esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Saving Traffic Scheduling in Hybrid Software Defined Wireless Reschargeable Sensor Networks
## 4 Mathematical Optimization Model
### Node Energy Consumption
After a time span \(t\), the rest energy in node \(v\)'s battery is \(E_{rest-v}\). Let \(d_{cv}\) denote the distance from node \(v\) to its responsible wireless charger \(c\), \(P_{cv}\) denote the charging power from the wireless charger \(c\) to node \(v\), and \(t_{cv}\) denote the total charging time of node \(v\). Considering \(d_{cv}\) should be less than \(d_{0}\) to ensure feasible charging efficiency (Equation (1)), there is:  
\[\frac{P_{cv}t_{cv}}{A\times d_{cv}^{2}}=E_{v}+E_{rest-v}-E_{mb-v}, \tag{7}\]  
where \(A\) is a constant coefficient. Note that, in Equation (7), the charging time \(t_{cv}\) may be accumulated from many discrete time slots in time span \(t\). When \(t\rightarrow\infty\), there should also be \(t_{cv}\rightarrow\infty\). In this case, we have:  
\[\frac{P_{cv}}{A\times d_{cv}^{2}}=\lim_{t_{cv}\rightarrow\infty}\{\frac{E_{v }}{t_{cv}}+\frac{E_{rest-v}}{t_{cv}}-\frac{E_{mb-v}}{t_{cv}}\}\overset{(a)}{=} \frac{E_{v}}{t_{cv}}, \tag{8}\]  
where _(a)_ follows that \(E_{rest-v}\) and \(E_{mb-v}\) are both limited constants, and \(E_{v}\) is a function of \(t\) (\(t\geq t_{cv}\)). Therefore, when \(t\rightarrow\infty\), we can consider the energy consumption \(E_{v}\) equals to the energy charged to node \(v\). That is to say, when \(t\rightarrow\infty\),  
\[E_{v}=\frac{P_{cv}t_{cv}}{A\times d_{cv}^{2}}. \tag{9}\]  
Suppose when a node \(v\) consumes \(E_{v}\) energy, the responsible wireless charger \(c\) should consume \(E_{cv}\) energy to compensate the energy consumption (i.e., \(E_{v}\) consumption at node \(v\)). Combining with Equation (9), there is:  
\[E_{cv}=P_{cv}t_{cv}=A\times E_{v}\times d_{cv}^{2}. \tag{10}\]
# Machine Learning-Driven Dynamic Traffic Steering in 6G: A Novel Path Selection Scheme
## 1 Introduction
In recent years, several studies have been conducted to improve the latency value, such as the placement of edge computing near the access node and the application of federated learning. Both non-exhaustive methods share the common objective of reducing the processing time. This study focuses on the manipulation of a different technique of machine learning to expedite and uphold the optimum decision from the learning process. In addition, the emergence of the non-terrestrial network co-existing with the terrestrial transport network in the end-to-end infrastructure necessitates automating the path selection process for optimum resource assignment. This study focuses on the usability of offline data generated from a series of machine learning and linear programming works in [8], where the clustering outcome implies the mixtures of traffic classifications that share similar attributes, and the path assignment is the outcome from the optimization solver for the resource assignment. The results for each packet are stored as an extensive list of training data for a classification task that labels the selected transport network for the assigned traffic. Based on the findings from the study in [8], the overall clustering and optimization activity consumes a significant amount of time and computational resources. It is inapplicable for real-time operations, insinuating an investigation to adopt supervised learning to manipulate the labeled data from the clustering and optimization solver processes for classification processes. This paper classifies path selection based on traffic attributes and assigns it to appropriate transport networks for traffic steering functions. The aim is to perform a classification that indicates the selection of transport network for each traffic. Respectively, the contributions to this study are stipulated as follows:  
1. Introduced a traffic steering model that learns from operational data handled by the 5G nodes in a scenario of heterogeneous transport network types. * Produced a novel traffic-transport network assignment scheme based on data generated in an area to achieve optimum resource management. * Conducted an analysis of the timeslot for optimum classification model performance. The appropriate volume size of timeslot constituting sufficient diversity of UE traffic types helped create a good performance model. The remainder of this study is organized as follows: Section 2 elaborates on the related work on traffic steering approaches using various machine learning applications for classifications and predictive tasks. Section 3 discusses the framework for the traffic-to-transport network assignment proposal. Section 4 highlights the findings and analysis of the observations from the simulation scenario incorporated in the framework. Finally, Section 5 describes the conclusion and future works.
