esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
These applications will help in filling the gap of our knowledge regarding the ocean and aquatic environments in general. Nevertheless, deployment of UASNs due to their acoustic modem transceiver and the appropriate hardware for protecting the circuitry is expensive and limited to experimental settings. The ASNs deployment missions can take several days since sensors might be attached to docks, anchored buoys, sea floors, underwater autonomous vehicles (UAVs), low-power gliders, or underpowered drifters, depending on the desired network architecture [5]. On the other hand, efficient data collection using ASNs is challenging due to the various aforementioned factors in the UWSNs. Often, underwater ASNs are equipped with acoustic modems to wirelessly communicate with each other. Underwater communication links are affected due to location and time-dependent acoustic channel communication characteristics. Moreover, temporary connectivity loss can occur due to shadow zones. Therefore, the wireless link between neighboring ASNs may perform poorly or even be down at any given moment, which will increase the packet retransmissions as an attempt to deliver data packets result in more packet collisions, delay and energy consumption in UWSNs. Thus, excessive rerouting due to poor link quality consumes nodes residual energy, which can disrupt underwater monitoring and exploration missions prematurely [11]. To address these issues, a number of data collection protocols have been proposed in the literature (see Section 2). The key aim of these routing schemes is to mitigate the impacts of the underwater environments and enhance the poor quality of underwater acoustic physical links by taking advantage of the multicast and broadcast nature of the wireless transmission medium. However, each approach has disadvantages that, when combined, could diminish significantly the performance of UWSNs. Some of their common drawbacks, e.g., poor data reliability and channel capacity, communication void region problem, high latency, and redundant data packet transmission are accented in acoustic communication, which severely diminishes UWSNs performance. Therefore, a highly reliable routing architecture is essential for UWSNs. In the rest of the paper, Section 2 discusses the existing routing protocols in UWSNs. In Section 3, we briefly describe the proposed quality of service routing protocol (QoSRP). In Section 4, we discuss the acoustic channel and energy consumption models used in the simulation studies. In Section 4, we also provide simulation results and analyze the performance of QoSRP against existing routing protocols in UWEs. Finally, in Section 5, we conclude the paper with future work.
## 5 Results and Discussion
### Experiment on ECG Beat Segmentation and Detection  
The ECG beat segmentation and detection algorithm described in [39] were implemented in the computing platform described in Section 4. We evaluated the algorithm on the 48 records of the MIT-BIH database. The experiment was repeated 10 times for each record and then the average time was calculated. The experimental result showed that, on average, it requires around 26 us to detect and segment one ECG beat with zero padding. This result justified the use of two stage ECG classification as proposed in this paper, as the segmentation time is minimal compared to the improvement achieved in the classification stage.
# Capacity Analysis of Lattice Reduction Aided Equalizers for Massive MIMO Systems
## 3 Receivers
### Zf  
The complete suppression of the interference has been made possible by ZF receiver [7]. In addition, it is done by taking the pseudo inverse of the channel matrix.  
\[H^{\dagger}=(H^{T}H)^{-1}H^{T} \tag{3}\]  
The output signal from the ZF receiver is given by,  
\[\tilde{y}_{zf}=H^{\dagger}x \tag{4}\]  
The output SNR for of the \(n\)th branch for the ZF receiver is given by,  
\[\rho_{zf,n}=\frac{\rho}{[(H^{H}H)^{-1}]_{nn}},1\leq n\leq N_{t} \tag{5}\]  
The problem in the ZF receiver appears when there are ill-conditioned \(H\) matrices and this leads the increase in noise variance after the matrices inversion. Without noise, ZF acts as an optimal receiver similar to ML. However, it produces a noise amplification.
## 3 Measurement of State Data Based on Non-Contact Image Analysis
### Respiration Signal Acquisition
Since such a change in blood flow causes a minute change in skin color, respiration information obtained through observation of the change in skin color can be used to improve signal quality when a skin area is detected in an image. The respiration measurement method through motion analysis is susceptible to movement other than the movement caused by respiration, whereas the skin color change analysis method enables stable observation of changes through facial area tracking. If motion analysis is difficult due to movement, the method of measuring respiration from changes in skin color can be used as a good alternative. Changes in blood flow due to heart rate are mainly periodic, and the cycle is shorter than changes due to breathing. Therefore, it is possible to estimate the respiration signal from which the heart rate component has been removed through a high-pass filter that can filter short periodic signals from the blood flow change signal. As shown in Figure 18, it is possible to extract more refined and stable breathing signals by integrating breathing information that can be obtained from skin color changes as well as motion analysis.
