esa-sceva/satcom-chunk-collection huggingface dataset:
# Immobilization of (Aqueous) Cations in Low pH M-S-H Cement
## 3 Results
### Ex Situ Characterization
#### 3.2.3 Thermogravimetric Analysis (TGA)  
The TGA and the derivative of the weight loss are presented in Figure 3a,b respectively, for the control sample together with the precipitates obtained in presence of secondary metal cation. TGA of the control sample presented the corresponding multi-step mass loss reported for synthetic M-S-H [27; 28; 48]. The first mass loss from 40 to 300 \({}^{\circ}\)C was associated with the physically bound interlayer water and is strongly related to the drying methods of M-S-H [28]. The weight loss in this region indicated a percentage of physically bound water for the control M-S-H samples of ca. 16-17%. The second loss of mass from 300 to 790 \({}^{\circ}\)C was due to the evaporation of structurally bound water in the M-S-H in the form of hydroxyl groups (Mg-OH, Si-OH). In this region, control experiments exhibited a loss of ca. 10-11 wt.%. Regarding the precipitates obtained in the presence of secondary metal cations, (excepted for the Pb, Cr, and Al containing samples), the content of physically bound water (30-300 \({}^{\circ}\)C) ranged from ca. 17 wt.% to 21 wt.% following the same water loss shape than the control sample (metal-free). For those precipitates the second weight loss related to the hydroxylation was also similar (both in % and in shape) to the control sample, indicating that no clear proofs of extra specific hydroxide phases by TGA. The Ba containing samples showed an additional last step over 790 \({}^{\circ}\)C, probably associated with the decomposition of co-precipitated Ba carbonate phases.  
The Pb-containing samples contained only ca. half of the physically bound water and half of the chemically bound water compared to M-S-H samples, while an additional small last step over 800 \({}^{\circ}\)C was found.  
For Al containing samples this step became broader and slightly shifted to higher temperatures (shift about -50 \({}^{\circ}\)C). At the same time, the second weight loss attributed to the dehydroxylation seemed to occur at lower temperature. This could be due to the presence of amorphous Al(OH)\({}_{3}\)[49; 50] and/or hydrotalcite [49].  
Finally, the Cr containing samples showed a second weight loss centred at 175 \({}^{\circ}\)C in addition of the physically water loss, probably related to the presence of amorphous Cr(OH)\({}_{3}\) and/or double layered hydroxide similarly to the precipitates obtained in the presence of Al.
## 3 Outage Analysis of Blind-RIS-NOMA Uplink System
### Outage Probability of the Blind-RIS-SR-NOMA System for Uplink Transmission
#### 3.1.2 Outage Analysis for User 1  
The outage for user 1 occurs under the condition  
\[\log_{2}(1+v^{x_{1}})<\widetilde{D}_{1} \tag{9}\]  
where \(\widetilde{D}_{1}\) is the desired rate of user 1. Substituting (6) in (9) and solving for \(\alpha_{1}\) gives the condition for the outage.  
\[\alpha_{1}<\frac{D_{1}(1+\mu_{2}\kappa\alpha_{2})}{\mu_{1}\ \kappa} \tag{10}\]  
where \(D_{1}=2^{\widetilde{D}_{1}}\ -1\). The detailed derivation of (10) is highlighted in Appendix A.  
