esa-sceva/satcom-chunk-collection huggingface dataset:
[39] proposed and applied a combined methodology to optimize a cable-driven parallel robot for upper limb rehabilitation exercises by minimizing the tensions in cable and achieving the smallest footprint. In [40], the authors presented a new mechanism for providing variable stiffness for a 4 DOFs robot used for upper limb rehabilitation. ASPIRE [22] is a device designed as an innovative structure, a parallel robotic system with 3 DOFs based on a spherical architecture designed for upper limb rehabilitation. The main purpose of its design was to solve problems related to shoulder flexion/extension as well as adduction/abduction and pronation/supination of the forearm. Its kinematic structure and motion were presented in [23]. Studies related to how it worked in clinical trials can be found in [30; 41]. In [41], the ASPIRE interchangeability was investigated with classical physiotherapy using quantitative electroencephalogram (EEG), motor driving times and turn/amplitude analysis. A significant effect of the therapy was found for various pathologies. Design optimization following clinical evaluation and clinician feedback was presented in [31]. After careful monitoring and analysis of the robotic system, significant differences were revealed compared to the initial laboratory tests performed on healthy subjects. An improved design was elaborated based on the critical characteristics of clinical trials. The aim of this study is to analyze from a kinematic, dynamic and experimental point of view the ASPIRE robot for the medical recovery of brachial monoparesis. For this purpose, a dynamic simulation of the robot in the ADAMS virtual environment when a virtual patient for rehabilitation uses the robot will be performed. Three distinct situations will be studied:  
* The kinematic elements will be considered as rigid bodies, and the friction in the kinematic couplings will not be considered;
* The kinematic elements will be considered as rigid bodies, and the friction in the robot couplings will be considered;
* Both the flexibility of the kinematic elements and the friction in the couplings will be considered. Results obtained by numerical simulation will be compared with those obtained from MSC.ADAMS. Finally, a verification of the structural integrity of the flexion/extension module of the robot, which is subject to the highest mechanical stresses, will be performed.
## 4 Experiment and Analysis  
This paper uses the publicly available TUM dataset in addition to the KITTI dataset to validate the algorithm's performance. Image sequences of indoor scenes taken with a Kinect camera and referenced with actual camera poses are included in the TUM dataset. Experiments are also conducted using the highly dynamic sequences walking_xyz, walking_static, and walking_halfsphere on this dataset to confirm the algorithm's accuracy and robustness in actual indoor environments. Sequences 01, 02, and 03 on the KITTI dataset are also utilized for experiments to confirm the accuracy and resilience of the algorithms in actual outdoor scenes. The dataset comprises real image data that were gathered from urban, rural, and highway scenes. For this paper, a laptop running Ubuntu 20.04 with 16 GB of RAM serves as the experimental platform.
# Deep Learning for Audio Event Detection and Tagging on Low-Resource Datasets
## 1 Introduction
Most of these methods formulate the provided weak labels of the recordings into a multi instance learning (MIL) problem. However, for the methods using neural networks, none of the datasets used could be considered low-resource. Most of the datasets used either come from transcription/detection challenges (e.g., the IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE)) or online sources, such as Youtube or Xeno-Canto, which contain a large number of training data. Training a neural network to predict an audio transcription using a low-resource dataset can sometimes prove to be impossible. A network needs to have enough parameters to be able to predict all the different classes without ignoring any rare events, but also be small enough or have just the right amount of regularisation as to not overfit the limited amount of training data available. This becomes even harder when the task is a weak-to-strong prediction where the network needs to predict full transcriptions from weak labels. Unfortunately, there is no specific way of defining a network and type of training that ensures that a transcription will be predicted successfully. However, a full transcription task can be defined as multiple intermediate tasks of detection and classification that might be easier to train even when using a low-resource dataset. A similar approach is used to enhance the performance of automatic speech transcription [19] by using speaker diarisation [20, 21] and speaker recognition [22] systems together in order to structure an audio stream into speaker turns and provide the speaker's true indentity, respectively. However, these speech approaches are highly customised to characteristics of speech signals. Our method is focused on general audio with speech events considered just a single class amongst other audio events without distinguishing between individual speakers. In this paper, we propose a factorisation of the final full transcription task into multiple simpler intermediate tasks of audio event detection and audio tagging in order to predict an intermediate transcription that can be used to boost the performance of the full transcription task. For each intermediate task, we propose a training setup to optimise their performance. Finally, we train the intermediate tasks independently and in two multi-task learning settings and compare their results. The rest of the paper is structured as follows: Section 2 describes the way we factorise the transcription task into intermediate tasks and presents in detail our setup and network architectures. In Section 3, we propose three different training approaches for the intermediate tasks, two of which are implemented in a multi-task learning setting. In Section 4, we present our experiments and compare the results of each training approach. Finally, in Section 5, we discuss our findings and future research directions.
