compromising the integrity of the emergency communication process and the confidentiality of the transmitted information.  
_Security measures_: To mitigate the risk of information disclosure, it is imperative to implement robust encryption mechanisms. Encrypting the information transmitted to the edge gateway ensures that even if intercepted, the data remains unreadable to unauthorized parties, safeguarding the confidentiality and integrity of the communication.
## 3 RSMA-Assisted Integrated Sensing and Communication Systems
### Key Challenges for RSMA-Assisted ISAC
#### 3.3.2 Dual-Functional Beamforming Optimization  
It is important to optimize the beamforming to both maximize the communication performance such as weighted sum rate and the minimum rate among all users while ensuring the radar performance by satisfying the CRB constraints.  
Extensive research has focused on optimizing ISAC beamforming. In [41], the joint optimization of full-duplex (FD) based ISAC systems has been investigated under the criterion of minimizing the transmit power. In [42], hybrid beamforming for FD-based systems is optimized. The joint design of communication reception coefficients and transmission waveforms is addressed in [43], and it is shown that the proposed technique achieves high communication quality of service (QoS). Additionally, ref. [44] introduced a finite alphabet input method to propose a block-level beamforming design for ISAC systems.  
Most of the literature on ISAC beamforming addressed the interference between multiple users and radar targets via SDMA. To achieve more flexible interference management, Refs. [10; 14] investigated RSMA-based ISAC systems. Since RSMA allows partial decoding of interference while treating some interference as noise [45], enabling robust interference management strategy for both inter-function and intra-function interference in future ISAC systems. Furthermore, ref. [26] extended RSMA-based ISAC to multiple target scenarios.
## 4 Results and Discussion  
The performance of the methodology described above and its usefulness to measure temporal changes of the topography is illustrated using two different case examples. The first one is the Morenci Mine in Arizona USA, where we use SkySat tri-stereo data to document topographic changes due to the mining operations. The second one is the Shisper Glacier in Pakistan, where we use multi-date PlanetScope data to document the change of the glacier topography due to a surge.
# Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques
## 10 Proposed Solution: Background, Implementation, and Simulation Results
### Machine Learning Techniques  
Machine Learning (ML) is a subset of artificial intelligence that builds mathematical models to find patterns in data sets. The goal is to make decisions without human assistance. They have been widely discussed in the literature for their ability to adapt to changing scenarios and reduce site planning. For instance, the authors of [47] present various ML algorithms and how they have been employed to coordinate co-channel interference.  
Moreover, these techniques can be classified as supervised, non-supervised, and Reinforcement Learning (RL). Supervised learning algorithms are akin to a supervisor that holds the desired knowledge. These algorithms adapt based on inputs previously known by the supervisor (labeled data sets). Non-supervised algorithms do not have labeled inputs. Hence, they aim to find hidden patterns or similarities within the data. On the other hand, RL continuously interacts with the system, and each decision produces a reward or punishment that serves to update the algorithm parameters. The goal is to maximize or minimize the reward or punishment [48].  
For the proposed scenario, RL is the most appropriate paradigm. Supervised learning requires prior knowledge and dedicated time for training, which is not desirable for a system that demands constant re-adapting. Non-supervised learning does not require prior knowledge, but RL is a better option for this task, considering the algorithm learns from each action taken using immediate rewards. RL is an efficient paradigm for interaction with uncertain environments [49], enabling real-time decision making.
# A Review on Head-Related Transfer Function Generation for Spatial Audio
## 1 Introduction
Starting by introducing measurement techniques and physical modelling, this paper will focus on anthropometric and morphological approaches for HRTF personalisation and interpolation methods for HRTF generation. An overview of the methodologies for HRTF generation discussed in this paper can be found in Figure 1.  
The rest of this paper is organised as follows. Section 2 provides a definition of ans HRTF and the reference system used to localise the sound sources relative to the head, along with the mathematical framework underlying this definition. Section 3 reports a short overview of the methods used for measuring and estimating HRTFs and other complementary data such as 3D meshes. Section 4 reports the process of HRTF physical modelling. Section 5 describes data-driven anthropometric and morphological approaches  
Figure 1: Scheme of the methodologies for HRTF generation presented in this paper. focusing on HRTFs personalisation. Section 6 analyses HRTFs interpolation algorithms. Its integration into consumer products and a real-world evaluation are reported in Section 7. Finally, conclusions are drawn in Section 8.