The probability of outage expression for user 1 is obtained using  
\[P\bigg{(}\alpha_{1}<\frac{D_{1}(1+\mu_{2}\kappa\alpha_{2})}{\mu_{1}\ \kappa} \bigg{)}=\int_{0}^{\infty}F_{\rm A_{1}}\bigg{(}\frac{D_{1}(1+\mu_{2}\kappa \alpha_{2})}{\mu_{1}\ \kappa}\bigg{)}f_{\rm A_{2}}(\alpha_{2})d\alpha_{2} \tag{11}\]  
where the cumulative distributive function (CDF) of A\({}_{1}\) is expressed as  
\[F_{\rm A_{1}}\bigg{(}\frac{D_{1}(1+\mu_{2}\kappa\alpha_{2})}{\mu_{1}\ \kappa} \bigg{)}=\int_{0}^{\frac{D_{1}(1+\mu_{2}\kappa\alpha_{2})}{\mu_{1}\ \kappa}}f_{\rm A_{1}}(\alpha_{1})\ d\alpha_{1} =1-exp\bigg{\{}-\frac{D_{1}(1+\mu_{2}\kappa\alpha_{2})}{\mu_{1}\ \kappa{N{v_{1}}^{2}}}\bigg{\}} \tag{12}\]The detailed derivation of (12) is highlighted in Appendix B. The above expression is a function of a random variable \(\alpha_{2}\). This expression needs to be averaged with respect to \(\alpha_{2}\). Substituting (12) in (11) gives  
\[P\bigg{(}\alpha_{1}<\frac{D_{1}(1+\mu_{2}\kappa\alpha_{2})}{\mu_{1}\;\kappa} \bigg{)}=\int_{0}^{\infty}\bigg{(}1-exp\bigg{\{}-\frac{D_{1}(1+\mu_{2}\kappa \alpha_{2})}{\mu_{1}\;\kappa{N}{v_{1}}^{2}}\bigg{\}}\bigg{)}f_{\Lambda_{2}}( \alpha_{2})d\alpha_{2} \tag{13}\]  
Solving the above expression results in a probability of outage of user 1, which is given by  
\[P_{out}^{Uker1}=1-\frac{exp\Big{\{}-\frac{D_{1}}{\mu_{1}\;\kappa{N}{v_{1}}^{2 }}\Big{\}}}{1+\frac{\mu_{2}D_{1}{v_{1}}^{2}}{\mu_{1}\;{v_{1}}^{2}}} \tag{14}\]  
The detailed derivation of (14) is highlighted in Appendix C.
# Depth Image Super Resolution Based on Edge-Guided Method
###### Abstract  
Depth image super-resolution (SR) is a technique which can reconstruct a high-resolution (HR) depth image from a low-resolution (LR) depth image. Its purpose is to obtain HR details to meet the needs of various applications in computer vision. In general, conventional depth image SR methods often cause edges in the final HR image to be blurred or ragged. To solve this problem, an edge-guided method for depth image SR is presented in this paper. To get high-quality edge information, a pair of sparse dictionaries was applied to reconstruct edges of depth image. Then, with the guidance of these high-quality edges, a depth image was interpolated by using a modified joint bilateral filter. Edge-guided method can preserve the sharpness of edges and effectively avoid generating blurry and ragged edges when SR is performed. Experiments showed that the proposed method can get better results on both subjective and objective evaluation, and the reconstructed performance was superior to conventional depth image SR methods.  
depth image; super-resolution; sparse coding; joint bilateral filter 1
## 4 Performance Test of SOADS
### Overall Processes of Battery Manufacturing  
The battery manufacturing process largely consists of plate, assembly, and formation processes as shown in Figure 12.  
Figure 11: Result of Local alignment.  
The plate process is the process of producing filling material for insertion into the battery. This process consists of mixing, which produces the filling material in the form of slurry, coating, in which the slurry is applied to the copper plate, press, which rolls the coated plate, slitting, which cuts the main roll of copper plate into multiple plates to produce a reel, and vacuum/dry. In the case of a positive plate, laminating is added. The assembly process consists of winding the reel produced in the plate process and assembling it with the battery case. Because of the nature of these processes, the plate process uses routing of the job shop type whereas the assembly process uses routing of the flow shop type. The formation process, which involves charging and testing the assembled battery, consists of room-temperature aging, high-temperature aging, charge/discharge, Intensity current/Resistance (IR), Open Circuit Voltage (VOC) inspection, and defect screening. However, this sequence varies depending on the product. This is a highly complex process of which the path is changed by the production status, logistics situation, line operation status, and the existence/absence of defect. Basically, the products are transported by an automatic conveyor, but when a problem occurs, a worker intervenes and determines the logistics route with non-long-term and limited information. Therefore, pattern derivation and anomaly detection from past data can be used in this process. In future, it is likely to evolve into an autonomous and adaptive process by using smart sensors and automated guided vehicles (AGVs). The experiment to assess the performance of SOADS is targeted at the event logs from the assembly process in which the lot number is generated to the formation process.
## 2 Background and Related Work  
### Clustering Overview  
In this section, we discuss the basic operations of the clustering mechanism in WSNs as follows [11].  