## 4 Experimental Setup  
### Corpora  
To evaluate the multilingual performance of our proposed model, the Berlin Database of Emotional Speech (EMO-DB) [68], the Chinese Emotional Corpus (CASIA) [69], and the Surrey Audio-Visual Expressed Emotion Database (SAVEE) [70] are combined together as input for the model, where the corpora represent German, Chinese and English, respectively. Since EMO-DB and CASIA are the most representative and influential discrete emotional corpora in German and Chinese, respectively, they are selected to facilitate subsequent comparison with other experiments. In contrast to the first two corpora, SAVEE is less frequently used in English SER studies than IEMOCAP, which is most widely used in English SER. However, considering that the large scale of IEMOCAP will affect the balance of the overall multilingual sample, SAVEE, which is relatively the same size as EMO-DB, is chosen in this study. These corpora contain data from professional actors who interpret relevant emotions based on the fixed scripts and have similar emotion labeling schemes. As in most of the previous studies, to maintain the consistency of emotions across the corpus, more concentration will be given to the recognition of four basic emotions (neutral, anger, happiness, and sadness), and we selected a total of 1439 utterances for the experiments. The basic information for each corpus is described in Table 1. Below, the relevant corpora are briefly introduced.
## 2 Materials and Methods
### Cell Lines  
All drug-sensitive cell lines, including HeLa (ATCC CCL-2), MCF-7 (ATCC HTB-22), HCT-15 (ATCC CCL-225), HCT-116 (ATCC CCL-247), Caov3 (ATCC HTB-75) and 184B5 (ATCC CRL-8799), were acquired from the American Type Culture Collection (Manassas, VA). The resistant counterpart MCF-7/Vin was developed through continuous exposition to vinblastine for nine consecutive years.
## 3 Methodology
### Data Collection  
The dataset used in this study was collected from a diverse set of GitHub repositories using the GitHub REST API. The objective of the data collection process was to gather detailed information about contributor behaviors, focusing on their PR activities, commit histories, and overall engagement within each project. The data were specifically curated  
Figure 1: Research Workflow Overview.  
to include repositories that adhere to the pull-based development model, which forms the foundation for studying contribution patterns in open-source projects. To ensure the quality and relevance of the dataset, the following criteria were used to select repositories:  
* Creation Year: Only repositories created in the year 2022 were included to ensure the data reflect recent and ongoing activities.
* Programming Language: The repositories selected were limited to those using Python as the primary programming language, enabling a more focused analysis of development behaviors within a specific technical domain.
* Contributor Size: Repositories with at least 3 contributors and no more than 100 contributors were selected. This range was chosen to capture repositories that are active but not overly large, where individual contributor behaviors could still be distinctly observed.
* Repository Popularity: Each repository had a minimum of 100 stars, indicating a certain level of popularity and community interest. This threshold helped filter out inactive or lesser-known projects.  
