esa-sceva/satcom-chunk-collection huggingface dataset:
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 3 Smart Meter Technological Solutions in Europe and Projects for Their Implementation
### Concluding Remarks  
As it can be concluded, many energy providers have started to replace their traditional meters with smart ones, whereas many relevant projects have taken place. The situation is not homogeneous throughout Europe. Some countries have made significant progress towards their smart meter roll-out, whereas in others pilot programs are planned to be realized in the future. It is also noteworthy that some countries apply different smart metering technologies, whereas in others there is a dominant technology chosen. Power Line Communications play an important role when it comes to smart metering applications. However, there is a variety of standards/specifications that can be selected. Table 2 summarizes the information provided in Section 3, with respect to the main technologies applied for smart meters in several countries. It should be highlighted at this point that the list of technologies and the standards/specifications that are mentioned to be used throughout Section 3 is not exhaustive. The technologies that are pointed out represent some cases of applications or in some occasions, the main technologies selected for a large-scale smart meter roll-out, like the case in Italy. The fact that other technologies can also be used or that have already been selected for specific applications is not excluded. Furthermore, in this Section, some information is also provided about rough estimations on investments and the quantity of smart meters installed, which refer to a specific project/actions taken by a specific energy provider. Other projects are likely to have taken place, involving other investment and smart meter scenarios.  
It is also worth noticing that in some cases, emphasis is also given to the communication between the smart meter and a device for customer information purposes, like the In-home Display. In the UK, the technology specified for this scope is wireless (second version of Smart Metering Equipment Technical Specifications) and in particular, the ZigBee specification should be supported within the band of 2400-2483.5 MHz. In Germany, one of the energy providers has enabled customer information regarding electrical consumptions through the Internet.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**County** & **Technology** & **Standard(s)/Specifications (If Available)** \\ \hline Italy & NB-PLC & Meters \& More \\ Spain & NB-PLC & Meters \& More, PRIME \\ France & NB-PLC & G3-PLC, IEC 61334-5-1 \\ UK & NB-PLC, WAN & IEC 62056-21, Communication based on open standards (some cases) \\ Germany & PLC, GPRS & IEC 62056-21, IEC 14908 (some cases) \\ Sweden & NB-PLC, GSM/GPRS & IEC 62056-21, IEC 14908 (some cases) \\ Greece & NB-PLC, GSM/GPRS &  
\end{table}
Table 2: Some of the technologies used by country for smart metering data transmission.
# A Prototype Mechatronic Device for Upper Limb Rehabilitation and Analysis of Its Functionality
## 3 Concept and Design Solution for the Rehabilitation Device  
### Functional Assumptions of the Device  
During the design process, contextual factors were identified based on criteria outlined in [79]. These factors are critical and influence the structure of rehabilitation technologies [80]. The needs and specifications for a potential home-based rehabilitation device that supports independent arm exercises have been summarised in several studies [81; 82; 83]. The key functional assumptions of the proposed device are as follows [84]:  
1. The rehabilitation device must not require the installation of sensors or mechatronic components directly on the user's limb.
2. The solution should enable users to physically perceive the shape and texture of the manipulated objects and support the diagnosis of insufficient grip strength based on surface properties, shape, or weight. Additionally, colour coding may assist in object identification and ensure correct placement.
3. The system should promote natural limb movement using real objects of varying shapes and weights. A mechatronic mechanism should facilitate the adjustable positioning of the arms and containers, thereby extending the range of motion and enhancing the rehabilitation process.
4. The mechatronic system and accompanying software should incorporate diagnostic functionalities, including assessments of task completion time, object shape and weight, and the number of repetitions in cases of an incomplete or impaired task performance.
5. The device should have a universal and adaptable design, allowing for adjustment to different limb lengths and configurations. It should be portable and suitable for use on tabletops or desks.
