esa-sceva/satcom-chunk-collection huggingface dataset:
# Data-Driven Decision Support in SaaS Cloud-Based Service Models
## 5 Discussion
### Insights and Strategic Recommendations

Table 6: _Cont._Other research [13; 23] highlighted that selecting the right machine learning model is crucial for effective churn prediction highlighting the trade-offs between different approaches. For example, the authors in [13] compared decision trees and random forests, finding that decision trees excel in recall, making them ideal for minimizing missed churn risks, while random forests offer better F1-scores, which may be preferable when balancing precision and recall. Meanwhile, research [23] achieved near-perfect predictive performance with an AUC of approximately 0.99 but warned against neural networks due to their tendency to degrade over time without frequent retraining. On the operational side, authors in [16] highlighted the computational cost of XGBoost, noting that it produces 335% higher emissions than logistic regression, and recommended cloud-based solutions for scalability and efficiency. Measuring the financial impact of churn ensures that retention efforts align with business objectives. The work presented in [38] demonstrated how CLV predictions can optimize budget allocation, ensuring marketing spending targets high-value users. Another key insight comes from [29], which found that organic search ads outperformed email campaigns in driving conversions, cautioning against overly aggressive retention tactics that may alienate users. Finally, the importance of addressing computational demands and sustainability in SaaS systems was highlighted by studies [14; 16; 19; 36]. Key recommendations included optimizing models for cloud deployment, minimizing carbon footprints, and ensuring scalable infrastructure for real-time, high-volume applications. For instance, study [14] pointed out the significant computational requirements of deep learning models, particularly for large firms. In addition, the authors in [19] focused on designing models suited for cloud-based SaaS environments, while in study [16] the authors evaluated the environmental impact, specifically the carbon cost, of model training. To support scalability, authors in [36] recommended hybrid computing approaches, especially for high-demand systems like billing.
# Carrier Phase Dual One-Way Ranging Method Based on a Frequency Hopping Signal
## 2 FH Dual One-Way Ranging in ISL  
### The Theory of Dual One-Way Ranging  
The structure of the DOWR model is as follows (Figure 1).  
Two satellites are equipped with ISL transmitters and receivers and measure the pseudorange through a local receiver. The time synchronization, clock difference calibration, true range calculation, and data transmission calculations are achieved by exchanging the pseudorange. Each satellite is capable of independently modulating, transmitting, receiving, and demodulating data and can achieve a single-channel pseudorange measurement function through the transmitted spread spectrum code [23; 24]. The signal delays measured using satellite A and satellite B are shown in (1):  
\[\left\{\begin{array}{l}T_{\rm A}=\tau_{\rm AB}+\tau_{\rm clock}+\tau_{\rm Bt}+ \tau_{\rm Ar}\\ T_{\rm B}=\tau_{\rm AB}-\tau_{\rm clock}+\tau_{\rm Ar}+\tau_{\rm Br}\end{array}\right. \tag{1}\]  
\(T_{\rm A}\) and \(T_{\rm B}\) are the time delays measured using satellite A and satellite B, respectively. \(\tau_{\rm AB}\) is the true time delay, \(\tau_{\rm clock}\) is the clock difference between satellites A and B, and \(\tau_{\rm At}\) and \(\tau_{\rm Bt}\) are the transmit channel delays. \(\tau_{\rm Ar}\) and \(\tau_{\rm Br}\) are the receive channel delays. To calculate \(\tau_{\rm AB}\), (1) can be expressed as (2).  
\[\left\{\begin{array}{l}D=\frac{1}{2}[(T_{\rm A}+T_{\rm B})-(\tau_{\rm Ar}+ \tau_{\rm At}+\tau_{\rm Br}+\tau_{\rm Bt})]\cdot c\\ \Delta t_{\rm clock}=\frac{1}{2}[(T_{\rm A}-T_{\rm B})+(\tau_{\rm Ar}-\tau_{\rm At }+\tau_{\rm Bt}-\tau_{\rm Br})]\end{array}\right. \tag{2}\]  
Because \(\tau_{\rm At}\), \(\tau_{\rm Bt}\), \(\tau_{\rm Ar}\), and \(\tau_{\rm Br}\) are measured via channel calibration, we assume that they are known. Therefore, each satellite can calculate the true distance \(D\) and clock difference \(\Delta t_{\rm clock}\).