The data collection process yielded a dataset from a total of 672 repositories that met the specified selection criteria. These repositories were carefully chosen based on their creation date, programming language, contributor size, and popularity, ensuring a diverse sample of active projects within the GitHub ecosystem. In total, the dataset includes 892,664 activity logs, which consist primarily of commits and pull request interactions, reflecting the core contribution activities of the repositories. The dataset also captures the activities of 19,221 unique participants, where a participant is defined as any individual who has made at least one contribution or performed at least one activity within the project.  
Figure 1(a) presents a histogram showing the distribution of activity duration (measured in months) across all repositories. Our goal is to examine how collaboration patterns evolve over time. To achieve this, we categorized the repositories based on their activity duration and divided them into the following five groups:  
* Group 1: 0-5 months
* Group 2: 6-11 months
* Group 3: 12-17 months
* Group 4: 18-23 months
* Group 5: 24 months and beyond  
In order to ensure balanced representation across these groups, we employed a stratified sampling approach. We randomly sampled 50 repositories from each of the five groups, creating a stratified dataset that captures projects at various stages of their lifecycle. Figure 1(b) shows a density plot of the sampled repositories by group.  
Figure 2: Distribution and Grouping of OSS Project Activity Duration for Data Collection and Sampling.
## 3 Method  
As illustrated in Figure 1, the pipeline of proposed anomaly detection system in this article is as follows: The system first performs feature pre-processing on the original audio waveform, converting it into a logarithmic mel spectrogram, and then processes it through a network consisting of a generator containing an encoder \(E_{1}\) and a decoder \(D_{1}\), a discriminator, and an extra encoder \(E_{2}\). During the training phase, the network learns audio representations by optimizing the loss function. During the inference phase, the system calculates the anomaly score based on the output of network and makes an abnormal judgment on the audio by using a threshold.  
In this section, we will describe the details of these above-mentioned parts of our system.
## 2 Materials and Methods
### Physical Clogging Model  
Physical clogging occurs when suspended particles are deposited in the pore space during AGR. The model must satisfy the following assumptions:  
1. The porous medium should be homogeneous and saturated.  
Figure 2: Schematic of setup for column experiments.  
* [PERSON]'s law governs water migration.
* Dispersion effects are absent during the transport of suspended particles.
* No biochemical reaction occurs to change the characteristics of porous media or suspended particles.  
The attachment and detachment of suspended particles in porous media typically happen simultaneously, leading to transport and deposition. This kinetic process is governed by the following factors:  
\[\frac{\partial C_{S}}{\partial t}=\alpha C-\beta C_{s} \tag{2}\]  
where \(\alpha\) is the particle attachment coefficient (min\({}^{-1}\)), \(\beta\) is the particle detachment coefficient (min\({}^{-1}\)), and \(C_{S}\) is the deposited particle mass per unit pore space (kg/m\({}^{3}\)).  
Porosity decreases as the particles in the fluid are trapped and deposited in the porous medium. A linear relationship exists between porosity and particle retention. Effective porosity is expressed as follows:  
\[n=n_{0}-b\sigma C_{s} \tag{3}\]  
where \(n\) is the porosity of the medium, \(b\) is defined as the effective volume of the deposited particles divided by the solid volume of the deposited particles, and \(\sigma\) is the specific deposit (the occupied volume of the deposited particles per unit mass of pore volume, m\({}^{3}\) kg\({}^{-1}\)).  
