esa-sceva/satcom-chunk-collection huggingface dataset:
# Implementation and Evaluation of Activity-Based Congestion Management Using P4 (P4-ABC)
## 1 Introduction  
Future mobile networks like 5G consist of small cells that issue large traffic rates with high fluctuations [1]. As quality of service (QoS) is required, economic provisioning of the transport network is a challenge. Datacenter networks and residential access networks of Internet service providers (ISPs) have similar requirements [2; 3; 4].  
To avoid QoS degradation, scalable bandwidth-sharing mechanisms for congestion management may be helpful, but they need to be simple and effective. That means, light users should be protected against overload caused by heavy users while avoiding per-user signaling and information within the transport network for complexity reasons.  
In [5], activity-based congestion management (ABC) was initially suggested for that purpose. It implements a domain concept where edge nodes run an activity meter that measures the traffic rates of users and add activity information to their packets. Forwarding nodes leverage activity-based active queue management (activity AQM) which uses this information to preferentially drop packets from most active users in case of congestion. In [6], ABC has been proposed in its current form and extensive simulation results have demonstrated that ABC can effectively protect light users against heavy users to such an extent that a single TCP connection from a light user does not significantly suffer in the presence of congestion caused by an aggressive non-responsive traffic stream of a heavy user.  
As ABC requires additional header information and new features in edge nodes and forwarding nodes, it cannot be configured on conventional networking gears. However, advances in network programmability support the definition of new headers and node behavior. The network programming language P4 is a notable example [7].  
In this work, we report about a P4-based prototype for ABC. It demonstrates the technical feasibility of ABC while revealing challenges and giving hints for the enhancement of P4 support on switches. Furthermore, we present experimental results which confirm the simulative findings in [6].  
The remainder of the paper is structured as follows. Section 2 briefly reviews related work. Section 3 explains the ABC concept in detail. Section 4 gives an introduction to SDN and P4. Section 5 describes the P4-based ABC implementation. Section 6 presents our evaluation methodology and reports experimental results. Finally, Section 7 concludes this work.
## 4 Discussion
### Emerging Recommendations  
Emerging recommendations for SASRs, a specialized subset of care robots, are derived from systematic reviews [11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27]. Designed to address patients' emotional, cognitive, and social needs, particularly among the elderly and those with special needs, SASRs present distinct opportunities and challenges.  
A key recommendation is an empathy-driven design, ensuring SASRs can recognize and respond to human emotions. Studies by [PERSON] et al. [11] and [PERSON] and [PERSON] [13] highlight the importance of emotional responsiveness in fostering meaningful interactions, reducing loneliness [11], and supporting cognitive functions in dementia patients [19].  
User-centered design is equally critical. SASRs must be adaptable to diverse users, from children with autism [14] to elderly patients with early-stage dementia [19] and individuals undergoing medical treatments [15]. Personalization enhances their therapeutic impact by addressing cognitive, emotional, and physical variations.  
Cybersecurity is another fundamental aspect. As [PERSON] and [PERSON] [28] emphasize, SASRs handle sensitive patient data, necessitating stringent security measures to prevent breaches and ensure safe operation. [PERSON] et al. [29] further highlight the need for cybersecurity training among healthcare professionals to mitigate risks in clinical environments.  
Interdisciplinary collaboration strengthens SASR development. Insights from healthcare, engineering, psychology, and ethics help create socially and ethically responsible robots [17; 19]. Ethical considerations, particularly \"algorethics\" (fairness, transparency, and accountability in AI systems), as discussed by [PERSON] et al. [30], are vital for ensuring trust and bias-free interactions.  
Finally, gradual integration into clinical practice, supported by standardized guidelines and rigorous testing, is essential. [PERSON] et al. [31] propose consensus conferences as a means to establish best practices and protocols for SASR deployment.  
Overall, SASR development requires a holistic approach combining empathy-driven design, user-centered customization, cybersecurity, interdisciplinary collaboration, and structured clinical integration. These principles will ensure that SASRs effectively support patients while addressing ethical, technical, and practical challenges. Table 6 summarizes these emerging recommendations. Table 5 reports the emerging recommendations.