# Predicting Reputation in the Sharing Economy with Twitter Social Data
## 3 Case Study and Methodology
### Identity Pairing  
Past research has proved that publicly available information such as names, self-reported descriptions and social connections can be used to disambiguate users from different platforms [7]. While these methods are useful to match profiles between different platforms, they suffer from a significant error rate at the same time that are difficult to scale. Instead, we have designed an alternative process which tracks social media patterns related to sharing economy platforms to match identities from both platforms (Figure 2a). This way we are creating a dataset of matched identities that we can enrich later with both platforms data (Figure 2b).  
This process leverages certain actions that can be performed in sharing economy platforms and have an impact on their connected social networks, such as posting referral invites or sharing some content. In the case of Wallapop, after users upload an item to the platform, they are offered the possibility to share such listing on multiple social networks to increase its potential audience. If users choose to share it on Twitter, a default message is offered, such message format has been constant through time and contains a link to the Wallapop listing. An example of this default message is: _\"I am selling PlayStation 4 on #wallapop <item URL at Wallapop>\"_.  
Figure 2: Proposed methodology to build a model for reputation prediction.  
Users can modify such messages, but most of the time, they do not change it or perform only minor changes. Based on this observation, we have searched tweets matching those patterns to collect Twitter users that sell a product in Wallapop so that we can match Twitter and Wallapop identities. In particular, we have searched for tweets that match the following two conditions:  
* _Contain a link to a Wallapop product_ that we can use to reach the Wallapop profile of the seller.
* _Tweets similar to the default tweet message,_ including keywords as _selling_ or hashtags as _#wallapop_.  
Accessing Twitter data is a straightforward task thanks to the Application Programming Interface (API) offered by the platform. Twitter offers different _endpoints_ to access different sets of data in the same way that the official applications do. For the profile matching, the _search tweets_ endpoint was used, which returns a collection of relevant Tweets matching a specified pattern. After frequently tracking Twitter for an extensive period (from December 2015 to December 2016), we ended with a dataset formed by 34,981 paired Wallapop and Twitter identities. We expected a very low error rate with this matching process as it searches for the default tweet created when users share their profiles. In addition, we have created a random sample of 100 matches and manually verified them by comparing the names and pictures of both profiles, resulting in 98 confirmed matches and two uncertain matches.
###### Abstract
(3) We modify the existing PeerJS bidirectional communication system, to enable unidirectional communication from the clinical site to the classroom. It only sends and receives texts using WebSocket technology [12] for bidirectional communication between the clinical site and the classroom. To verify the effectiveness of the proposed method, we implement a secure video streaming system. Specifically, we first install a general-purpose computer and a USB camera. Next, we install the Node.js library [13] and build an HTTP server [14], a PeerJS server, and a WebSocket server. On the client side, we install a bone sensing PoseNet model. In addition, we designed web screen transitions and created three modules: a home page, a video delivery page, and a video viewing page. To evaluate the effectiveness of the proposed method from various perspectives, we conducted preliminary experiments on face masking using bone sensing and general face recognition under different viewing angles and interpersonal distances for human faces captured from cameras. Our main experimental results show that the results using bone sensing are more stable at middle and far distances, with less drastic changes in the eye mosaic. In contrast, the results using face recognition show that the eye line mosaic and face mosaic are stable at near distances and when the head is facing forward, but otherwise the mosaic is largely unable to produce a mosaic with a more consecutive effect. Through these results, we clearly show that bone sensing is more effective than face recognition for consecutive face masking by combining various factors such as distance and head direction. The remainder of this paper is organized as follows: In Section 2, we discuss related works on image-based face masking. We provide a detained description of the proposed method in Section 3. A preliminary experiment of evaluating face masking effects using bone sensing and face recognition is presented in Section 4, followed by the conclusions in Section 5.
# Steel Surface Defect Diagnostics Using Deep Convolutional Neural Network and Class Activation Map
## 2 Background
### Feature Extraction Methods  
#### 2.1.1 Gray Level Co-Occurrence Matrix  
Gray level co-occurrence matrix (GLCM), proposed by [PERSON] et al. [18], is a two-dimensional matrix that contains statistical information about the texture of the single-channel image. While the images are generally comprised of three different layers, i.e., red, green and blue, GLCM utilizes a single level of the gray image. Briefly speaking, texture analysis could be carried out while statistically considering the spatial relationship from the GLCMs. The matrix is generated by the pixel values in the image, calculating the frequency sum of adjacent pixel pairs in a particular region. Owing to its advantages of extracting the features from the images, it has been utilized to the image analysis tasks in a variety of fields, e.g., medical, material and manufacturing [19; 20; 21].  