Changes to the porosity influence permeability of the medium are described by using the Kozeny-Carmen equation [22; 23]:  
\[\frac{K}{K_{0}}=\frac{n^{3}}{\left(1-n\right)^{2}}\frac{\left(1-n_{0}\right)^{ 2}}{{n_{0}}^{3}} \tag{4}\]  
The initial and boundary conditions are included in the transport deposition model of the suspended particles, as follows:  
\[\begin{array}{c}-\frac{\partial}{\partial x}\left(C\cdot v_{x}\right)=\frac {\partial}{\partial t}\left(nC+C_{s}\right)\ 0\geq x\leq L,\ t>0\\ \frac{\partial C_{s}}{\partial t}=\alpha C-\beta C_{s}\ t>0\\ C\ |_{t=0}=0\ 0\leq x\leq L\\ C\ |_{x=0}=C_{0}\ t>0\end{array} \tag{5}\]
## 2 Stress Analysis Method of the Arch Ring Section
### Analysis of the Initial Stress State of the Arch Ring Section Considering Prestressing Effect
The bottom flanges:  
Figure 2:3  
**Condition 2:** Buckle cable tensioning condition  
Axial force:  
Figure 2:4  
Bending moment:  
Figure 2:5 Figure 2:6  
Eccentric distance:  
Figure 2:7  
The top flanges:  
Figure 2:8  
The bottom flanges:  
Figure 2:9  
where \(N_{i}\) is the axial pressure of the section \(i\) of the arch ring, \(M_{i}\) is the bending moment of the section \(i\) of the arch ring, \(M_{S_{i}}\) is the temporary bending moment load at the section \(i\), generally considered as hanging basket and formwork load, \(G_{i}\) is the self-weight load of the segment \(i\), \(e_{i}\) is the distance from the center of gravity of the net section to the point of the combined force of prestressing and normal reinforcement in the body, \(\sigma_{c_{i}}\) is the stress at the top flange of the arch ring of section \(i\) of segment \(i\), \(\sigma_{t_{i}}\) is the stress at the bottom flange of the arch ring of section \(i\) of segment \(i\), \(\alpha_{j}\) is the angle from the position of the \(j\)-th buckle cable and buckle point to the straight line of the shaped center point of the calculated section, \(\beta_{j}\) is the angle from the position of the center of gravity point of segment \(j\) to the straight line of the shaped center point of the calculated section to the vertical direction, and \(l_{j}\) is the length of segment \(j\), \(l\) is the length of the segment, \(h\) is the height of the section. Found from the formula analysis that the formula can solve the stress at the top and bottom flanges of each section of the main arch construction process in the forward direction and can solve the cable force of each section in the reverse direction under the premise of controlling the number of prestressing force unchanged. The formula can also solve the number of prestressing tendons in each section under the condition of controlling the cable force unchanged. Since the formula assumptions and the actual bridge conditions have a certain amount of deviation, the formula solution results are used as the proposed initial state of the structure, and then the precise analysis and adjustment of the stresses in the control section of the arch ring are carried out by the joint method of Matlab and ANSYS. The specific analysis and research process are detailed in the following.
###### Abstract  
Synthetic aperture radar interferometry (InSAR) has the advantages of a wide monitoring range, high density, high accuracy, and is not limited by weather conditions, providing a new technical means for landslide research. On 21 August 2021, a landslide occurred in Zhonghai Village, Hanyuan County, Ya'an City, Sichuan Province, China, resulting in nine deaths. For the research area, the Small Baseline Subsets InSAR (SBAS-InSAR) technique was used to extract the spatiotemporal evolution characteristics before the landslide occurred (from 16 January 2019 to 22 May 2020), and the height difference before and after the landslide occurrence was extracted using unmanned aerial vehicle photogrammetry, high-resolution remote sensing images, and digital elevation model data. By analyzing seismic activity, human activities, and rainfall in the study area, the main causes of landslides were discussed. This study not only reduces the losses caused by landslide disasters but also provides a scientific basis and technical support for local governments' disaster prevention and mitigation work.  