1. The whole network is partitioned into clusters.
2. After cluster formation process, the selected CHs gather and aggregate the data received from member nodes and transmit it towards the sink node. Usually, the CHs consume more energy as compared to the other nodes and ran out of power due to high load. Therefore, to balance the energy consumption, the role of CH is switched among different sensor nodes, meaning that a CH may not be CH for longer time in the network and other high resources sensor nodes can take over the role of CH. However, in order to elect an optimal CH, the following strategies may be adopted and are discussed as follows:Deterministic CH election: In deterministic schemes, CHs are super-nodes having high resources such as energy, storage, and computational capability etc.
* Random CH election: In these schemes a CH is elected based on randomly generated value.
* Adaptive CH election: Instead of electing CH randomly, adaptive CH election schemes provide a mechanism to elect CH based on several parameters such as residual energy, computational capability, storage, distance, etc. The combination of the multiple parameters is utilized to elect an optimal CH among several potential candidates.
* After CHs election, each CH broadcasts its information of becoming a CH to other nodes in its communication range. The receiving nodes may receive information from several CHs in its vicinity and decide which CH to join based on several metrics such as distance from CH, computational capability, residual energy and storage capability of CH, etc. After joining a particular CH, the node forwards its sensed data towards that CH.
* When resources of a current CH falls below a certain threshold, re-clustering is performed to avoid the data loss. However, frequent re-clustering degrades the performance of network due to control overhead.
# Three Diverse Applications of General-Purpose Parameter Optimization Algorithm
## 4 Application to Fingerprint-Based Indoor Localization System
### Localization Logic and Parameters  
As the initial parameter values, one is used for the initial value of the number of fingerprints, and the average of all the measured LQI data at a receiver from a transmitter located in the target room is used for the initial value of the corresponding fingerprint value.  
1. Properly locate the _Raspberry Pi_ devices with the receivers in the target field.
2. Run the programs and create the connection to the MQTT broker.
3. Locate the transmitter at the specified location in the field. In our experiments, we selected several locations where we moved the transmitter from one place to another after measuring LQI for one minute by transmitting packets every 500 ms.
4. Receive and collect the packets from the transmitter at the _Raspberry Pi_ device for 30 s.
5. Forward the collected data from the _Raspberry Pi_ device to the server through the MQTT broker.
6. For each receiver, calculate the average LQI using the forwarded data from it after the last average LQI calculation.
7. Make the fingerprints at the server and store them in the SQLite database.  
In the _detection phase_, the server detects the current room of the user by applying steps (1)-(6) in the procedure for the _calibration phase_ periodically. Then, in step (7), after the vector of the average LQI values from all the receivers are obtained, the Euclidean distance is calculated against every pre-stored fingerprint by Equation (2), and the room whose fingerprint has the smallest distance is appointed as the detected room.  
\[disF_{i}^{k}=\sqrt{\sum_{j=1}^{n}(r_{j}^{i}-R_{j}^{k})^{2}} \tag{2}\]  
where  
Figure 3: FILS15.4 application overview.  
* \(disF_{i}^{k}\) represents the Euclidean distance between the \(i\)-th measured average LQI and the fingerprint for room \(k\);
* \(r_{j}^{i}\) does the \(i\)-th measured average LQI at receiver \(j\); and
* \(R_{j}^{k}\) does a fingerprint for room \(k\) at receiver \(j\).
# Research on Multiple-Axis Contour Error Suppression Method Based on Composite Layered Control
## 2 Design of a Single-Axis Controller Based on Sliding-Mode Disturbance Observer
### Design of Disturbance Observer
Considering the disturbance observer block diagram depicted in Figure 3, with the initial values for the two system states set as \([-1,\,5]\), as shown in Figure 4, the system states can converge within a finite time. Figure 5 illustrates the estimation error of the disturbance observer under different parameter conditions. It is evident from Figure 5 that the estimation error of the disturbance observer varies with different parameters. However, ultimately, the disturbance observer catches up with the target curve. Depending on specific performance requirements and the controlled system, different parameters for the disturbance observer should be set. Figure 4: System state convergence. Figure 3: Structure diagram of disturbance observer.
# Adversarial Attacks on Medical Segmentation Model via Transformation of Feature Statistics
## 3 Experiments
### Application of the Proposed Method: Data Augmentation  
In this section, we explore the potential uses of our proposed method for data augmentation. To evaluate data augmentation effectiveness, we increased the size of the dataset by 25% during the model training phase by adding new samples to the original training set. These additional samples are randomly generated, their \(\alpha_{\mu}\) and \(\alpha_{\sigma}\) values adhering  
Figure 8: The figure depicts the limitations of our method. As observed in image (**a**), the adversarial sample is darker and more blurred than the original image, and in image (**b**), white noise is evident at the bottom of the adversarial sample.  