## 1 Introduction
for eight weeks improved glucose tolerance and hepatic steatosis in a high-fat diet-induced C57 BL/6] and PPAR\(\alpha-/-\)mice (\(n=7\)-10), by activating PPAR\(\alpha\), which regulates fatty acid oxidation and ketogenesis [39]. C3G also showed anti-allergic effects; oral administration of C3G (1.2 mg/mouse/day, p.o.) for 25 days alleviated allergic airway inflammation in an ovalbumin (OVA)-induced asthma model (4-week-old male BALB/c mice, 14-16 g, \(n=5\)) by modulating Th2 cytokines and inhibiting the IL-1R\(\alpha\)-STAT6 pathway [40]. Similarly, 12 days of C3G (25 mg/kg, p.o.) in an OVA-induced asthma model (6-8-week-old male BALB/c mice, \(n=5\)) mitigated allergic airway inflammation by modulating histamine, IgA levels, and balancing Th1/Th2 immunity [41]. Additionally, C3G (200 \(\upmu\)mol/kg BW, 1 h, p.o.) inhibited passive cutaneous anaphylaxis in IgE-sensitized mice (5-week-old male ICR, 100 ng/ear, \(n=4\)) by suppressing mast cell degranulation from anti-DNP IgE-stimulated RBL-2H3 mast cells elucidated [42]. These results suggest that C3G plays a crucial role in managing allergic reactions. Therefore, the consumption of natural products containing C3G may be beneficial for the prevention and treatment of allergic responses. However, the anti-allergic effects and underlying mechanisms of C3G-rich _Lonicera caerulea_ fruit have not yet been fully elucidated. This study aims to evaluate the potential of _Lonicera caerulea_ fruit extract (HR2302-30E) as a functional food ingredient with anti-allergic properties. We assessed its bioactive components, including total phenolic and flavonoids and C3G, and evaluated its antioxidant activity. Additionally, the anti-allergic effects of HR2302-30E and C3G were investigated by measuring the secretion of \(\beta\)-hexosaminidase and histamine from IgE-stimulated RBL-2H3 mast cells. Furthermore, we explored the underlying mechanisms by analyzing key signaling pathways, such as Fc\(\epsilon\)RI, Syk, Fyn, Src, PLCy1, PKC8, and MAPK. These evaluations provided valuable insights into the effects of mast cell degranulation and the mitigation of allergic reactions.
# Towards Understanding Driver Acceptance of C-ITS Services--A Multi-Use Case Field Study Approach
## 3 Results
### Questionnaire Results
#### 3.1.3 Safety Improvement and Intended Usage  
In the ESRIUM questionnaire, the usefulness of bad surface information and its estimated impact on road safety were investigated. 25 out of 27 respondents indicated that they consider \"bad surface information\" as useful. Furthermore, 23 respondents indicated that they would like to receive bad surface information, and 21 of them do not fear being distracted from the driving task. Regarding the potential improvement of road safety through bad surface information provision, 25 out of 27 agree that such information would improve safety. This is in line with the concerns about unexpected situations caused by a bad surface. More than half of the respondents (17 out of 27) indicated that they have concerns about a bad surface.  
In the ASFINAG, the online questionnaire and the interviews also covered the question of whether the participants want to have such in-vehicle information permanently in their vehicle. Whereas in the interviews, the question was referring to every use case separately, the online questionnaire included the question once for all use cases. The analysis of the online questionnaire responses indicates that a substantial proportion of participants (60-72%) express concerns related to potential driving hazards. These concerns primarily focus on exceeding the speed limit and encountering unexpected events such as traffic congestion or road construction. Moreover, a majority of respondents believe that C-ITS information provision can effectively mitigate driving-related concerns and enhance both road (78% approval rate) and personal safety (56% approval rate). This finding is further supported by interviews conducted with test drivers, which revealed a similar sentiment, although with minor variations observed across different use cases.  
Therefore, it is not surprising that results reveal a strong overall preference for the integration of C-ITS-based in-vehicle information systems. 50% of the respondents of the online questionnaire and 75-87.5% of the respondents of the interviews agree on having such an in-vehicle information system. Notably, a clear positive correlation emerges between the concreteness of the presented use case, the level of user experience with the use case, and the corresponding approval rate. The substantial proportion of \"I don't know\" responses (35% of the respondents of the online questionnaire) suggests the existence of untapped potential for further increasing approval rates, likely through enhanced user education and more tangible demonstrations of the technology's benefits.
## 4 Proposed HTPR Method  
### Overview of the Proposed Solution  
The proposed HTPR method consists of two phases: offline preparation and online recommendation, as shown in Figure 3. Firstly, in order to achieve a high-quality recommendation effect, we consider both item features and user behaviors in the process of feature input and description. Secondly, to explore effective recommendation techniques with good capability of dealing with massive data, a parallelized execution strategy was designed to optimize the recommendation process. After item clustering, feature supplementation and user clustering were carried out in a parallelized way. A brief introduction of the key components and concepts of the proposed HTPR method is given in the following:  
* **Feature input and description**. Feature information was input to the recommendation system after feature description. The input features involved the content of items, i.e., item/attribute matrix and user behaviors including browse, add-to-favorite, purchase, review and rating (described as user-item browse matrix, user-item wish list matrix, user-item purchase matrix, item review tag matrix, and user-item rating matrix respectively).