InSAR; landslide; SBAS; spatiotemporal evolution; height difference Article
# An Adaptive Symmetrical Load Balancing Scheme for Next Generation Wireless Networks
## 2 Related Work
### Load Balancing in Data Plane  
Distributed load balancing in wireless fidelity (Wi-Fi) networks has been extensively studied in the prior decades [28; 29]. A dynamic distributed load-balancing algorithm using a specific affiliation control approach is designed [30]. The researchers expressed the distributed stabilizing complication in a comparable optimization structure by utilizing stochastic approach [31]. It is noteworthy that only AP data are utilized in the aforementioned design. In the SDN design, the network controller acquires the global understanding of the network, opening up innovative opportunities for scalable administration. Not long ago Wi-Fi networks have expanded with SDN capabilities [32]. Researchers have integrated Mininet with NS-3 to develop an Open Net simulator for managing simulations for SD-Wi-Fi [33]. The Mininet-Wi-Fi tool was equipped to support genuine network assessment for load balancing at data plane [34]. DCF access protocol in this emulator made it more difficult to conduct studies on uplink network throughput when several users were sharing a single channel. SD-Wi-Fi prototype systems are typically built using software-defined wireless network testbeds [35]. It should be noted that the testbeds are only suitable for implementing network layout with small-scale experiments and are not applicable for highly dense Wi-Fi network scenarios with a significant number of users and APs.  
Several investigations have been carried out to enhance the performance of SD-Wi-Fi [36]. The researchers constructed the Ethanol experimentation platform to execute a load-sensitive admission control design by amalgamating information from both the client side and AP side [37]. The work ensured the potential of distributing the workload among access points in SD-Wi-Fi. The researchers also presented load distribution techniques for data plane in SDN but their research was restricted to few APs [38; 39]. The efficiency of load distribution techniques is yet to be evaluated for SD-WiFi. In this paper, we are inspired to examine the workload distribution issues for high density SD-WiFi to facilitate the adaptive network environment.
## 4 Discussion  
### Gradually Reversed Rural Migration  
Since the reform and opening-up in 1978, the Chinese government has implemented an unbalanced development strategy tilted towards the eastern region, prioritizing the investment of limited resources in this area, which in turn led to the development of the whole country through the development of the eastern region [66; 67]. This strategy has shaped China's unbalanced pattern of economic development and dictated a steady flow of population and other factors of production to the eastern region, especially the Yangtze River Delta, Pearl River Delta, and Beijing-Tianjin-Hebei [68]. In this study, we confirm the large-scale interprovincial migration in China over the past two decades, and the main destination has been the eastern coastal region. On the other hand, the rural population mainly migrates to urban areas for better production and living conditions due to the urban-rural dual structure and its resulting urban-rural gap [69; 70; 71].  
Although socio-economic development has driven interprovincial rural migration to continue to rise, its growth rate has shown a clear downward trend, and the proportion of interprovincial migration within total rural migration has been declining. In the case of migrant workers, who account for the vast majority of rural migration, data from the National Bureau of Statistics (NBS) show that the proportion of interprovincial migrant workers in the total number of migrant workers has declined from 51.2% in 2009 to 38.2% in 2023. In addition to being influenced by major national strategies, this change is also closely related to the law of industrial gradient transfer. The former mainly refers to China's vigorously implemented regional coordinated development strategy, which aims to form a new pattern of mutual promotion, complementary advantages, and common development among the eastern, central, and western parts of the country through sound market mechanisms, cooperation mechanisms, and support mechanisms [72; 73; 74]. The latter focuses on the central and western regions actively undertaking industrial transfers by leveraging their abundant natural resources, low factor costs, and large market potential, effectively promoting their industrialization and urbanization [75; 76]. Meanwhile, industrial transformation and upgrading in the eastern region have resulted in the reduction in a large number of labor-intensive jobs, leading to low-skilled migrant workers being continuously pushed out of the labor market [77; 78; 79; 80]. Additionally, economic slowdown has reduced employment opportunities, causing a significant number of migrant workers to return to their hometowns [81]. Based on the above trends and influencing factors, it is necessary to further explore the profound impacts of rural migration on economic and social development and propose targeted policy recommendations to address the current challenges.