esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistical Analysis  
Data were processed in Python, with SciPy as the main package used for statistical analysis. Accuracy was determined by \"recovery\" calculated with ((pXRF value/CRM value) \(\times\) 100), while repeatability, which is indicated as precision in the U.S. EPA report [11], was determined by RSD% as ((standard deviation/mean) \(\times\) 100) on replicates for each analyte and technique. For further statistical analysis and data quality levels for pXRF results, the U.S. EPA [11] procedure was followed according to the majority of recent studies, e.g., [13; 24; 25; 30]. Comparability of pXRF with confirmatory data (ICP-MS and DMA-80) was assessed using linear regression statistics, where optimal correlations were found with a coefficient of determination r\({}^{2}\) = 1, slope equal to 1, and intercept with the y-axis equal to 0. Since a wide range in concentrations can be associated with a wide range in variance, to provide a correlation that was equally influenced by both high and low values, all data were log\({}_{10}\) transformed according to the U.S. EPA method [11], providing equal weight in the square regression analysis to both small and large values. A Z-test was used to test the statistical similarity between the slope regression and an ideal slope = 1 and between the y-intercept of the regression function and an ideal y-intercept = 0. Furthermore, the _t_-test was also carried out on log\({}_{10}\)-transformed data to evaluate if the two datasets, respectively, obtained by means of pXRF and the confirmatory data for each element and sample treatment procedure belong to the same population, implying that any significant difference between the two analytical techniques occurred. _t_-Test, slope and y-intercept z-tests are successful for _p_-value > 0.05. Lower values of _p_-value imply significant differences between sample populations or from the ideal slope and y-intercept and vice versa. According to criteria established by the U.S. EPA [11], 3 levels of pXRF data quality can be defined: the definitive level is considered the highest level of quality, quantitative screening level data provide the quantification although it may be relatively imprecise, whereas qualitative screening level data indicate the presence or absence of elements of interest but do not provide reliable concentration estimates (Table 1).  
Lastly, a \"site-specific accuracy\" was calculated ((pXRF concentration/ICP-MS or DMA-80 concentration) \(\times\) 100) and used to highlight method detection limits (MDLs) and concentration effect issues for the analysis of pXRF [11]. The MDL is defined as a threshold below which the analytes exhibit a region where there is no longer a linear relationship.
## 2 Materials and Methods
### Materials
#### 2.1.2 Aggregates  
The natural and recycled aggregates came from local companies linked to the construction sector. The physical properties of the aggregates are detailed in Table 1. The natural aggregate was of siliceous origin. The recycled aggregate was of low quality, derived from aggregate processing of precast concrete fence wall debris with a compressive strength of design of 30 MPa, and was subsequently treated to improve its properties. The coarse recycled aggregate used to replace the natural aggregate had a particle size of 4.75-12.5 mm. The particle size distribution for the two aggregates is shown in Figure 1, while Figure 2 shows the uncoated and coated coarse recycled aggregate. In addition, for treatment of the recycled aggregate, the fine fraction of the waste from the concrete fence walls was manually sieved using a 45 \(\upmu\)m sieve (No. 325) to obtain the recycled binder paste (RBP).  
It is important to note that because of the attached mortar, the recycled aggregate had an old interfacial transition zone (ITZ), which causes a weak zone due to continuous cracking caused by the crushing process of the RA [31]. Even though the amount of ITZ is low, it represents a weak and porous region, which is a characteristic of recycled aggregates [32]. The multiple cracks on the ITZ may be related to porosity and water absorption associated with the origin of the recycled aggregates [31]. As mentioned above, because the recycled aggregate is of low quality, coming from the demolition of precast concrete fences, it is more porous; thus, it was treated to reduce its porosity and improve its water absorption.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Aggregate Type**} & **Specific Gravity** & **Water Absorption** & **LA Abrasion Loss** \\  & **(g/ cm\({}^{3}\))** & **(\%)** & **(\%)** \\ \hline CNA\({}^{1}\) & 2.65 & 1.41 & 19.93 \\ FNA\({}^{2}\) & 2.48 & 2.17 & - \\ CRA\({}^{3}\) & 2.27 & 7.19 & 37.94 \\ C-CRA\({}^{4}\) & 2.26 & 6.81 & 40.20 \\ \hline \end{tabular} \({}^{1}\) Coarse Natural Aggregate, \({}^{2}\) Fine Natural Aggregate, \({}^{3}\) Coarse Recycled Aggregate, \({}^{4}\) Coated Coarse Recycled Aggregate.  