# A Study on Identification of Development Status of MASS Technologies and Directions of Improvement
## 2 IMO MASS RSE and Human Element Issues  
Technological development is a key element to the realization of MASSs, but prior to this, it must meet the requirements of international instruments in order to enable the international navigation of the MASS. Basically, autonomous vessels have the initial expectation, where they have to be at least as safe as the most advanced manned ships [17]. To confirm this, MSC 98 decided to start the MASS RSE [18], and the two-year work will be completed by the MSC 102 nd session in 2020 [19]. The objective of the IMO RSE for MASS is to assess the degree of autonomy that may affect existing regulatory frameworks in order to address MASS operations. To facilitate the process of the RSE, the degrees of autonomy were categorized into four phases at MSC 99, as shown in Figure 1[8]. It should be noted that MASS can operate in more than one degree of autonomy during a single voyage. Figure 1: Degrees of autonomy. The MASS RSE was divided into two steps, and the first step was completed at the MSC 101 meeting in 2019. In the first step of RSE, the contents of 14 international instruments under MSC were analyzed and identified as the following four categories, which had been decided during the MSC 99 th session. 1. Apply to MASS and preclude MASS operations; or
2. Apply to MASS and do not preclude MASS operations and require no actions; or
3. Apply to MASS and do not preclude MASS operations but may need to be amended or clarified, and/or may contain gaps; or
4. Have no application to MASS operations. In the second step, an analysis has been conducted to determine the most appropriate way of addressing MASS operations, taking into account the human element and technology and operational factors. The second step of MASS RSE will be completed by the 102 nd MSC session in 2020. In the second step, the contents of the 14 international instruments under MSC were analyzed and identified as the following four categories. 1. Equivalences as provided for by the instruments or developing interpretations; and/or
2. Amending existing instruments; and/or
3. Developing new instruments; or
4. None of the above, as a result of the analysis. As there are limitations in handling the results of all contents of MSC-related instruments, the results of the MASS RSE for the Standards of Training, Certification and Watchkeeping Convention (STCW 1978, as amended)--as one of the key instruments of IMO in regulating the minimum qualification for seafarers--are as follows [9]. The most important aspect of the MASS RSE of the STCW 1978 Convention is its application [12]. According to article 3 (Application) of the STCW Convention, it is stipulated that the convention only applies to ships with seafarers on board [20]. Based on this understanding, the results of MASS RSE, depending on autonomy degrees for the STCW convention, are as listed in Table 1.
# Implicit Algorithm of the SBSP-R Model for Predicting the Non-Unique Critical State of Soils
## 5 Validation of the Implicit Algorithm of the SBSP-R Model
### Kaolin Clay  
Triaxial compression tests on Kaolin Clay are adopted for verifying the implicit algorithm of the SBSP-R model with parameters shown in Table 4, the values of \(M\), \(\lambda\), \(\kappa\), and \(\mu\) are set according to the thesis [35], and the values of \(\varepsilon_{r}\), \(c_{b}\), and \(r\) are determined according to Section 4.1 and the test results from the thesis.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \(M\) & \(\lambda\) & \(\kappa\) & \(\mu\) & \(\varepsilon_{r}\) & \(c_{b}\)/\(k\)Pa & \(r\) \\ \hline \(1.05\) & \(0.14\) & \(0.05\) & \(0.3\) & \(0.03\) & \(0\) & \(2.9\) \\ \hline \end{tabular}
\end{table}
Table 4: Material parameters for Kaolin clay.  
Figure 9: Comparisons between model prediction and test results (**a**) OCR = 10, (**b**) OCR = 4, (**c**) OCR = 2, (**d**) OCR = 1.5, (**e**) OCR = 1, (**f**) stress paths corresponding to different OCR.  
As shown in Figure 10, the critical state of Kaolin Clay is well captured by the implicit algorithm of the SBSP-R model with \(r=2.9\), compared with \(r=2.0\) (MCCM) or \(r=2.7\) (CCM). That is, point A\({}^{\prime}\) corresponding to \(r=2.9\) is close to the final stress state of Kaolin clay, while point A\({}^{\prime\prime}\) corresponding to \(r=2.0\) is far away from the final stress state (see Figure 10f). This is because \(r=2.9\) is determined by the triaxial compression test reflecting the real \"distance\" between CSL and ICL. While \(r=2.0\) and \(r=2.7\) are, respectively, taken from MCCM and CCM they cannot reflect the real \"distance\" between CSL and ICL of Kaolin Clay. Besides, the implicit algorithm requires less time and shows high accuracy in capturing the critical state of Kaolin clay, especially when the spacing ratio \(r\) is large than \(2.5\).  
