esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Geometric Error of Five-axis CNC Machine Tool
### Definition of Average Position Error of Rotary Axis  
As shown on the left side of Figure 1, machine tool was divided into two systems. One was error detection system, which consisted of the X, Y, Z axes, and the contact trigger probe. The other one was a double turntable system for detected objects. The system was composed of a swing axle a-axis and rotary table c-axis, which had large errors and were difficult to identify. Addressing this problem was the main aim of this paper.  
The geometric error kinematic chain of two motion systems of the TOPNC VMC-C50 five-axis machine tool was established. The error kinematic chain was composed of coordinate systems of each kinematic unit of the machine tool. This chain clearly and intuitively reflected the relationship between the movement of each axis and various errors of the machine tool. The error of the linear axis was smaller than that of the rotary axis and could be compensated for by conventional technical methods [32]. The linear axis detection system and the double turntable system of the TOPNC VMC-C50 five-axis machine tool are independent of each other. Therefore, in the process of geometric error identification of the rotating axis, if the influence of the linear axis error is considered, the measured spherical coordinate value can be directly subtracted from the linear axis error.  
The position error of the rotation axis is the error of the actual rotation axis deviating from theoretical axis [37]. In this section, 12 average position errors were determined on rotary axes for calibration, as shown in Table 1, and the source of each error was defined.  
\begin{table}
\begin{tabular}{c c} \hline
**Error Symbol** & **Definition** \\ \hline \(\varepsilon_{aA}\) & Average angle positioning error of A-axis \\ \(\varepsilon_{BA}\) & Average angle error of A-axis rotating around Y-axis \\ \(\varepsilon_{YA}\) & Average angle error of A-axis rotating around Z-axis \\ \(\delta_{XA}\) & Average line error of A-axis along X direction \\ \(\delta_{yA}\) & Average line error of A-axis along Y direction \\ \(\delta_{xA}\) & Average line error of A-axis along Z direction \\ \(\varepsilon_{aC}(A_{i})\) & Average angle error of C-axis around X direction at \(A_{i}\) \\ \(\varepsilon_{BC}(A_{i})\) & Average angle error of C-axis around Y direction at \(A_{i}\) \\ \(\varepsilon_{YC}(A_{i})\) & Average angle error of C-axis around Z direction at \(A_{i}\) \\ \(\delta_{xC}(A_{i})\) & Average line error of C-axis along X direction at \(A_{i}\) \\ \(\delta_{YC}(A_{i})\) & Average line error of C-axis along Y direction at \(A_{i}\) \\ \(\delta_{zC}(A_{i})\) & Average line error of C-axis along Z direction at \(A_{i}\) \\ \hline \end{tabular}
\end{table}
Table 1: Definition of average position error of rotary axis.  
Figure 1: Schematic diagram of spatial structure and error kinematic chain of five-axis CNC machine tool.
# Security in Wireless Sensor Networks: A Cryptography Performance Analysis at MAC Layer
## 3 Analyzed Cryptographic Methods and Primitives
### Elliptic Curve Criptography (ECC)
#### 3.3.1 Confidentiality in ECC  
As a traditional public key cryptographic algorithm, the ciphering and deciphering steps are carried out as we described when describing RSA related to confidentiality. The only difference in the case of ECC is the key generation mechanism which is based on the geometric element called elliptic curve and it has been proved that this mechanism is very efficient and optimized. So, the sensor uses the public key, generated using ECC, of the server (the target) to cipher messages, and then the server upon the receipt of the messages uses its own private key, also generated using ECC, to decipher the ciphertext and get the plaintext, see Figure 6.