* **Item clustering**. After the feature combination of item review tags and item attributes were obtained, item clusters were generated by the K-Means algorithm.
* **Feature supplementation**. For each item without rating in the user-item rating matrix, the most similar neighbors from its cluster were picked out, and then it was rated based on the rating of its nearest neighbors. In this way, the user-item rating matrix was supplemented.
* **Integrating similarity**. The preference of a user for an item category was defined based on the results of item clustering, and then the concern degree of the user for the item category was defined based on the user's historical behaviors. By integrating preference and concern degree, the tendency of the user for the item category could be defined, and the integrating similarity could be calculated.
* **User clustering**. Clustering users according to the user-item category tendency matrix, and obtaining user clusters and their cluster centers.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Types of User Behavior** & **User Behavior** \\ \hline Explicit behavior & rating, vote, share/forward, add-to-favorite, purchase, review \\ Implicit behavior & click/browse, time on page \\ \hline \hline \end{tabular}
\end{table}
Table 2: Common user behavior.  
**Online recommendation**. Selecting some clusters from all user clusters by comparing cluster centers with a predefined similarity threshold, and then obtaining the set of nearest neighbors by measuring the similarity between the target user and users of selected clusters. Next, the rating prediction of all candidate items was made for the target user based on the rating of the nearest neighbors. Finally, a personalized service recommendation list (Top-\(N\) list) was generated and the most appropriate items were recommended to the target user.
## 3 The Proposed Model
### Emotion Classification  
For accurate and timely emotion classification, the presented CLBEDC-SND model employed the ELM model. [PERSON] et al. [26] devised the ELM model for single hidden layer feedforward neural network (SLFN). The algorithm arbitrarily chooses the input weight and empirically defines the output weight of SLFN. Afterward, the input weight and hidden layer bias are selected at random, SLFN is regarded as a linear technique and the output weight of SLFN is empirically defined by a generalized inverse function of the hidden layer output matrix. This model produces a fast-learning speed when compared to classical feedforward network learning algorithms while attaining good performance of generalization. Further, ELM tends to accomplish the minimum norm of weights and the minimum training error. The output weight is evaluated by Moore-Penrose (MP) generalized inverse. The learning speed of ELM is hundreds of times quicker than classical learning algorithm with good performance when compared to the gradient based learning mechanism. Different from the conventional gradient-based learning algorithm that works only for differentiable activation functions, the ELM mechanism is utilized for training SLFN with multiple non-differentiable activation functions. Figure 2 portrays the infrastructure of ELM.  
For a sequence of training instances \(\left\{\left(x_{j},\ t_{j}\right)\right\}_{j=1}^{N}\) with \(N\) instances and \(m\) classes, the SLFN with activation function \(g(x)\) and \(L\) hidden nodes is formulated below [27], where \(w_{i}=\left[w_{1i},\dots,w_{in}\right]^{T}\) indicates the input weight, \(b_{i}\) denotes the bias of \(i\)-\(th\) hidden nodes, \(\beta_{i}=\left[\beta_{i1},\dots,\beta_{in}\right]^{T}\) shows the weight vector linking the \(i\)-\(th\) hidden and output nodes, \(w_{i}\cdot x_{j}\) shows the inner products of \(w_{i}\) and \(x_{j}\) and \(t_{j}\) is network output interms of input \(x_{j}\).  
\[\sum_{i=1}^{L}\beta_{i}g\left(w_{j}\cdot x_{j}+b_{i}\right)=t_{j},j=1,2,\dots, \ N \tag{8}\]  
The Equation (8) is formulated by Equation (9):  
\[H\beta=T \tag{9}\]  
where  
\[H=\begin{bmatrix}g(w_{1}\cdot x_{1}+b_{1})&\cdots&g(w_{L}\cdot x_{N}+b_{L})\\ \vdots&\cdots&\vdots\\ g(w_{1}\cdot x_{N}+b_{1})&\cdots&g(w_{L}\cdot x_{1}+b_{L})\end{bmatrix}_{N\times L },\beta=\begin{bmatrix}\beta_{1}^{T}\\ \vdots\\ \beta_{L}^{T}\end{bmatrix}_{L\times m},T=\begin{bmatrix}t1^{T}\\ \vdots\\ tN^{T}\end{bmatrix}_{N\times m}\]  
In the expression, \(H\) indicates the hidden layer output matrices of NN system, whereas \(\beta\) shows the output weight matrix.