A method for calculating the GLCM is schematically described in Figure 1a. In a grey-scale original image, the co-occurrence of the paired pixel values which are represented as the numbers in the figure is counted at a given offset. By and large, the offset can be varied over degrees, i.e., horizontal, vertical and diagonal. The coordinates of the GLCM, \(i\) and \(j\), are determined based on the previous pixel values from the gray-level image, while the frequency sum of the counts is calculated via the intensity values from the original image and assigned to the GLCM as \(P_{i,j}\) corresponding to the coordinates. Since the pixel value of the original image ranges between 0 and 255, basically 256 \(\times\) 256 of the GLCM can be produced. Finally, several second-order statistical features for the texture analysis, i.e., angular second moment (ASM), contrast, entropy, and homogeneity, are defined as follows [18; 22].  
\[ASM=\sum_{i,j=0}^{255}P_{i,j}^{2} \tag{1}\]  
\[Contrast=\sum_{i,j=0}^{255}P_{i,j}(i-j)^{2} \tag{2}\]  
\[Entropy=\sum_{i,j=0}^{255}P_{i,j}(-lnP_{i,j}) \tag{3}\]  
\[Homogeneity=\sum_{i,j=0}^{255}\frac{P_{i,j}}{1+(i-j)^{2}} \tag{4}\]
## 7 Conclusions and Future Work  
The over-limit problems of the wheel-rail force and the carbody acceleration when multiple sets of trains pass through the high-speed railway turnout were monitored and evaluated using experimental analysis and numerical simulation in this paper. To create a single vehicle-turnout coupled dynamic model for the simulation study, we chose the vehicle with the worst carbody lateral acceleration alarms, set the wheel polygonal wear at the same time, and compared the dynamic response before and after grinding the turnout rail profile. The following are the main findings:  
1. Following grinding, the wheel-rail contact points, equivalent conicity, and rail damage were improved, and the turnout rail profiles matched the wheel profile well. The simulation findings of the vehicle-turnout coupled dynamic model show that the wheel-rail force and carbody vibration acceleration are significantly reduced when the train goes over the ground turnout, with polygonal worn or not.
2. The wheel-rail contact dynamic stress tester, comprehensive inspection train, and portable train vibration analyzer were used to monitor the wheel-rail force and the carbody lateral acceleration before and after the rail grinding. It is evident that following the grinding, the wheel-rail force, the carbody lateral acceleration, and the alarm phenomena of the carbody lateral acceleration were all improved.
3. According to the research presented in this paper, rail grinding can improve the wheel-rail force and carbody acceleration, but in order to avoid over-grinding, it is important to grind the rail in strict compliance with the rail's standard profile. When grinding the front panel, switch panel, closure panel, crossing panel, and rear panel, special attention should be paid to the smoothness of each area and avoid the after-grinding phenomenon of an uneven rail surface. In addition, over time, due to the influence of wheel-rail force, the rail profile will gradually deteriorate following grinding, leading to the issue of over-limited wheel-rail force and carbody acceleration. As a result, it is necessary to routinely grind high-speed railway turnouts.  
Conceptualization, Q.X.; Methodology, Y.Y. and C.C.; Software, Y.Y.; Investigation, D.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [National Natural Science Foundation of China, Research on harmonic wear mechanism of high-speed train wheels under spatially coupled vibration behavior of wheel-rail system] grant number [51975210].  
No new data were created.  
The authors wish to thank all the staff members involved in the field test at the China Railway Shanghai Group Co., Ltd., Shanghai, China. This work was supported by the National Natural Science Foundation of China (51975210). Thank you to [PERSON] from Chengdu Railway Bureau in China for his support in the resource and project management of this paper.  
The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.
## 3 Results
### Dry Unit Weight  
Within the scope of the study, geopolymer cubes of dimensions 50 \(\times\) 50 \(\times\) 50 mm were produced. After a curing period of 28 days, their weights were measured in a dry state (air-dried) using a precision balance, and their dry unit weights were calculated. The results are presented in Figure 17.  