# Detection and Localization of Failures in Hybrid Fiber-Coaxial Network Using Big Data Platform
## 6 Conclusions  
Big data technologies are being introduced to many large service provider networks. The main reason for this trend is the possibility of extracting valuable information from data available and collected from the network. The main purpose is typically to improve business decisions and strategies. However, since collected data are at our disposal, the data can be used for other purposes as well. This paper provides one example in which collected data can be used for efficient automatized hard failures detection and localization. As a consequence, overall network availability and performance are improved, and users are more satisfied. Our proposed approach was confirmed by the successful deployment of FDLBD in real HFC network. Thus, we can conclude that the utilization of big data platform dedicated to collecting and processing data from the network can be increased by adding new functionalities that expand the big data platform's portfolio of possibilities. Additionally, the comparison given in Section 5.3 shows that some other existing solutions could benefit from big data technologies, especially in case of very large networks. Finally, our predictions are that big data technologies will play one of the major roles in most of the large service provider networks in near future.  
FDLBD is efficient for tree topology networks. However, in case of adaptation to other network technologies where topologies other than trees are used (e.g., ones that contain loops), our proposed localization solution would have to be modified to support these other topologies as well. Thus, our future work will include the consideration of other network topologies. Furthermore, FDLBD covers hard failures, but soft failures are important too, as they also impact the overall network performance. An important part of our future work will be dedicated to the detection of weak spots in the network that degrade the network performance. In this case, data collected in longer period of time will be evaluated to detect and predict the performance degradation of the devices so they can be timely replaced or repaired. In this way, efficient performance monitoring and optimization of the network will be achieved. These are the two main directions of our future work, but we will also look into other aspects of using big data, such as efficient planning of network expansions and optimization of users' QoE.  
Conceptualization, M.S. and Z.C.; methodology, M.S. and Z.C.; software, M.S.; validation, M.S. and Z.C.; formal analysis, M.S.; investigation, Z.C.; resources, M.S.; data curation, M.S.; writing--original draft preparation, M.S. and Z.C.; writing--review and editing, Z.C.; visualization, Z.C.; supervision, Z.C.; project administration, M.S.; funding acquisition, M.S. and Z.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Challenges, Threats, Security Issues and New Trends of Underwater Wireless Sensor Networks
## 2 Particularities and Constraints
### Multipath and Doppler Effect  
The multipath effect is a phenomenon of a wave from the source node transmitted to the destination node via two or more paths. Moreover, under the right conditions, the two (or more) arriving signals may interfere at the destination node. In UWSNs, the multipath effect is more severe than that in WSNs. In the deep water environment, the medium is homogeneous and surface and bottom reflections may be neglected. But in the shallow underwater environment, the transmission distance is larger than the water depth; moreover, depending on the depth of the water, the factors (e.g., acoustic speed, temperature, salinity, turbidity) are different. Hence, the shallow water environment can be divided into many layers from surface to bottom. The acoustic wave can be transmitted and reflected in many layers, and then the multiple arrivals of the same signal may be received at the receiver, which causes a significant multipath effect.  
In WSNs, movement of the sending node or receiving node contribute to the changes in the radio channel response. As a consequence, this will result in frequency shifting as well as additional frequency spreading, and this phenomenon is the Doppler effect. The magnitude of the Doppler effect is proportional to the ratio \(a=v/c\), where v is the relative speed between sending node and receiving node and \(c\) is the speed of underwater acoustic wave. As discussed in Section 2.2.1, the speed of the acoustic wave is much slower as compared to the speed of the electromagnetic wave. Hence, the Doppler effect is more severe in UWSNs. This effect causes distortion in two ways: spreading the received signal bandwidth \(B\) to \((1+\alpha)B\) which is referred to as the motion-induced Doppler spreading, and shifting the reception frequency \(f\) by an offset of \(\alpha f\) which is referred to as Doppler shifting [13]. The Doppler effect and time synchronization can influence localization accuracy.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Long-Distance** & **Medium-Distance** & **Short-Distance** \\ \hline Spreading Loss Model & cylindrical wave & cylindrical wave & spherical wave \\ Spreading Loss Factor & \(d=1\) & \(d=1.5\) & \(d=2\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Spreading loss model for different communications range.
# A Software-Defined Networking Framework to Provide Dynamic QoS Management in IEEE 802.11 Networks
## 4 Evaluation
### SDN-Based Framework Implementation  
#### 4.2.1 Implementation Issues  
The IEEE 802.11 backoff standard forces that any value of the \(CW_{min}\) must be always a power of 2 less 1 (i.e., 31, 63, 127, 255, 511, and 1023). Therefore, the \(CW_{min}\) value is an all 1's binary number of length \(m\). In this way, the algorithm to generate a random number in this range is simple and easily implementable.  