\end{table}
Table 1: Physical properties of the aggregates.
# IoT System for Detecting the Condition of Rotating Machines Based on Acoustic Signals
## 3 Architecture of the Proposed System
### Hardware Implementation
During the development of the device and its initial commissioning for testing purposes, it was necessary to achieve a simple and efficient interaction between the device and the user. In order to make sure that the device works properly in certain phases of operation, the simplest way was to display certain messages to the user. An LCD screen commonly used in industrial applications has been used for this purpose. This LCD is easy to manage through the I2C communication interface, it is a small energy consumer and it is possible to display enough information. On the other hand, the control of the device in order to issue the appropriate commands is performed by means of two buttons, also intended for industrial applications. This means that they are resistant to dust, moisture, abrasives and similar potential hazards that can be encountered in an industrial plant. With these buttons, the user navigates through the application menu, and starts or stops certain tasks. Components of the device for acoustic signal acquisition and processing and their layout in practical implementation are shown in Figure 8. Only the power bank and SD card are not visible. SD card is inserted at the bottom of the Raspberry Pi, and the power bank is accommodated at the bottom of the box, under the plastic divider that separates the power supply and electronics. The list of components used for the implementation of this device, together with the most important characteristics and approximate price, is shown in Table 2.  
The device (the mentioned components) is packed in a plastic box, 3D printed of high-quality plastic, resistant to the high temperatures. Initially, it was planned to mount the device on the rotary machine using strong magnets. However, after the experience in the initial design phase, we gave up on such a solution because it turned out that we would have huge problems with the temperature and vibrations that produce rotary machines in an industrial surrounding. Therefore, we decided to mount the device on the machine using the bracket made of metal construction (Figure 9, left), which is fixed to the construction of the machine by the screws (Figure 9, right). Using a rod of appropriate length, which is mounted on the body of the bracket, the microphone approaches very close to the surface of the machine, while the device itself is far enough to avoid the heat impact to the body of the box and electronic components inside.
# Approximate Networking for Universal Internet Access
## 2 Approximate Networking Technologies
### Approximate Networking Protocol Stack
As an example work, Selective Approximate Protocol (SAP) [18] allows applications to coordinate with multiple networks layers to accept potentially damaged data. The authors of SAP, which is built over UDP-Lite, have reported a 30% speedup for an error-tolerant file transfer application over Wi-Fi.
# An Efficient Multi-Scale Focusing Attention Network for Person Re-Identification
## 2 Related Work  
### Person ReID and Attention Mechanism  
With the rise of deep learning, there has been an increasing amount of literature on person re-identification, such as deep metric learning [30; 34] feature representation learning [27; 35], attribute learning [36], and re-ranking techniques [37]. Taken together, these studies presented different types of ReID methods to address a wide variety of ReID problems or challenges. Furthermore, attention methods have achieved great success in ReID [19; 20; 21; 22; 23]. In this paper, we aim to introduce an improved person re-identification method via the attention mechanism to enhance the performance of ReID.  