# Water Jacket Systems for Temperature Control of Petri Dish Cell Culture Chambers
## 2 Materials and Methods
### Experimental Prototype  
A custom-built experimental prototype was fabricated to verify the accuracy of numerical simulations under reference static and dynamic thermal conditions. The experimental prototype included a (i) 35 mm polystyrene Petri dish for storing the sample, (ii) a 0.5 mm thick rubber washer surrounding the Petri dish, (iii) a water jacket for controlling the temperature of the sample inside the Petri dish, (iv) a water reservoir equipped with heating elements for regulating the temperature of the water recirculating through the system, (v) a thermocouple for monitoring the temperature of water inside the water reservoir, (vi) a peristaltic pump along with Teflon tubing for recirculation of hot water, and (vii) an infrared camera for monitoring the temperature across the Petri dish.  
The water jacket was made of aluminum with an external diameter of 55 mm, an internal diameter of 36 mm, a height of 10 mm, and a thickness of 1 mm, as presented in Figure S1. The water jacket was equipped with inlet/outlet ports with an internal diameter of 3 mm to recirculate water. The water reservoir consisted of a stainless steel cylindrical container with an internal volume of 200 mL. A Nichrome heating element with an electrical resistance of 13.77 \(\Omega\)/m was immersed in the water  
Figure 1: Schematic of the water jacketed system.  
reservoir. Applying a DC current of 3 A to the wire resulted in the water inside the reservoir reaching a stable temperature of 55 \({}^{\circ}\)C within 30 min. The temperature of the water inside the reservoir was constantly monitored using a K-type thermocouple (TC Measurement & Control Pty Ltd). The water was recirculated through the system using a peristaltic pump with an integrated geared motor (Welco, WPM1-P3-AA). Operating the pump at 12 V resulted in a flow rate of 60 mL/min. The pump was interfaced with the water jacket using Teflon tubes (Bohlender(tm) PTFE tubing, ID = 4.35 mm, OD = 6.35 mm). The temperature of the Petri dish and the water jacket was monitored in real-time using a thermal imaging camera (FLIR systems, Thermo Vision A320, Sweden) interfaced with ThermaCAM researcher software.
## 2 Related Work
The Routing by Energy and Link quality (REL) proposes an adaption of Ad-hoc On-demand Distance Vector (AODV) aiming to increase the network reliability and power efficiency. The proposed protocol uses a _weakLinks_ mechanism to identify links with low quality across a path. In the route creation process, paths with a high number of _weakLinks_ are avoided. The choice of the best route also considers the residual energy of the nodes and hop count. In all the studied scenarios, REL was able to outperform the AODV regarding latency, packet delivery ratio, and network lifetime. [PERSON] et al. [21] present a solution based on smartphones to allow the interoperability of IoT devices. The authors propose an architecture in which a smartphone aggregates several communications interfaces to work as a gateway for different technologies. The proposal is software-based and uses only the default implementation of the considered standards. Thus, no improvements were made at the routing layer. A specific testbed was deployed to evaluate the solution in terms of memory, CPU, and energy consumption. In [22; 23], the authors studied solutions for gateway discovery in mobile ad hoc networks (MANETs). The proposals are based on the usage of periodical control messages and consider networks that have mobile nodes or are equipped with an IEEE 802.11 communication interface, which is not the most appropriate for IoT low-power devices. The performance of the solutions was evaluated in terms of packet delivery ratio and end-to-end delay. Considering the limitations of the current literature and the requirements of IoT low-power networks, this work proposes a new mechanism for LOADng that will allow it to search Internet-connected nodes in a dynamic and on-demand manner. Moreover, the proposed approach can improve normal data traffic among the nodes, enabling the network to become more energy efficient and reliable.
# Investigating Supercomputer Performance with Sustainability in the Era of Artificial Intelligence
## 4 Methodology  
The detailed procedure of the methodology adopted in this study is presented in this section. It outlines the systematic stages as shown in Figure 1 followed to achieve the research objectives, including data source selection, data collection, and data analysis strategies. Each component of the methodology is carefully described to ensure transparency and reproducibility. The section also highlights the statistical tools and techniques employed.  