Figure 9: This figure illustrates the comparison of the proposed method with baseline approaches. The transformation factors are set at \(\alpha_{\mu}=0.3\) and \(\alpha_{\sigma}=0.3\), whereas the baselines’ hyperparameters are tuned based on Dice scores.  
to the \((0.9,1.1)\) range as specified in Equation (2). This augmentation strategy yields an improvement in the Dice score: it climbs from an initial score of 0.4524 to an improved score of 0.4819.  
As demonstrated in Table 1, our method significantly enhances the model's generalization capabilities, particularly evident in segmenting the esophagus (ES), inferior vena cava (IV), and both adrenal glands (RA and LA). Models trained with our data augmentation approach exhibit improved segmentation for these organs. Notably, for the ES, RA, and LA, models trained without data augmentation fail to segment these organs entirely. As previously mentioned in Section 3.1, the training set comprises only 120, 242, and 279 images featuring the ES, RA, and LA, respectively, out of a total of 821 images. Furthermore, it was noted that the pixel count representing these organs in each image is relatively low in the original dataset. Nonetheless, our data augmentation method successfully increases the variability of pixel representation for these organs, addressing the issue of limited data availability that was previously hindering the segmentation model's learning efficacy.
# Joint Transmit Waveform and Receive Mismatched Filter Design to Suppress Range Sidelobe
## 2 Design of NLFM-CPM Integrated Waveform and Signal Processing Methods
### Complexity  
The computational complexity of the proposed method stems from (35). The operation described in (35) incurs a computational complexity of \(O(N^{3})\). Given that the waveform and filter are optimized in an alternating fashion, the overall computational complexity of the proposed algorithm is \(O(KN^{3})\).
# The Performance and Fouling Control of Submerged Hollow Fiber (HF) Systems: A Review
## 9 Conclusions and Research Opportunities
### Hydrodynamics and Bubbling  
Although hydrodynamics and bubbling in submerged HF systems have been actively studied, there may be opportunities for further incremental improvement. Areas for research include identifying the optimal bubble size (somewhere between relatively few large bubbles and many micro-bubbles) and the means for generating these bubble sizes. Also, the potential energy benefits of intermittent bubbling should be further evaluated.
## 5 Real Vehicle Experiments and Analysis of Results
### Establishment of Experimental Platform  
The mobile robot experimental platform is shown in Figure 4. The electronically controlled crawler chassis can realize omnidirectional movement. Mobile power supply, display, on-board computing platform JETSON AGX ORIN (built-in ubuntu 20.04 OS), sensor equipment including ZED2i camera, 16-line LIDAR VLP-16 (vertical field of view of 30\({}^{\circ}\)), horizontal field of view of 360\({}^{\circ}\)), GUI-610 GNSS combined navigation suite (positioning accuracy of 1 cm + 1 ppm, attitude accuracy of 0.1\({}^{\circ}\)), and WHEELTEC N100 nine-axis IMU have been deployed on the car.
# Correlation between Coda Wave and Stresses in Uni-Axial Compression Concrete
## 3 Experimental Investigation  
In order to verify the rationality of the model, an experiment of CWI under stress in concrete was carried out. The propagating law of the coda wave in concrete was first investigated, and then the relation between the wave velocity and stress was calibrated by step loading. The experiment was conducted using a pulse generator to transmit a pulse signal, which can excite the transmission energy converter, causing it to vibrate, and generate an ultrasonic signal to pass through the test specimens. Finally, sensors received the signal and the data were recorded by a data collector.
# NFV/SDN as an Enabler for Dynamic Placement Method of mmWave Embedded UAV Access Base Stations
###### Abstract  
In the event of a major disaster, base stations in the disaster area will cease to function, making it impossible to obtain life-saving information. Therefore, it is necessary to provide a wireless communication infrastructure as soon as possible. To cope with this situation, we focus on NFV/SDN (Network Function Virtualization/Software-Defined Networking)-enabled UAVs equipped with a wireless communication infrastructure to provide services. The access link between the UAV and the user is assumed to be equipped with a millimeter-wave interface to achieve high throughput. However, the use of millimeter-waves increases the effect of attenuation, making the deployment of UAVs problematic. In addition, if multiple UAVs are deployed in a limited frequency band, co-channel interference will occur between the UAVs, resulting in a decrease in the data rate. Therefore, in this paper, we propose a method that combines UAV placement and frequency division for a non-uniform user distribution in an environment with multiple UAVs. As a result, it is found that the offered data rate is improved by using our specific placement method, in terms of not only the average but also the outage user rate.  