# The Principle and Downhole Testing of Water Injection Exploration in Depleted Reservoirs
## 1 Introduction
### Analysis and Discussion of Water Injection Results  
#### 3.4.1 The Pressure-Permeability Patterns at Different Distances from the Old Goaf  
Through the water injection tests conducted at boreholes 1# and 3#, it was observed that the maximum water pressure and permeability patterns varied at different distances from the old goaf. Curves depicting the flow rate and pressure at different injection points for the three boreholes were plotted separately (refer to Figures 5-7).  
From Figure 5, it can be seen that at distances ranging from 24 m to 20 m from the old roadway, the water injection pressure ranged between 9 and 10 MPa; at distances ranging from 20 m to 16 m from the old roadway, the water injection pressure ranged between 5.8 and 6.4 MPa; at distances ranging from 16 m to 12 m from the old roadway, the water injection pressure ranged between 5.0 and 6.0 MPa; and at distances ranging from 12 m to 8 m from the old roadway, the water injection pressure decreased to 4 MPa. It is evident that the water injection pressure at borehole 151 decreased significantly as the distance from the old roadway decreased.  
Figure 6 illustrates that at distances ranging from 38 m to 34 m from the old goaf, the water injection pressure ranged between 9 and 10 MPa; at distances ranging from 34 m to 30 m from the old goaf, the water injection pressure ranged between 5.4 and 6.0 MPa; at distances ranging from 30 m to 26 m and from 26 m to 22 m from the old goaf, the water injection pressure increased to between 8.0 and 9.0 MPa; and at distances ranging from  
Figure 4: Comparison chart for 151—left: vertical apparent resistivity contour section results.  
22 m to 18 m from the old goat, the water injection pressure decreased to below 3 MPa. Analysis suggests that in the 30 m to 22 m segment, the abnormal water injection pressure was mainly due to grouting after the injection at 34 m to 30 m, resulting in the diffusion of grout into the 30 m to 22 m segment. However, overall, borehole 153 also exhibited the characteristic of decreasing water injection pressure with proximity to the old goat.  
Figure 5: Water injection pressure and flow chart of different positions of hole 151.  
Figure 6: Water injection pressure and flow chart of different positions of hole 153.  
Figure 7: Water injection pressure and flow chart of different positions of hole 155.  
From Figure 7, it can be observed that borehole 156, unaffected by the old goat or old roadway, maintained relatively stable water injection pressure, ranging between 4.5 and 5 MPa.
## 3 Proposed Solution
### Chromosome Decoding
#### 3.2.2 Binary Decoding  
* In binary decoding, each gene represents one feature: true indicates that the feature at that gene is selected and false marks it as unselected.
* Each chromosome represents the selected feature subset.
* The order of genes in a chromosome is set to the same order as the features in the dataset.
* The length of the chromosome \(n\) is the same as the number of features.  
A chromosome is expressed as \(c=(f_{1},f_{2},f_{3},\ldots,f_{n})\), where \(f_{n}\) expresses \(n\)th feature, \(n\) is equal to the number of total features, and, therefore, \(n=(1,2,3,\ldots n)\), and \(f_{n}\in F\). The value of each gene is randomly chosen as true or false by the initialization method in the initial population step and located in the problem space. The length of a chromosome is the same as the number of total features. As an example (Figure 4), the number of features of KDD Cup '99 [36] was 41. If feature selection is performed for this dataset, the chromosome length becomes 41. Given a dataset having five features, \(c=(1,0,1,1,0)\) indicates that the 1 st, 3 rd, and 4 th features will be selected and detection models will be generated by building training models only with the selected features.  
The advantage of binary decoding is that both the optimal partial feature set and its length can be found despite the simple decoding design. Since a global optimal solution (partial feature set) can be obtained for the corresponding dataset, the feature set which shows the best performance, and its length, can be obtained. When the number of original features in the dataset is large, the operation and search times for the optimal solution may abruptly increase since selections should be made from the maximum number of features.