Figure 10: Comparisons between model prediction and test results (**a**) OCR = 1.2, (**b**) OCR = 2, (**c**) OCR = 5, (**d**) OCR = 8, (**e**) OCR = 12, (**f**) stress paths corresponding to different OCR.
## 2 Model and Data
### Reliability Series Model with Weibull-Distributed Components  
Suppose that the system consists of \(m\) independent components connected in series, and the life of each component follows a 2-parameter Weibull distribution. The reliability function of component \(i\) can be express as  
\[R_{i}(t)=\exp\left[-\left(\frac{t}{\beta_{i}}\right)^{\alpha_{i}}\right]\text {, }t\geq 0,\alpha_{i}>0,\beta_{i}>0,i=1,2,\cdots,m \tag{1}\]  
where \(\alpha_{i}\) and \(\beta_{i}\) represent the shape parameter and characteristic life for component \(i\), and \(t\) is the mission time.  
In this work, the main interest is in the reliability and reliable life of a series system. According to the series model, the system reliability function is  
\[R(t)=\prod_{i=1}^{m}R_{i}(t)=\exp\left[-\sum_{i=1}^{m}\left(\frac{t}{\beta_{i }}\right)^{\alpha_{i}}\right] \tag{2}\]  
For a given reliability \(R\in[0,1]\), the reliable life of the system, denoted by \(t_{R}\) (indicating that on average 100R% of the population will not fail before \(t_{R}\)), is the only root of the following equation:  
\[R=\exp\left[-\sum_{i=1}^{m}\left(\frac{t_{R}}{\beta_{i}}\right)^{\alpha_{i}} \right]\text{, }t_{R}>0 \tag{3}\]  
In particular, if the \(m\) components are identical, i.e., \(\alpha_{i}=\alpha_{0}\) and \(\beta_{i}=\beta_{0}\), \(i=1,2,\cdots,m\), Equation (2) simplifies to  
\[R(t)=\exp\left[-m\left(\frac{t}{\beta_{0}}\right)^{\alpha_{0}}\right] \tag{4}\]  
The life of a series system consists of independent and identically distributed components following a 2-parameter Weibull distribution, which is well known as the weakest link model. In contrast, when the components differ, deriving an explicit expression for the system's lifetime distribution becomes nearly impossible. For mathematical convenience, many authors (e.g., [23; 24; 25]) have assumed that the series system failures are asymptotically exponential, regardless of the component lifetime distributions. This assumption lacks sound physical explanation and theoretical support.
# Key Recovery for Content Protection Using Ternary PUFs Designed with Pre-Formed ReRAM
## 4 Content Protection with Ternary Unclonable Functions
### Decryption of the Digital Files by the Client Device  
To trigger the read cycle, the server communicates the missing piece of the challenges, _ID Access_, which the client device combines with **T**, see Figure 6. The one-way unclonable function generates the responses **K'** from the PUF, providing the necessary information to the client device to generate the key **K** from **K'**\(\leftarrow\)**V** (**T**, _ID Access_).  
Figure 5: Block diagram of the encryption of digital file M. The challenges (**T**, _IDA Access_) generate **K** from the image of the PUF. **M** is encrypted with **K**. The client device stores **T**, ciphertext C, and message digest **H(K)**. The server keeps _ID Access_ in a look-up table.  
The search engine, similar to the one described in Section 3.2 for session key recovery, recovers the initial responses **K** from **K'** and the message digest **H(K)**. This allows access to the digital file **M** by decrypting the ciphertext **C** = **E(M,K)**. Any symmetrical encryption scheme such as AES or DES can be considered for use in this method. We used AES-256 in this study. A commercial application example is where a service provider communicates **IDAccess** after receiving a payment from a client's device for a digital file such as a movie. Other practical examples are suggested in the summary section of this paper (Section 7).