The basic idea of the attention mechanism in computer vision is to allow the deep learning model to ignore irrelevant information and focus on critical details. ASTPN [20] (A joint Spatial and Temporal Attention Pooling Network) is based on the attention mechanism to learn ReID features in video sequences better. The video sequence-based ReID task has the problem of varying lengths of video sequences. [PERSON] et al. [21] split a long video sequence into multiple short segments and merged the first few segments after sorting for model learning. Based on the clip-level spatial and temporal attention (STA) [22] learning features of spatial and temporal dimensions. The self-attention mechanism is also used to learn the relationship between long and short video sequences [21]. ABD-Net [19] is designed to combine attention modules and diversity as complementary mechanisms to improve ReID performance jointly. Ref. [23] proposed an effective model to determine the importance of each spatial-temporal feature from a global perspective. However, these spatial-temporal attention methods are coarse and unstable, which cannot deal with the complex relationships among parts. Thus, the Multi-Scale Focusing Attention Network (MSFANet) is proposed to capture more complex and detailed information for ReID training task.
## 3 Cell Modeling
One of the main reasons for the increasing error is irreversible changes in the SOC-open circuit voltage (OCV) curve. The ESC model is more robust to this change because its hysteresis element allows for some adjustment of the curve over time. To support this claim, the difference in the error between the Thevenin and ESC models is not significantly large in the earlier cycles, but it gradually increases when approaching later cycles. This observation can be interpreted as the curve remaining intact during the early cycles, when battery aging is minimal. Another observation regarding the effect of model type is that the addition of RC elements significantly enhances the Thevenin model but does not improve the ESC model. This discrepancy arises because the hysteresis element reduces the relative influence of additional RC elements in the ESC model. Therefore, integrating the hysteresis element is more helpful in creating a cell model with greater fidelity, compared to simply increasing  
Figure 4: Circuit schematic for the ESC model, which is almost the same as the Thevenin model, except for the addition of hysteresis voltages (shown in blue). the number of RC elements. In consequence, we selected the ESC 1 RC as the optimal model type. The state and output equations of the selected model are formulated as:  
\[ \begin{bmatrix}z_{k}\\ i_{R_{1}k}\\ h_{k}\end{bmatrix} = \begin{bmatrix}1&0&0\\ 0&e^{-\frac{\Delta t}{R_{1}+1}}&0\\ 0&0&e^{-\frac{|\vec{p}_{k-1}-1\Delta t}{Q}}|\end{bmatrix}\begin{bmatrix}z_{k-1} \\ i_{R_{1}k-1}\\ h_{k-1}\end{bmatrix} + \begin{bmatrix}-\frac{\eta\Delta t}{R_{1}c_{1}}&0\\ 0&e^{-\frac{|\vec{p}_{k-1}-1\Delta t}{Q}}-1\end{bmatrix}\begin{bmatrix}i_{k-1} \\ sign(i_{k-1})\end{bmatrix} +\omega_{k-1} \tag{1}\]  
\[v_{T,k}=OCV(z_{k})+M_{0}sign(i_{k})+Mh_{k}-R_{1}i_{R_{1}k}-R_{0}i_{k}+v_{k} \tag{2}\]  
where \(k\) is the integer time index, \(\Delta t\) is the measurement time interval, \(\eta\) is the coulombic efficiency, and \(Q\) is capacity. The three states z, \(i_{k_{1}}\), and \(h\) are involved, along with the seven parameters \(Q\), \(\tau\), \(M\), \(M_{0}\), \(R_{0}\), \(R_{1}\), and \(\gamma\), all of which are estimated using a dual filter. The estimated values of \(z\) and \(Q\) were compared against their true values obtained using the current sensors integrated in the test equipment. This dedicated sensor was designed for ultra-high precision measurement of currents, ensuring an accuracy of less than 0.01% across all measurement ranges. Although typical current sensors in commercial BMSs operate with single-digit accuracy around 3%, we aim to improve upon these less reliable measurements with estimates derived from the developed cell model and its filter.