It is clear if our proposed DEDCA mechanism does not satisfy this requirement. However, this is not a big obstacle, neither when using simulations nor in Software Defined Radio (SRD) [7], since it is quite simple to modify the software code.  
On the other hand, most hardware manufacturers develop the backoff protocol in a binary microcode firmware or in proprietary kernel modules, which are loaded by the operating system directly into device memory. Although this complicates significantly any change of the default protocol behavior, it is still possible to use an unrestricted value for \(CW_{min}\), as authors of [8] demonstrate using Broadcom B43 wireless card. The changes in the assembly language of the backoff algorithm can be easily exported by manufacturers to any other wireless card.  
We have implemented the proposed DEDCA mechanism in a testbed consisting of a Ryu SDN controller and an 802.11 Access Point equipped with OpenFlow v. 1.3 using the simulator Estinet 9.0 OpenFlow Network Simulator [9].
# Assessing Energy Efficiency in Desktop-Size FFF 3D Printers
## 2 Materials and Methods
This method re flects the practical approach typically employed by 3D printer users in production scenarios, where judgments are based on expert experience rather than standardized measurement equipment. If a sample was found to be incorrectly produced, the manufacturing process was repeated until a satisfactory result was achieved. To minimize potential measurement device impact on results, four different devices were used to measure current and power usage. For each FFF machine, four copies of the snap hook were manufactured, with each device connected to a different measuring device. This ensured independent measurements without duplicating devices. The final results are presented as average values derived from individual measurements. The following measuring devices were utilized: Sonoff Pow R2 (ANRis, Tokyo, Japan), DPM Solid DT21 (Budowlex, Lublin, Poland), GreenBlue GB202 (GreenBlue, Robertsbridge, UK), and Voltcraft logger 4000F SD (Voltcraft, Hirschau, Germany). Repeatability for each measuring device was previously verified on the Anet A8M machine, where each device measured three identical manufacturing processes, consistently yielding the same results. To enhance representativeness, twelve different FFF machine setups were tested. The selected devices represented a range from simple, home-assembled models to machines intended for professional and semi-professional use in small to medium-sized enterprises. Three different versions of the Ender 3 were included to assess FFF process evolution in power management. The first model was released in early 2018, while the latest was introduced in mid-2023. High-cost machines with entirely closed material and control systems, predominantly used by large companies, were not included in this study. The device characteristics are presented in Table 1.  
To ensure comparability, all printing operations were conducted after complete device cooldown, with nozzle temperatures matching ambient conditions. Printing took place at 22 \({}^{\circ}\)C and 50% relative humidity, and all processes used the same material: white PLA filament from Spectrum. The VSSHAPER machine (VSSHAPER, Jasionka, Poland) includes a heated working chamber; however, this is unnecessary for PLA processing, as heating is not required during the process. The VSSHAPER does require a compressed air source due to its pressurized worktable attachment. Energy consumption for air compression was excluded from the results, as a single compressor charge can release and lock the worktable multiple times. Finally, the mass of each manufactured snap hook, post raft separation, was measured using the Radwag precision balance, model WTC 200. The average sample mass facilitated the calculation of energy efficiency per unit mass of processed material. Figure 1: The carabiner was used for tests as a printing pattern.
# ARNS: Adaptive Relay-Node Selection Method for Message Broadcasting in the Internet of Vehicles
## 4 Method Design  
In this section, we will propose ARNS to solve the problems described in Section 3, but before that, we need to improve the EPBP-based methods to make them suitable for real-world scenarios. Therefore, the content of this section is organized as follows: we first propose an EPBP-based relay-node selection method suitable for curved road scenarios with obstacles, then, develop a criterion of classifying road structures. Moreover, we improve the EPBP-based junction relay-node selection method [26] to resolve the problems of multiple road structures existing in the communication range of the sender. Finally, an adaptive relay-node selection method based on these above works is proposed. The goal of this method is to achieve full coverage of RoI with the lowest delay.