# Performance Analysis of On-Demand Scheduling with and without Network Coding in Wireless Broadcast
## 2 Related Works
### Non-Real-Time Model  
In non-real-time applications, the responsiveness of the system is measured by the time from the submission of a request to the time that this request has been served. The primary objectives of non-real-time scheduling algorithms is to minimize the response time of serving requests. Many classical algorithms have been proposed for non-real-time applications. FCFS (First Come First Served) [26] broadcasts data items in the arrival order of the corresponding requests. [PERSON] [27] proposed two popular scheduling algorithms: MRF (Most Requested First) and LWF (Longest Wait First). MRF broadcasts the data item according to the popularity of the requested data items. LWFbroadcasts the data item with the longest total waiting time. [PERSON] and [PERSON] (1998) suggested that the straightforward implementation of LWF is exhaustive and not suitable for large systems. They proposed a new scheduling scheme called R \(\times\) W (Number of Pending Requests Multiply Waiting Time), which combines the benefits of MRF and FCFS. R \(\times\) W broadcast the data item with the maximum R \(\times\) W value. [PERSON] and [PERSON] (2015); [PERSON] and [PERSON] (2015) analyzed the mean response time of different existing scheduling algorithms under multi-channel environment. [PERSON] et al. (2016) studied the complexity of adaptive push-based broadcast system for reducing mean response time. [PERSON] et al. (2017) studied the combined data push and data retrieval scheduling problem in multi-channel environment.
# Toxic Metals Migration from Plastic Food Contact Materials in Romania: A Health Risk Assessment
## 4 Limitations and Recommendation  
The novelty of this study lies in its being one of the first investigations into the health risks associated with heavy metal migration from food contact materials in Romania. It provides scientific evidence on potential hazards, particularly under long-term exposure. However, the study has limitations: (1) only four materials (PP, PE, PVC, and composites) were examined, and others like PS and PET should be included for a comprehensive risk assessment; (2) the sample size was limited; (3) only metals were considered, excluding other chemical contaminants (e.g., bisphenol A, phthalates); (4) additional toxic metals (As, Hg, Al) were not assessed; and (5) testing was conducted solely under long-term exposure, so further research is needed for different conditions (e.g., high temperatures, short contact times).  
Although the release of heavy metals from plastics is generally low, caution is advised. Prolonged or repeated used of plastics, especially with hot food, can increase the risk of ingesting harmful heavy metals or other chemical migrants. Based on these findings, several recommendations are suggested: (1) Clear plastic containers are preferable as they tend to release fewer particles, organic compounds, and heavy metals. (2) Reducing meal time can minimize plastic particles ingestion [38]. Ideally, glass containers should replace plastic for hot foods and beverages [53]. Moreover, further scientific research and public education are necessary to provide solid evidence and raise awareness about potential health risks [38].
## 1 Introduction
Compared to existing scoring-based and reconstruction-based methods, our proposed approach eliminates the necessity for generating numerous positive and negative samples, thus enhancing both efficiency and interpretability. Furthermore, our method excels at  
Figure 1: Existing weakly supervised approaches for temporal sentence ground. (**a**) Scoring-based methods learn to score the semantic alignment using a large number of positive/negative samples; (**b**) Reconstruction-based method assumes that the mask query can be reconstructed from matched video proposals; (**c**) RL-based methods adjust the candidate windows by rewarding beneficial actions. capturing semantically complex visual cues while effectively mitigating issues related to language bias. Additionally, our approach integrates the MMS to hierarchically adjust candidate windows using a multi-policy movement framework. This strategy is based on a tree structure that first determines the direction of movement and zooming, followed by predicting the number of frames for adjustment. This hierarchical method identifies temporal boundaries in a coarse-to-fine manner, resulting in more accurate predictions. In comparison to existing methods utilizing multi-policy movement strategies [25], we further introduce a series of constraints within the loss functions to rationalize the movement process. This enhancement enables us to train the reinforcement learning (RL) framework more stably and efficiently. Our contributions can be summarized as follows:  
* The GDAM, which integrates scoring-based and reconstruction-based methods, is designed to define rewards by modeling semantic consistency between the video segment and query. * The MMS with rational constraints is employed to imitate the behavior of human beings when grounding the temporal moment. The movement results of the MMS give feedback to the reward, which establishes a bridge between reinforcement learning and grounding tasks. * Extensive experiments on Charades-STA and ActivityNet-Captions datasets demonstrate the superiority of the proposed method against existing weakly supervised and RL-based methods.