By detailing the stages, this section provides a comprehensive understanding of how the study was carried out.  
The three most relevant HPC ranking lists in the HPC community are as follows: Top500, Green500, and Graph500 [44; 49]. This study focuses on Top500 and Green500 to cover the scope of performance and sustainability. Supercomputers must first be listed in TOP500 before qualifying for the Green500 ranking, encouraging designers to prioritize both high performance and EE.  
The study utilized TOP500 and Green500 as data sources because they are well-established and widely accepted reference points for information within the HPC community [30; 32; 34]. The primary source for data collection is the official TOP500 website, and the data is publicly available. The TOP500 releases rankings of the 500 most powerful supercomputers worldwide two times a year, in June and November. The most recent data at the time of this investigation was from November 2024. For this study, we focused on the top 10 supercomputers for each year over a span of five years. This approach can account for variations in rankings driven by technological advancements and the deployment of new systems over time. The study considered five years of data to provide a recent perspective in this era of AI. Data on Rmax (PFlop/s), Repeak (PFlop/s), cores, and power consumption (kW) for the top 10 supercomputers in terms of performance were collected from June 2020 to November 2024, resulting in a total of 10 lists.  
Unlike TOP500, Green500 ranks the 500 most energy-efficient supercomputers globally, promoting the balance of performance and sustainability. The data is released simultaneously with the TOP500 rankings. Data on the top-EE supercomputers were extracted for the same period, from June 2020 to November 2024. The variables collected include cores, power consumption (kW), Rmax, Repeak, and EE rating (EER) (GFlops/watts). The data focuses on the top 10 supercomputers in terms of EE, as the study addresses both top performance and sustainability. This approach creates two groups of supercomputers: the top-performing category and the top-EE category, enabling an in-depth analysis of both performance and sustainability. In this study, the format MonthYear[index measure] is used for clearer representation during analysis. For example, Nov23E refers to the performance of the top 10 supercomputers in terms of EE in November 2023, while June23P represents the top 10 supercomputers in terms of performance in June 2023.
## 3 Model Construction and Parameter Analysis of Space-Based Information Network
### Construction of Space-Based Information Network Model  
Nowadays, SBIN has become an important means for economic countries and military powers to obtain and transmit useful information. It has greatly improved the ability of information sharing and joint command and control, and accelerated the transformation from an information advantage to a decision-making advantage. It can give full play to the subjective initiative of the integration of space and earth, and comprehensively enhance the coordinated development of countries and organizations. SBIN is a network that uses spatial advantages to obtain various kinds of information. It is mainly composed of a GEO backbone network, MEO functional architecture network, and LEO satellite networking and ground terminal access unit. The composition of the SBIN system is shown in Figure 1.  
Figure 1: Composition of the space-based information network system.  
As can be seen from the composition of the whole SBIN, its internal units are numerous and their connections are intricate. SBIN is mainly composed of four layers, namely the Resource Layer, Service Layer, Function Layer and Mission Layer. The resource layer is mainly composed of GEO, MEO, LEO, and ground connection terminal units and their connection relations. It is the basis from which SBIN performs tasks. Different service businesses can be realized by encapsulating different components. The service layer mainly carries out a series of service functions, such as visible imaging observation, microwave communication and mapping data processing. It is the basis from which SBIN realizes its functions. Through different business services, they are aggregated into the functional layer of SBIN. The function layer has the ability to organize different business services in the service layer so as to achieve a certain kind of function. It mainly includes the data transmission and distribution, environmental information detection, and the position and navigation of a series of modules. The top layer of the function module of SBIN is the mission layer. With the support of different functional attributes in the function layer, SBIN can perform related tasks conveniently and quickly. The functional-relationship diagram of SBIN is shown in Figure 2.