# Solid-Phase Extraction of Organic Dyes on Mixed-Ligand Zr(IV) Metal-Organic Framework
## 4 Conclusions  
In summary, a ligand-mixed Zr(IV)-MOF based on terephthalatic acid and 1,10-phenanthroline is a promising sorption material for the solid-phase extraction of organic pollutants (synthetic dyes), demonstrating a high efficiency of removing the target pollutant. The adsorption behavior has been investigated in terms of several important parameters, including adsorption kinetics, isotherms, and initial pH. The results showed that the maximum adsorption capacity of MOF reached 40 mg/g. Moreover, it has been used in practice in food samples and has shown high performance. The excellent performance of this sorbent in artificial seawater makes it a promising material for seawater treatment. Potentially, it can serve as a filler for column chromatography to separate dye molecules. The sorbent studied favorably differs from the sorbents described earlier in the literature by its versatility, since it shows good adsorption qualities with respect to both cationic and anionic dyes. The complex can also be used to extract neutral dyes using solid-phase extraction. The extraction of dyes occurs in a wide pH range of the medium and under conditions of solutions with high ionic strength (seawater). Selecting the conditions for elution, one can achieve the selective separation of dyes, which favorably distinguishes this compound from the sorbents described earlier in the literature.  
Conceptualization, I.U. and B.K.; methodology, I.U., B.K. and O.K.; writing--original draft, I.U.; writing--review and editing, I.U. and B.K.; investigation, V.Z., J.B. and O.K.; visualization, J.B., supervision, I.U. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 18: Selective separation of a mixture of dyes (methylene blue, malachite green, and Congo red) on a column filled with the studied sorbent.
## 1 Introduction  
Worldwide transport emissions, with nearly 8 Gt CO\({}_{2}\) in 2022, have grown at an annual average rate of 1.7% since 1990 [1]. With an oil dependence of approximately 91%, the transport sector accounts for approximately one-quarter of global anthropogenic CO\({}_{2}\) emissions [2].  
In the European Union, road transport represents approximately 70% of the transport sector's CO\({}_{2}\) emissions. Among the different modes, light-duty vehicles (LDVs) are by far the group with largest emissions with 60.6%, followed by heavy-duty vehicles (HDVs) contributing to 38% [3]. To face this situation, the European Union has launched a strong push to adopt more sustainable road transport driven mainly by the European Green Deal and the Sustainable and Smart Mobility strategy [4]. In addition to several aspects that cover the use of low-carbon fuels, the roll out of public charging for light- and heavy-duty vehicles, the EU established that all new cars and vans that come on the market from 2035 must have zero tailpipe emissions. In addition, the EU set the ambitious milestones of at least 30 million of these vehicles operating on European roads by 2030, and nearly all cars, vans, buses, as well as new heavy-duty vehicles being zero-emission vehicles by 2050.  
Recently, the Council of the European Union ratified the approved revision of the HDV CO\({}_{2}\) emissions standards [5]. Compared to the 2019 levels, the new standards for almost all new HDVs with certified CO\({}_{2}\) emissions must provide 45%, 65%, and 90% reductions from 2030, 2035, and 2040, respectively. In addition, all new buses providing urban services must be zero-tailpipe-emission vehicles from 2030.  
The aforementioned objectives involve a progressive substitution of the fleet vehicle technology over the coming years. This additional energy must be covered by a greater penetration of renewable energy (RE) [6; 7], which eventually puts pressure on the grid and requires a massive deployment of energy storage systems to avoid energy spill. Among the different storage technology options, hydrogen-based storage systems and their use to generate synthetic fuels for sectors where direct electrification is challenging could play a key role in increasing the RE share in the primary energy supply [8]. However, further research and development are still needed to reduce electrolysis cost [9], ensuring its viability. In addition, the hydrogen supply chain must be carefully studied and planned [10], as there is no infrastructure in place to support the technology's deployment.  