## 1 Introduction  
The production of jam is one of the oldest food preservation techniques that allows people to enjoy all kinds of fruits during the off-season. According to European Union standards, jams, honey and dried fruits are classified as high-sugar, low-water products [1]. In China, coconut is mainly distributed in the Wenchang area along the coast of the Hainan province. Coconut water and pulp contain a variety of nutrients and have a unique flavor. Coconut water is considered a healthy beverage because it contains a variety of vitamins and minerals, enzymes with anti-inflammatory properties, and antioxidants [2]. In mature coconuts, coconut pulp is commonly used in coconut milk production [3; 4]. The physical and chemical properties of coconut pulp make this food suitable for consumption in natural conditions. In addition, coconut pulp is an excellent raw material for the jam industry. However, little research has been conducted on coconut jam. Product development and formulation research are essential parts of the jam industry [5]. The quality of jam is normally defined according to its flavor, color and texture. Among these fundamental properties, aroma as the sensory indicator plays a key role in evaluating the quality of products. From an industrial point of view, an efficient procedure is required to control the quality and stability of the products. Aroma may be a good option to monitor production as a marker [6]. Generally, heating to a high temperature can improve the variety of aroma compounds in coconut jam products and create a unique flavor. Aroma compounds have been identified in coconut water, including alcohols, aldehydes, esters, and acids [7]. Moreover, the flavor perceived by consumers is mainly due to many volatile compounds at different concentrations. Therefore, it is particularly important to explore the changes of the characteristic aroma compounds in the coconut jam during processing.  
Due to the advantages of solvent free sample processing, high sensitivity and reliability, headspace solid phase microextraction (HS-SPME) combined with gas chromatography-mass spectrometry (GC-MS) has been widely used for the analysis of volatile compounds [8; 9; 10; 11; 12]. The purpose of this study was to verify the volatile compounds of coconut jam produced during the concentration process. Through statistical analysis of a number of aroma compounds, the characteristic aroma compounds appearing in each concentration stage were determined. The marker aroma substances were combined with the concentration time to fit the kinetic equation, which may achieve the monitor production of coconut jam in the industry.
## 3 Results and Discussion
In the study, the focus was specifically on the index finger and middle finger to minimize the contact pressure concentration level. In a previous study, all fingers were considered to design a handle with a shape that perfectly fits the fingers. A suitable threshold value for element density that should be removed can be freely decided by a designer. Extant studies reported that the optimal diameter of an object for gaining gripping comfort corresponded to between 27 and 40 mm [15; 43]. Additionally, the optimal diameter of an object was also acquired based on the hand anthropometry of the users [17; 36]. Therefore, an optimal diameter of the column is achieved via selecting a unique threshold value for element density that should be removed. The study presented several limitations. The simulation in the study only reproduced a grasp type of a young person (age: 21 years). In the product development, products should be developed to provide gripping comfort for larger populations. Gripping comfort is reported as affected by hand anthropometry [36; 43]. Furthermore, grasp type varies among individuals and object shapes and sizes [6; 44; 41]. Therefore, it is necessary to construct subject-specific hand FE models with various hand shapes considering age and gender and to simulate various grasp types to evaluate gripping comfort under different conditions. Moreover, the regression equation used for gripping comfort evaluation was obtained from nine young male subjects (age: \(22.2\pm 0.6\) years) [6]. The differences among age groups may affect gripping comfort and contact pressure because the anthropometric characteristic is considered different between elderly and young people. Additionally, perception of comfort may also change according to the age of a subject. Therefore, it is necessary to investigate the difference among age groups in gripping comfort and contact pressure in the future. Additionally, gripping comfort evaluation was performed under a static condition when the hand reached a final posture (a gripping posture without any finger motions), and this is another limitation of the study. Many manufactured products used in daily life, such as dryers, shavers, and irons, require dynamic evaluation including ease to use and muscle fatigue in a certain time span. Hence, a dynamic gripping comfort evaluation method should be developed by conducting a muscle activity measurement using Electromyography (EMG) as well as by developing a hand musculoskeletal simulation in the future to obtain a gripping part shape that is comfortable for all users.