U  
U
# InfNRS-QC: Crowd-Sourced Creation of a Dataset and Machine Learning Model for fNIRS Quality Control
## 1 Introduction
[PERSON] and [PERSON] [14], for example, employed a deep learning classifier to decode the strength of hand movements in order to develop more accurate Brain-Computer Interfaces (BCI). Similarly, [PERSON] et al. [15] developed a Deep Learning classifier to classify motion imagery of three different hand gestures. Deep Learning models have also been employed to assess and classify the mental workload of difficult tasks, such as driving [16] or memory tasks [17]. Concerning signals' quality estimation, a machine learning version of the SQI (MLSQI [18]) has been developed based on the training dataset described in [PERSON] et al. [13]. However, the training dataset was collected from only 14 participants and labeled by individuals working at the company that produces the fNIRS recording device used. The limited number of collected signals is a crucial limitation for the efficient application of a Deep Learning approach, which is however intrinsically connected with the fNIRS field for multiple reasons. First, the novelty of the field results in a reduced availability of large-scale fNIRS datasets that can be employed for secondary data analysis or for the development of novel tools and techniques. Secondly, the difficulty in obtaining data labeled by experts of the field, combined with the lack of a ground truth that determines the quality of a signal makes it impossible to obtain large-scale labeling of the quality of fNIRS signals. To overcome these limitations, in this paper, we introduce a crowd-sourced training dataset consisting of 510 10-s segments of single-channel fNIRS signals. Through crowdsourcing, we are able to leverage multiple fNIRS recordings from a wider range of participants. By making use of a web interface, we were able to reach out to more individuals with experience working with fNIRS and tapped onto their expertise in labeling the quality of the segments through this interface. The labeled dataset is here used to train and test a machine learning model that can identify the quality of a signal, and that can therefore be used to support non-experts of the field that approach the fNIRS signals for the first time or to make pre-processing pipelines more objective, by introducing an objective way to identify segments of signals of high quality that can be used for further analysis.
# An Intelligent Error Correction Algorithm for Elderly Care Robots
## 4 Experimental Results and Analysis
### User Experience and Cognitive Load  
To test whether the elderly escort robot based on the error correction algorithm meets the design requirements, 20 elderly people mentioned above were invited to this part. The evaluation of the elderly on the convenience, helpfulness, experience, and intelligence of the escort system was investigated. The evaluation adopts the 5-point system; 0-1 indicates that the evaluation is very low, 1-2 indicates that the evaluation is relatively low, 2-3 indicates that the evaluation is general, 3-4 indicates that the evaluation is relatively high, and 4-5 indicates that the evaluation is very high. The evaluation results are shown in Figure 12.  
Figure 11: Comparison of recognition rates of different algorithms.  
Figure 12: User experience evaluation.  
As can be seen from Figure 12, in the aspect of helpfulness evaluation, the probability of the robot providing correct help is greatly improved because the error-correction algorithm based on game rules improves the gesture recognition rate. In terms of experience evaluation, the error rate of the CNN method is high, which greatly reduces the fluency of the whole process and the experience of the elderly. Intelligent evaluation is for the same reason, strong error-correction ability, intelligent evaluation is naturally high.  
At the same time, 20 elderly people who participated in the experiment were invited to conduct a NASA evaluation. User evaluation indicators are divided into mental demand (MD), physical demand (PD), performance (P), effort (E), and frustration (f). NASA's evaluation index adopts a 5-point system; 0-1 indicates that the cognitive burden is small, 1-2 indicates that the cognitive burden is relatively small, 2-3 indicates that the cognitive burden in general, 3-4 indicates that the cognitive burden is relatively large, and 4-5 indicates that the cognitive burden is very large. As can be seen from Figure 13, the game rule-based error-correction algorithm proposed in this paper has a low user cognitive load and smoother accompanying process. At the same time, it also brings novelty to the elderly and high user evaluation.