Upon examining Figure 17, it is observed that the dry unit weight values of the mortars varied between 2.04 and 2.11 g/cm\({}^{3}\). The heaviest dry weight obtained from the tests was 2.11 g for every cubic centimeter in mix No. 9, as shown in Figure 18. This tells us how different materials that make up this mixture affect its density when everything is dry and not full of water (or wet).  
Because FA particles are bigger than the other waste materials mixed, it was noticed that the samples had more void ratios. As the amount of FA increased, a decrease in dry  
Figure 16: The relationship between the mixing ratio of the material used and the water-absorption values.  
Figure 17: The dry unit weights of the geopolymer mortar.  
Figure 18: The relationship between the mixing ratio of the material used and the dry unit weights.  
unit weight was observed. This outcome matched what [PERSON] et al. found in 2020 [31]. The size of particles in BFS and LP is less than that in FA. This leads to making geopolymers with fewer empty spaces inside them. Therefore, it was found that as more BFS and LP were added, the dry weight increased.  
The consolidated results of experiments conducted on prepared geopolymer mortars are shown in Table 3.  
Upon reviewing Table 3, it is evident that sample mix No. 9, which provides the highest dry unit weight, also yields the highest compressive strength value. This is attributed to porosity; the sample with the highest porosity exhibited lower dry unit weight and compressive strength, while simultaneously presenting a higher void ratio and higher water-absorption values.
## 3 Results and Discussion
### Kinetics of Bacterial Growth
The lack of effect of drying temperature on the kinetics of bacterial growth may be explained by the shorter exposure time of garlic slices to thermal conditions during the drying process at higher temperatures. Specifically, to reduce the moisture content of garlic slices to the desired level of 10%, samples exposed to 90 \({}^{\circ}\)C were dried for only 80 min, whereas at lower temperatures, such as 50 \({}^{\circ}\)C, the drying process lasted 550 min. This shorter exposure time at higher temperatures maintained similar antimicrobial activity of garlic compared to samples dried for longer times at lower temperatures. Since temperature does not affect the growth kinetics of the tested _E. coli_ strain, the only factor that can effectively inhibit or delay bacterial growth, which should be subject to optimization, is the concentrations of extracts derived from raw or dried garlic. This knowledge, gained from the study, helps in understanding the variability in kinetic parameters of bacterial growth and can be leveraged to design garlic products with enhanced antimicrobial properties. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Growth Kinetic Parameter**} & \multirow{2}{*}{\begin{tabular}{c} **Content of Garlic** \\ **Extract (\%)** \\ \end{tabular} } & \multirow{2}{*}{**Raw Garlic**} & \multicolumn{3}{c}{**Dried Garlic**} \\ \cline{4-6}  & & & **50 \({}^{\circ}\)C** & **70 \({}^{\circ}\)C** & **90 \({}^{\circ}\)C** \\ \hline \multirow{3}{*}{\(\mathrm{lag\ time}\), \(\tau_{\mathrm{lag}}\) (h)} & 0 & 2.184 & - & - & - \\  & 2 & \textgreater{}18 & \textgreater{}18 & \textgreater{}18 & \textgreater{}18 \\  & 0.5 & 6.663 & 6.967 & 7.084 & 6.286 \\  & 0.25 & 4.258 & 4.010 & 4.017 & 3.906 \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} maximum rate of exponential \\ growth, \(\mu_{max}\) (1/h) \\ \end{tabular} } & 0 & 0.301 & - & - & - \\  & 2 & 0.000 \({}^{\ast}\) & 0.000 \({}^{\ast}\) & 0.000 \({}^{\ast}\) & 0.000 \({}^{\ast}\) \\  & 0.5 & 0.263 & 0.257 & 0.276 & 0.257 \\  & 0.25 & 0.285 & 0.270 & 0.275 & 0.292 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} total increase in bacterial \\ population, \(\Delta_{max}OD\) \\ \end{tabular} } & 0 & 0.221 & - & - & - \\  & 2 & 0.000 \({}^{\ast}\) & 0.000 \({}^{\ast}\) & 0.000 \({}^{\ast}\) \\ \cline{1-1}  & 0.5 & 0.215 & 0.203 & 0.219 & 0.245 \\ \cline{1-1}  & 0.25 & 0.220 & 0.217 & 0.185 & 0.186 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Growth kinetic parameters of _Escherichia coli_ ATCC 25922 estimated for control samples and test samples containing garlic extracts based on raw garlic and garlic dried at different temperature conditions.