# A Review on Local Failure Probability Sensitivity Analysis
## 4 Local RSA in the Standard Normal Space
### Sensitivity through the Design Point of the System
#### 4.1.2 Sensitivity with Multiple Design Points in the Standard Normal Space  
For systems with multiple design points, an accurate formula of \(P_{f}\) is difficult to derive [32]. However, in the standard normal space, with the approximation introduced in [33], the derivatives can be easily derived in the FORM context. Suppose \(K\) design points have been identified \(P_{1}^{*},P_{2}^{*},\ldots,P_{K}^{*}\); we recall here that the failure probability is approximated with the formula [31]  
\[P_{f}^{\text{FORM}}=1-F_{K}(\beta(\mathbf{s});\mathbf{C}(\mathbf{s})),\]  
where \(F_{K}\) is the cdf of a centered normal vector of dimension \(K\) with covariance matrix \(\mathbf{C}(\mathbf{s})\) and \(\boldsymbol{\beta}(\mathbf{s})\) is the vector of the norms of all the design points \(\left(\beta_{k}=\|P_{k}^{*}\|\right)_{k=1,\ldots,K}\). The covariance matrix \(\mathbf{C}(\mathbf{s})\) is defined such as \(C_{i,j}=\boldsymbol{\alpha}_{i}^{\top}\boldsymbol{\alpha}_{j}\) where \(\boldsymbol{\alpha}_{i}=-\
abla_{\mathbf{x}\mathbf{g}}(\mathbf{s},P_{i}^{*})/ \|\
abla_{\mathbf{x}\mathbf{g}}(\mathbf{s},P_{i}^{*})\|\). The derivative of the failure probability with respect to \(s_{\ell}\) is written  
\[\widehat{\frac{\partial P_{f}(\mathbf{s})}{\partial s_{\ell}}} =-\sum_{k=1}^{K}\left(-\frac{\partial\beta_{k}(\mathbf{s})}{ \partial s_{\ell}}\frac{\partial F_{K}(\boldsymbol{\beta}(\mathbf{s}); \mathbf{C}(\mathbf{s}))}{\partial x_{k}}+\sum_{j=1}^{k-1}\frac{\partial C_{k,j }(\mathbf{s})}{\partial s_{\ell}}\frac{\partial F_{K}(\boldsymbol{\beta}( \mathbf{s});\mathbf{C}(\mathbf{s}))}{\partial C_{k,j}}\right),\]  
where the derivative of each \(\beta_{k}\) can be derived from Equation (13). The interested reader can find the derivatives of \(C_{k,j}\) in [31]. Another approach to compute the sensitivity of \(P_{f}\) in a series system is presented in [12]. First, a MC sampling is performed using the various approximated lsfs built at the different design points, in a FORM/SORM context, to estimate \(P_{f}\). Then, the derivatives are obtained with the score function method with IS, using, in the \(\mathbf{Z}\)-space, the failing observations obtained from the computation of \(P_{f}\); cf Section 3.3.2. This special approach is restricted to distribution parameters \(\boldsymbol{\theta}\) only.
# Implementation and Evaluation of Activity-Based Congestion Management Using P4 (P4-ABC)
## 6 Performance Evaluation
### Evaluation Methodology
#### 6.1.2 Test Environment  
Our testbed is hosted by a virtual machine with Ubuntu 16.04, 4 CPU cores with hyperthreading, 3.5 GHz, and 8 GB RAM. We utilize Mininet version 2.3.0d4 to emulate the mentioned experimental network. Clients, server, and the switch are implemented as virtual machines. We leverage Iperf 3.6 for TCP and UDP traffic generation between client and server and measure the goodput in terms of transport layer payload. One run takes 300 s and 10 runs were carried out per data point.