## 2 Related Work
### Static Assessment Metrics  
Static resilience metrics quantify a system's resilience based on predefined structural, organizational, or procedural characteristics. These metrics are often derived from standards, expert consensus, or structured frameworks, and are typically expressed through indicators such as availability, reliability, risk indexes, or system-level resilience scores [8; 9]. They offer an accessible way to compare systems or assess baseline resilience levels without requiring complex modeling or simulation.  
A prominent example is the guidance provided by the U.S. National Institute of Standards and Technology (NIST). In Special Publication 800-160 [10], NIST outlines a systems engineering approach to developing cyber resilient systems, emphasizing resilience objectives, design principles, and traceable metrics across system layers. This methodology builds on earlier work by MITRE [11; 12], which formalized structured goals and trade-space analysis in resilience engineering. Building upon these foundations, NIST also released SP 800-82 [13], a guide focused on operational technology security in ICSs. It incorporates static metrics aligned with the widely adopted Cybersecurity Framework [14], covering categories such as governance, detection, and recovery.  
Similar initiatives have been introduced globally. The United Kingdom's National Cyber Security Centre proposed a Cyber Resilience Assessment Framework for critical infrastructure [15]. The European Union incorporated cyber resilience objectives in the NIS2 Directive [16], while China issued cyber resilience evaluation criteria targeting national information infrastructure [17].  
While these metrics provide a clear structure and facilitate benchmarking, they are limited in capturing dynamic system behavior or adapting to evolving threats. They often represent high-level abstractions that may overlook internal system complexity or cross-domain interactions. As a result, static metrics are best suited as foundational tools or for compliance purposes, rather than as standalone resilience evaluation strategies.
## 2 ISP and Performance Indicators
### Speed Stability  
Speed stability is quantitatively characterized by the degree of dispersion of the actual speed from the given speed, similar to the concept of standard deviation in statistics. Speed stability is calculated as follows  
\[St=\sqrt{\frac{1}{N_{\omega}-1}\sum\limits_{i=1}^{N_{\omega}}\left(\omega_{i}- \overline{\omega}\right)^{2}} \tag{2}\]  
where \(\omega\) is the actual speed, \(\overline{\omega}\) is the average of the actual speeds, and \(N_{\omega}\) is the number of datasets. The unit of speed stability is rad/s.
## 5 Results
### Peak Throughput  
This section presents a comprehensive performance evaluation of different Wi-Fi technologies in various environments, considering three distinct distances: short, medium, and long. Analysing performance at varying distances allows for a detailed assessment of signal attenuation, multipath effects, and throughput degradation, providing insight into the robustness of each Wi-Fi standard under different propagation conditions. Both TCP and UDP traffic are measured to capture the distinct characteristics of connection-oriented and connection-less communication. TCP incorporates congestion control and retransmission mechanisms to ensure reliable data transfer, making it highly sensitive to network conditions and latency variations. In contrast, UDP, which lacks reliability mechanisms, prioritises low-latency transmission, making it more representative of real-time applications such as VoIP, video streaming, and online gaming. Evaluating both transport protocols provides a holistic view of Wi-Fi performance across different traffic profiles.  
This study focuses on uplink traffic, or traffic from clients to AP, because unlike downlink traffic, uplink transmission is more susceptible to channel contention and medium access constraints, particularly in environments with high device density. The evaluation of uplink performance allows for a broader understanding of how different Wi-Fi generations handle resource allocation and interference mitigation.  
Performance measurements are performed using iperf3, a widely adopted tool for precise network throughput, latency, and jitter analysis. A Raspberry Pi connected via Ethernet is designated as the server to provide a stable, low-latency endpoint, minimising external variability. The desktop computer, acting as the client, generates Wi-Fi traffic, ensuring that performance metrics accurately reflect wireless link behaviour without interference from wired network inconsistencies. This experimental setup ensures a controlled environment for evaluating the efficiency and stability of Wi-Fi technologies in real-world deployment scenarios.