In this context, small-to-medium isolated energy systems are the most vulnerable. Their geographical conditions restrict their willingness to be self-sufficient and also generate significant economic costs [11]. In addition to this, a small power grid size is a barrier for the mass penetration of renewable sources, as large quantities can generate an imbalance in the network, endangering its stability and generating intermittency problems in almost all networks [12].
## 2 Materials and Methods
### Chromatographic System and Operating Conditions
#### 2.6.2 UHPLC-MS/MS Triple Quadrupole Method for Melatonin  
Using an Agilent 1290 Infinity series and a Triple Quadrupole 6420 from Agilent Technology (Santa Clara, CA, USA), equipped with an electrospray ionization (ESI) source operating in positive ionization mode, the method was conducted. The separation of target compounds occurred on a Sinergy Polar-RP 80 A (250 \(\times\) 4.6 mm, 4 \(\upmu\)m) from Phenomenex (Castel Maggiore, Bologna, Italy). A gradient elution with a flow rate of 0.8 mL/min was employed, utilizing a mobile phase consisting of a mixture of water (A) and methanol (B), both with 0.1% formic acid. The solvent composition was: 0-15 min, 40-85% B; 15-17 min, isocratic condition, 85% B; 17-22 min, 85-40% B. This was followed by a 10-min reconditioning of the column. A 2 \(\upmu\)L injection volume was utilized, and the column temperature was maintained at 30 \({}^{\circ}\)C, while the ionization source's drying gas temperature was set at 350 \({}^{\circ}\)C. The gas flow, nebulizer pressure and capillary voltage were set at 12 L/min, 25 psi and 4000 V, respectively. Detection in \"multiple reaction monitoring\" (MRM) was performed, and quantification was based on the integration of MRM peak areas. For quantitation, the most abundant product ion, 174.2 \(m/z\), was utilized, while 159.1 \(m/z\) was employed to confirm the analyte presence. The acquisition parameters for this analyte, including retention time, fragmentor and collision energy, are detailed in Table 2.
## 4 Experiments
### Datasets  
We have used two datasets namely DSTC-2 and WoZ-2.0, which were provided by thankful to [PERSON] [34]. The motivation behind the selection of the dataset for this study is to improve the transformer-based dialogue state-tracking performance on a single-domain dataset. Currently, DSTC-2 and WoZ 2.0 are the two famous datasets utilized by numerous researchers. Furthermore, they are easily publicly accessible and more suitable for single-domain DST. Moreover, the aim behind the selection of single-domain datasets is to provide a fundamental study in the domain of DST. The DSTC-2 dataset belongs to the dialog system technology challenge (DSTC), which is an ongoing series of research community challenge tasks. It is used in human-to-computer dialogues in the domain of information about restaurants. [PERSON], [PERSON], and [PERSON] organized DSTC-2 with the DSTC-3 dataset and the produced results were presented in sessions at SIDIAL 2014 and IEEE SLT 2014 ([[https://www.microsoft.com/en-us/research/event/dialog-state-tracking-challenge/](https://www.microsoft.com/en-us/research/event/dialog-state-tracking-challenge/)]([https://www.microsoft.com/en-us/research/event/dialog-state-tracking-challenge/](https://www.microsoft.com/en-us/research/event/dialog-state-tracking-challenge/)), accessed on 12 November 2022). The WoZ-2.0 dataset is also known as the Cambridge Restaurant Corpus dataset, collected for developing neural network-based dialogue systems. It was collected through the Wizard of Oz experiment on Amazon MTurk similar to MultiWoZ corpus [12; 35]. The WoZ-2.0 includes single-domain dialogues. Each dialogue holds a goal label and several exchanges between the system and its customer. Each user turn was labeled by a set of slot-value pairs that represent a coarse representation of the dialogue state. Most of the dialogues are finished but some of the dialogues were not in WoZ-2.0 dataset [74]. Table 2 presents the statistics of datasets as per domains associated with the dataset in column 2, and the slots which are available for the assessment of dialogue state trackers are shown in column 3. Moreover, in the following columns, we have demonstrated the total utterances, average turns, total dialogues, total words, and key points in the description column.