## 1 Introduction:
### Motivation, Objective, and Innovation Contribution  
Considering the limitations of the measurement devices in the present BMS, many external features of the battery are hard or even impossible to obtain in actual operation. Moreover, the applications of the above-mentioned methods are also limited by the computational capability of a real BMS. To address the above drawbacks of the methods described in the literature, this article proposes two states of health estimation techniques for Li-ion batteries, and then, another technique has been developed and compared to show the robustness of the proposed technique in this field. In this article, the proposed method requires only two external states (voltage and current), making the method suitable for EV applications. The key contributions of this article are summarized as follows:  
* Employing an input time-delayed strategy to handle dynamic information of system.
* The Adaptive Neruo-fuzzy Inference System (ANFIS) and group method of data handling (GMDH) techniques are employed to analyze the relational grade between the SOH and selected features.
* Developing two data-driven frameworks to estimate the SOH. This article utilizes the fuzzy C-means clustering algorithm to tune and adjust the ANFIS parameter in advance, to create the initial rules.
* Accurate and effective validation of the framework in comparison to recently published articles and other methods.  
The paper is organized as follows: in Section 2, a brief introduction is done regarding the group method of data handling and adaptive neuro-fuzzy inference system; in Section 3, both the discussion and comparisons between the proposed techniques are provided. The outcome of the article is summarized and concluded in Section 4.
# Coexistence in Wireless Networks: Challenges and Opportunities
## 3 Types of Wireless Communication
### Zigbee and Other Low-Power Wireless Networks  
Zigbee networks are a kind of low-power wireless communication system designed for controlling and transferring data over short distances [43; 44]. Zigbee networks are characterized by their mesh topology, which enables devices to communicate directly or relay data through intermediate nodes, improving range and reliability. It supports up to 65,000 nodes, making it highly scalable for large networks. Zigbee is frequently used in situations where devices need to interact wirelessly while using the least amount of power, which makes it suitable for use in sensor networks, home automation, and industrial control [45]. Some other examples of similar low-power networks are Z-Wave and Thread [46; 47].  
Other low-power wireless networks, such as Z-Wave, Long Range Wide Area Network (LoRaWAN), and Thread, complement Zigbee by targeting similar use cases with unique strengths. Z-Wave operates in the sub-1 GHz band, reducing interference and extending the range, making it popular in smart home devices. LoRaWAN is designed for long-range, low-power communication, often used in industrial IoT and smart city applications. Thread, like Zigbee, leverages IEEE 802.15.4 but focuses on IPv6-based communication  
Figure 3: Example of a LAN for an IOT system [37].  
for seamless internet integration. These networks prioritize low power consumption to extend the battery life of the connected devices, making them essential for IoT deployments. Each protocol caters to specific requirements, from Zigbee's robust mesh networking to LoRaWAN's extensive coverage, ensuring flexibility in low-power wireless communication across diverse environments. Together, they enable scalable, energy-efficient solutions for smart technologies.
# Success Prediction of Crowdsourced Projects for Competitive Crowdsourced Software Development
## 2 Literature Review
### Success Prediction on the CCSD Platform  
[PERSON] et al. [4] addressed the inefficiency in software development by proposing a machine learning model to predict project success, stemming from the challenges of low submission rates and high failure rates in competitive markets. Their novel approach, leveraging a modified keyword ranking algorithm, achieved an average precision of 82.64%, recall of 86.16%, and F-measure of 84.36% in predicting project success using CNN classifiers. Implementing their model can potentially save companies significant time and effort in assessing project feasibility, serving as a benchmark for future algorithmic enhancements. [PERSON] et al. [33] explored how network positions, like central and structural hole positions, influence success in crowdsourcing challenges using data from 99 designs. They found that occupying these positions affected success in an inverted U-shaped pattern among the 2479 members studied, impacting access to knowledge and information flows. Their research enriches the crowdsourcing literature, offering insights for both crowd members and managers organizing such competitions.
## 6 HydroSAR Cloud Computing Environment  
The HydroSAR processing algorithms presented in Section 4 are implemented in the Amazon Web Services (AWS) cloud (Figure 10) to enable rapid processing across large spatial scales [49]. This cloud approach provides an innovative, open, scalable, and sustainable processing solution for the HKH, being ready for continued operation by our in-region partners at ICIMOD. To achieve a cloud architecture that is both performant and cost effective, we partnered with NASA's Alaska Satellite Facility (ASF), which has extensive cloud development expertise from their preparations for the upcoming NASA-ISRO SAR (NISAR) mission.  