# Structural Particularities, Prediction, and Synthesis Methods in High-Entropy Alloys
## 2 Structural Particularities of HEAs, Prediction and Estimation Concepts
### Microstructural Stability
#### 2.2.1 High-Entropy Effect  
The high-entropy effect is the concept behind high-entropy alloys and proposes an increase in configurational entropy in equimolar or near-equimolar alloys with 5 or more elements that can favor the formation of solid solution phases over intermetallic compounds. To support this concept, the configurational entropy is often evaluated in relation to the formation entropy of pure metals [1; 8; 9; 10] or the formation enthalpies of certain metal compounds [11]. At the same time, only configurational entropy will be considered [12]. Among the terms that describe the components of entropy (configurational, vibrational, electronic, and magnetic), the configurational component turns out to be dominant over the other three [8]. The vibrational entropy is much higher in the ideal solid solution, but the thermodynamic processes that take place between the solid solution phases and the intermetallic compounds are quite complicated and a large part of the vibrational entropy cancels out in the initial and final phases. Finally, the first observations supporting this effect highlight the phases of the cast product, making the interpretation more complicated. The magnetic and electronic components of the entropy change are particular to the materials placed in magnetic or electric fields. Otherwise, these components have little or no influence on the total entropy of the system. Configurational entropy actually favors the formation of solid solution phases up to a certain level, which can be evaluated against the classical concepts of thermodynamics and against the microstructures reported in scientific papers [12].  
Figure 1: The four effects that influence the formation of high-entropy alloys.
# Modeling and Compensation of Motion Errors for 6-DOF Robotic Manipulators
## 2 Kinematics Model for a 6-DOF Robotic Manipulator  
The improved DH method [21] was used to establish the connecting rod coordinate system of the manipulator, and the kinematics model was used to obtain the analytic solution of the forward and inverse kinematics of the manipulator, by which the motion posture of the manipulator could be obtained. This method was also the foundation for solving the stiffness matrix.  
For the forward kinematics model, the connecting rod coordinate system of a 6-DOF manipulator (Figure 2a) was established as follows: first, the position of the base coordinate system and the initial position and attitude angles of the end were determined. Second, the improved DH method was adopted to establish the coordinate system of the base and each joint, and the forward kinematics solution model was utilized to calculate the point-to-point (PTP) trajectory. The established coordinate system is shown in Figure 2b. Figure 3 shows one of the connecting rod coordinate systems.  
From Figure 3, the homogeneous transformation matrix of the linkage can be obtained as follows:  
\[\begin{array}{llll}A_{i}&=Rot(z,\theta_{i})Trans(0,0,d_{i})Trans(a_{i},0,0)Rot( x,a_{i})\\ &=\left[\begin{array}{cccc}cos\theta_{i}&-sin\theta_{i}cos\alpha_{i}&sin \theta_{i}sin\iota_{i}&a_{i}cos\theta_{i}\\ sin\theta_{i}&cos\theta_{i}cos\iota_{i}&-cos\theta_{i}sin\iota_{i}&a_{i}sin \theta_{i}\\ 0&sin\iota_{i}&cos\alpha_{i}&d_{i}\\ 0&0&0&1\end{array}\right]\end{array} \tag{1}\]  
where _Rot_ and _Trans_ represent the rotation matrix and translation matrix, respectively, and \(i\) represents the number of joints. \(\theta_{i}\), \(\alpha_{i}\), \(\alpha_{i}\), and \(d_{i}\) represent the angle, connecting rod torsion angles, connecting link length, and connecting rod offset, respectively, of joint \(i\).  
Figure 1: Schematic of the proposed method.  
The end posture (including position and attitude) of the 6-DOF manipulator (\(4\times 4\) matrix\({}_{6}^{0}\)T) was calculated as follows:  
\[{}_{6}^{0}T=\prod_{1}^{6}A_{i}=\left[\begin{array}{cccc}n_{x}&\ o_{x}&a_{x}&p_{ x}\\ n_{y}&\ o_{y}&a_{y}&p_{y}\\ n_{z}&\ o_{z}&a_{z}&p_{z}\\ 0&0&0&1\end{array}\right] \tag{2}\]  
To solve the angles of the six joints from the space posture inversion of the manipulator and pave the way for the subsequent compensation model, the inverse kinematics of the manipulator need to be solved. The angles of the six joints can be obtained by solving eight inverse kinematics solutions with the improved algebraic method, which will be explained in detail in Section 4.  
Figure 3: Connecting rod coordinate system.  
Figure 2: (a) 6-DOF manipulator model and (b) connecting rod coordinate system.