## 4 Discussion
### Error Sources in Estimation of \(cSIF_{yield}\) and \(\Phi_{F}\)
Secondly, \(\omega_{L}\) in the red band is not constant across the growing season, and the temporal variations in leaf pigment content would induce a temporally varying \(\omega_{L}\), which could further cause misleading changes in \(f_{esc,688}\). Thirdly, the estimation of \(f_{esc,688}\) by \(Red_{v}/fPAR\) ignored the effects of soil background, despite the fact that \(NDVI^{2}\) could be used to correct vegetation reflectance from the apparent reflectance. We conducted sensitivity tests on the estimation of \(f_{esc,688}\) and \(\Phi_{F,688}\) and on their responses to temperature by varying \(\omega_{L}\) (Figures S5 and S6). Based on the PROSPECT-D model, we found that \(\omega_{688}\) was largely influenced by leaf chlorophyll a/b concentration (\(C_{ab}\)) and exhibited a decreasing trend with increasing \(C_{ab}\). Due to the changes in pigment pool affecting the estimation of \(f_{esc,688}\) more than that of \(f_{esc,760}\) (Figures S5 and S6), the responses of \(\Phi_{F,688}\) estimated by \(f_{esc,688}\) to temperature contained some uncertainties resulting from the changes in pigment content. Therefore, we changed \(C_{ab}\) in SCOPE model to change the \(\omega_{L}\) and further estimated the variations of \(f_{esc,688}\) and \(\Phi_{F,688}\). From Figure S6, we can see that \(C_{ab}\) affects \(f_{esc,688}\) and \(\Phi_{F,688}\) estimated by \(Red_{v}\), which indicates that seasonal variations of \(C_{ab}\) along with temperature would cause misleading changes in \(f_{esc,688}\) in response to temperature. Many studies have demonstrated that canopy observed SIF and \(f_{esc}\) were largely influenced by changes in \(C_{ab}\)[15; 16; 66]. Finally, although the zenith inclination distribution of the leaves in winter wheat is random and the leaf clumping effects in planophile wheat are generally not obvious [67; 68; 69], we only assessed the above assumptions using the SCOPE model, which assumes that the canopy is horizontally homogenous and has full canopy cover over the soil background; further testing with 3D radiative transfer models will be helpful (e.g., Discrete Anisotropic Radiative Transfer, abbreviated as DART; [70; 71]). In addition, it should be noted that the depths of the O\({}_{2}\)B are generally smaller than that of O\({}_{2}\)A. \(cSIF\) retrievals at the O\({}_{2}\)B band are generally less robust than those at the O\({}_{2}\)A band due to the different absorption depths [41], which might also result in some uncertainties.
## 1 Introduction
Therefore, the structure should be finely shaped by using topology optimization, as this is an effective method to obtain lightweight and high-strength structures [19]. In this work, the proposed structure was designed and optimized by using finite element-based topology optimization software ProTOP 6.2. [20]. It should be noted that cellular lattice structures typically have a complex design and can only be manufactured by using additive manufacturing technologies [21; 22; 23]. Therefore, the gear studied in this work was manufactured by selective laser melting (SLM) using Ti-6 Al-4V material. However, there are several challenges in the manufacturing of intricately designed parts using metals [24]. The thermal gradient, overhanging areas, and pore formation depend on the orientation of the part for the build and the manufacturing parameters in the SLM process [25]. Laser power, scanning speed, and hatch spacing are the key factors regulating these phenomena. Therefore, a perfect combination of these factors is required to obtain a part with few defects and high mechanical strength [26]. Inthis study, the best possible way to keep these properties in balance with adequate values was found. The manufactured gear was tested on a test rig with a closed loop. Previously, different test rigs were employed to test the dynamic behavior of gears [27; 28; 29]. For the investigations of strains, a test rig was newly designed, manufactured, and assembled. The strain during gear testing is typically measured by engaging strain gauges [30; 31; 32]. In this study, strain gauges were mounted on the hub, tooth root, and gear ring in a variety of positions and directions. This article is structured as follows. Section 2 briefly describes the design and optimization of the gear-body structure, while Section 3 describes the manufacturing of the gear. Section 4 presents the developed test rig, the measurement setup, and the obtained results.