Figure 10: Cloud architecture of the HydroSAR service, including (1) integration with ASF’s cloud-based archives; (2) HydroSAR product generation in the cloud using ASF HyP3; (3) temporary product storage in AWS S3; and (4) data delivery to end users via an image service.  
Figure 9: The HydroSAR inundation data download tool under the RDS portal. Data can be selected by country, date range, or user-drawn AOI.  
As shown in Figure 10, HydroSAR utilizes and seamlessly integrates with two existing ASF-supported services to provide a streamlined user experience: ASF's cloud-based data archive and ASF's Hybrid Pluggable Processing Pipeline (HyP3) [52]. The resulting service is able to produce from hundreds to thousands of higher-level products an hour. ArcGIS image services are used to allow for automatic integration of data products into the HydroSAR web portal (Section 5) and for use within desktop GIS environments at HKH partner organizations.  
The cloud architecture of HydroSAR includes the following components (Figure 10):  
1. **Integration with ASF's cloud-based archives** is achieved by co-locating the HydroSAR services with ASF's archives in AWS region us-west-2. This design reduces data movement and enables rapid in-region data access without requiring data downloading.
2. **Cloud-based HydroSAR product generation** is facilitated by ASF HyP3, a cloud-scaling service allowing science algorithms to run automatically from regional to global scales. Mature HydroSAR workflows are integrated into HyP3 using Docker containers [53] and are run automatically whenever new SAR data over an area of interest hits the ASF archive.
3. **HydroSAR cloud storage** is provided in the form of an AWS S3 storage bucket. HydroSAR products are deposited in this bucket immediately after product generation and stored temporarily until pickup by ICIMOD.
4. **Product delivery** to end users is facilitated by ICIMOD. Using a cron job scheduler utility, ICIMOD fetches new HydroSAR products on a daily basis from the project's maintained S3 bucket for inclusion into their local database. As discussed in Section 5, ICIMOD serves out HydroSAR data to its end users via an image service-supported web interface.  
The HydroSAR service has been using this implementation since 2022 to generate flood hazard information across the HKH and serve these products to end users in Nepal, Bangladesh, Bhutan, and northern India.
## 3 Results  
The repeated selection of a handful of TI hyperparameters--%B (\(n=58\)), EMA (\(s=6\)), ROC (\(n=49\)), etc.--by the grid-search optimisation, points to the prevalence of real and identifiable behavioural biases across the Belgian DAM. Our results, presented in Tables 1 and 2 as well as Figures 3-5, demonstrate that TI features can help in the identification of these biases by adding explanatory power and significantly boosting the regression accuracies of linear, ensemble, and deep models. On average, per the results in Table 1, the _best TIs_ reduce forecast RMSEs by 3.28%, MAEs by 3.32%, and increase PCCs by 1.96%. These empirical metric improvements are statistically significant at a 1% level, yielding DM values above 2.33, in 9/10 cases.  
Summarising TI performances across the ML models: the ROC increased nine models' accuracies; EMA, 8; SMA, 7; MOM, 6; 'Series', 4; 'Signal', 4; 'Histogram', 4; MSD, 4; %B, 4; COPP, 4; TSI, 3; and Bandwidth, 1. Broadly, the Bandwidth was found to be the worst-performing TI, while the ROC and the EMA were found to be the best. In Sections 3.1-3.3, we further breakdown the performance impacts of TIs; determining optimal indicators for each of the three ML model groups.  
Figure 4: Heatmaps showing monthly means (**left**), standard deviations (**centre**), and skewness (**right**) of the test set prices (\(\boldsymbol{\xi}\)/MWh). These metrics are calculated for each hour denominated DAM contract, to highlight both the periods and products exhibiting higher than average price, volatility, and skewness. Note that the colour bars are centred at each metrics mean value to facilitate our analysis.  
Figure 5: Heatmaps showing the %RMSE for each month of the test set and each hour denominated DAM contract, resulting from including EMA (\(s=2\)), MOM (\(n=58\)), and ROC (\(n=49\)) features with HR, AB, and 2 CNN models respectively. Negative values (**green**) indicate a reduction in forecast error, while positive values (**red**) indicate an increase in forecast error.  
Figure 3: Scaled univariate kernel density estimates of daily model forecast RMSEs. **Black** curves present the distribution of forecast errors with the _best TI_ features, while **grey** curves present benchmark model errors without TIs.