# An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision
## 1 Introduction
### Methodology and Articles Selection
#### 1.3.3 Geographical Attribution of Research Articles  
In this subsection, we elaborate on the methodology used to attribute geographical affiliations to the research articles included in our analysis. It is important to note that the process of determining the geographical source of research articles can be complex, especially in cases involving international collaboration or multi-region affiliations of authors [14]. We followed a set of principles to attribute geographical affiliations:  
* Primary author affiliation--In cases where the primary author of a research article had affiliations in multiple countries or regions, we attributed the article to the country or region corresponding to the primary author's primary institutional affiliation;
* Collaborative research--For articles involving collaboration among multiple countries or regions, we counted the article once for each collaborating country or region. This approach ensures that collaborative efforts are appropriately acknowledged within our regional analysis;  
Figure 1: SLR methodology flow diagram.  
* Inclusive approach--We adopted an inclusive approach that recognized the contributions of all regions involved in collaborative research. This approach aligns with our goal of providing a comprehensive overview of the geographical distribution of domain adaptation research;
* While we made every effort to accurately attribute geographical affiliations, it is important to acknowledge that certain complexities, such as dual affiliations or author mobility, may not have been fully addressed. Additionally, author information may change over time, and our analysis represents a snapshot based on available data.  
By disclosing our attribution principles and verification process, we aim to provide transparency and address potential concerns about the accuracy and validity of our regional analysis.
# Using Full Pose Measurement for Serial Robot Calibration
## 4 Robot Calibration Based on 3D Measurements  
Once the twist angles \(\tilde{\mathbf{\kappa}}\) are estimated, they can be inserted in (4) and the remaining DH parameters \((\mathbf{\epsilon},\mathbf{r},\mathbf{d})\) can be found in traditional calibration procedure using 3D data. Given \(K\) configurations of the arm (i.e., \"poses\") defined by \(\mathbf{\theta}_{k}\) (\(k=1,\ldots,K\)), the SMR is moved to \(K\) positions \(\mathbf{g}_{k}=[x_{k},y_{k},z_{k}]\) in 3D Cartesian space. Since \(\mathbf{g}_{k}\) and \(\mathbf{t}_{k}\) in (8) are determined in different coordinate frames, the error function \(Err_{pos}\) in the calibration process is based on relative distances between two 3D points to avoid a dependence on a registration. For convenience, the whole set of \(K\) points can be divided in two halves and then  
\[Err_{pos}(\tilde{\mathbf{\kappa}},\mathbf{\epsilon},\mathbf{r},\mathbf{d})=\frac{1}{K/2}\sum _{k=1}^{K/2}(L_{k}(\tilde{\mathbf{\kappa}},\mathbf{\epsilon},\mathbf{r},\mathbf{d})-h_{k})^{2} \tag{10}\]  
where  
\[L_{k}(\tilde{\mathbf{\kappa}},\mathbf{\epsilon},\mathbf{r},\mathbf{d})=\|\mathbf{t}_{k}-\mathbf{t}_{ k+K/2}\| \tag{11}\]  
and \(\mathbf{t}_{k},\mathbf{t}_{k+K/2}\) are determined in (8), \(\|\ldots\|\) is the Euclidean norm and \(h_{k}=\|\mathbf{g}_{k}-\mathbf{g}_{k+K/2}\|\) is a distance between two points measured by laser tracker.  
Thus, the fitted DH parameters \((\tilde{\mathbf{\epsilon}},\tilde{\mathbf{r}},\tilde{\mathbf{d}})\) can be estimated by minimizing \(Err_{pos}\)  
\[\big{(}\tilde{\mathbf{\epsilon}},\tilde{\mathbf{r}},\tilde{\mathbf{d}}\big{)}=\min_{\mathbf{ \epsilon},\mathbf{r},\mathbf{d}}Err_{pos}(\tilde{\mathbf{\kappa}},\mathbf{\epsilon},\mathbf{r}, \mathbf{d}). \tag{12}\]  
providing the vector of link twist angles \(\tilde{\mathbf{\kappa}}\) is known. The actual dimension of search space is \(3N-2\) since the distance between two points in (11) does not depend on \(d_{1}\) and \(\epsilon_{1}\) (the two parameters may have arbitrary values which only affect the registration transformation between robot and sensor). In the remainder of this paper, we call this procedure Method 1.