# Predicting Employee Absence from Historical Absence Profiles with Machine Learning
## 3 Employee Absenteeism Data
### Methodology for Dataset Construction  
To develop robust predictive models for vacation and sick leave, we created two different dataset groups with a consistent methodology, differing only in their target attributes. Each dataset is aimed to be used for predicting the number of days of leave within a given week (in the case of single-target prediction) or several weeks (in the case of multi-target prediction). In the first case, the targets represent vacation days, in the other case sick days. Both dataset groups contain comprehensive descriptive attributes that include both the demographic profiles of employees and their absence profiles, as described in Section 3.2. In total, we used 145 descriptive attributes in the datasets.  
The datasets were created using the sliding-window technique, which is ideal for capturing temporal patterns and trends in time series data. For each employee, we started with the earliest available data and created an observation window that captures all relevant data up to a specific week. The number of days of leave in the current week represents the target attribute for this observation. All lagged attributes that provide historical context are derived from the previous weeks' data. In the case of a multi-week forecast, the delayed attributes are calculated for the weeks preceding the first week in the target attributes. In Figure 3, we present the sliding window technique for the case of a one-week forecast (Figure 3a), a two-week forecast (Figure 3b) and a four-week forecast (Figure 3c).  
Figure 3: Sliding-window technique: (**a**) one week forecast; (**b**) two weeks forecast; (**c**) four weeks ahead forecast.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Attribute Name** & **Type** & **Description** \\ \hline \multirow{2}{*}{WeekVacationLeaveK+X} & \multirow{2}{*}{numeric} & Target attribute for predicting vacation leave for \\  & & defined week. \\ \hline \multirow{2}{*}{WeekSickLeaveK+X} & \multirow{2}{*}{numeric} & Target attribute for predicting sick leave for \\  & & defined week. \\ \hline \hline \end{tabular}
\end{table}
Table 5: The structure of the target attributes where X present week number.  
To systematically generate training examples, the window is moved forward by one week, and the process is repeated until the end of the available data for each employee. This method ensures that each training instance is a snapshot of the employee's record, containing all relevant information up to the week immediately preceding the prediction week. This sliding window approach is applied uniformly to all companies and employees to ensure that the datasets are comprehensive and consistently structured.
## 2 Preliminaries
### Bregman Divergences  
In addition to the distance generated by the Riemannian metric, the divergence of the SPD matrix based on Bregman divergence can also be used as the distance metric of the SPD matrix. For all \(X_{1},X_{2}\in Sym_{++}^{D}\), the Bregman matrix divergence [24] is defined as  
\[\delta_{\Phi}(X_{1},X_{2})=\Phi(X_{1})-\Phi(X_{2})-\left\langle\
abla_{\Phi(X_{ 2})},X_{1}-X_{2}\right\rangle_{F}, \tag{11}\]  
where \(\Phi:Sym_{++}^{D}\to R\) is a strictly convex function, and \(\
abla_{\Phi(X_{2})}\) is the gradient of \(\Phi\) at point \(X_{2}\). Bregman divergence is similar to distance measure, which does not satisfy trigonometric inequality and symmetry. To symmetrize Bregman divergences, different seed functions \(\Phi\) are used. Among them, the Stein divergence [21] and Jeffrey divergence [22] play an important role in computer vision.  
For any given pair \(X_{1},X_{2}\in Sym_{++}^{D}\), the Stein divergence adopting \(\Phi(X)=-log\ det(X)\) and Jensen-Shannon symmetrization is defined as  
\[\delta_{S}^{2}(X_{1},X_{2})=log\ det\bigg{(}\frac{X_{1}+X_{2}}{2}\bigg{)}- \frac{1}{2}log\ det(X_{1}X_{2}). \tag{12}\]  
For any given pair \(X_{1},X_{2}\in Sym_{++}^{D}\), the Jeffrey divergence adopting \(\Phi(X)=-log\ det(X)\) and direct symmetrization is defined as  
\[\delta_{J}^{2}(X_{1},X_{2})=\frac{1}{2}tr\Big{(}X_{1}^{-1}X_{2}\Big{)}+\frac{ 1}{2}tr\Big{(}X_{2}^{-1}X_{1}\Big{)}-D, \tag{13}\]  
where \(D\) is the dimension of manifold. In the same way as the AIRM, the Stein divergence and Jeffrey divergence are affine invariant.