## 2 Real-Time Monitoring of Power Distribution Systems
### Smart Grids  
A smart power grid involves information-based applications made available by increasing automation and communication infrastructure. Among the components present in future power grids are SMs, power devices, energy storage, intelligent appliances, and software infrastructures such as interoperability standards and cybersecurity protocols. The motivations for the development of these applications are network resiliency, performance, and efficiencies generated by automating PDSs [23]. SGs aim to modify the power grid to create a powerful system. They do this by incorporating various control modes and automated functions. SMs are used to record energy consumption and other information such as energy quality indicators and the measurements are sent via upstream messages in response to requests from DOCs. On the other hand, it is possible to receive price signalsor even have some level of control that can be incorporated into the SMs. In addition, they can also establish a path of communication with the nearest meters [24].
# Leveraging Large Language Models for Enhancing Safety in Maritime Operations
## 6 Challenges and Limitations
### Operational Challenges  
#### 6.3.1 Training and Skill Requirements  
The successful integration of Large Language Models (LLMs) into maritime operations is heavily dependent on the proficiency of the crew and staff who interact with these technologies. Introducing LLMs necessitates comprehensive training programs to equip personnel with the necessary skills to operate and interpret these advanced systems effectively. This training extends beyond basic operational instruction to include an understanding of the underlying principles of artificial intelligence, data security considerations, and the limitations of the technology [173; 174; 175].  
The necessity for crew training on new technologies cannot be overstated. Maritime personnel are traditionally trained in specific operational protocols and may not possess the technical background required to engage with AI-driven tools confidently. Tailored educational programs should be developed to address this gap, focusing on practical applications relevant to their roles. Simulation exercises, interactive workshops, and ongoing support can enhance the learning experience and promote competence in using LLMs [173; 174; 175].  
Addressing resistance to change is a critical aspect of the implementation process. Crew members may exhibit reluctance due to fear of job displacement, uncertainty about the reliability of AI systems, or discomfort with altering established routines. To mitigate these concerns, it is essential to foster a culture of inclusivity and transparency. Involving personnel in the planning and deployment phases can encourage ownership and acceptance. Clear communication about the benefits of LLMs, such as improved safety and efficiency, and reassurance that these tools are designed to augment rather than replace human expertise can alleviate apprehensions [173; 175; 176].  
Moreover, leadership should actively engage with staff to solicit feedback and address any issues that arise during the transition. Providing avenues for open dialogue ensures that concerns are heard and acted upon, thereby enhancing trust and collaboration. By prioritizing training and addressing resistance proactively, organizations can facilitate a smoother integration of LLMs into maritime operations [173; 174; 175; 176].
## 3 Test Result Analysis  
### Stress-Strain Curve  
Upon conducting unconsolidated consolidated undrained (UU) triaxial tests, we obtained the stress-strain curves of Fifty-four C-M-S specimens under different dry-wet cycles and temperatures. The stress-strain curves under the three confining pressures are plotted in Figure 6. The shear strength was considered as corresponding to the peak deviator stress or the deviator stress corresponding to 15% axial strain (when there was no peak) of the stress-strain curve.  
Figure 5: (a) IPP 6.0 software analysis, (b) Box-counting dimension diagram.  
From Figure 6, we noted that the stress-strain curves exhibit a two-stage nonlinear relationship: (a) Elastic-plastic stage: the stress-strain curve was nonlinear below the peak deviator stress. (b) Strain softening stage: the deviator stress decreased with increasing strain after the CMS reaches its peak deviator stress.  
The shear strength index (cohesion \(c\) and net friction angle \(\phi^{\prime}\)) and microscopic parameters (plane fractal dimension \(D_{\rho}\) and probability entropy \(H_{\text{n}}\)) under different dry-wet numbers (\(N\)) and temperatures (\(T\)) are shown in Table 3. The peak deviator stress of the CMS under different confining pressures was lower than 277.10 kPa. This result indicated that the shear strength of the CMS was very low. Moreover, a higher temperature corresponded to a lower peak deviator stress. For instance, at the confining pressure of 100 kPa and 0 cycles, the deviator stress at 0 \({}^{\circ}\)C was 150.79 kPa, which was 1.34 times the stress at  
Figure 6: Stress–strain curves of C-M-S samples. (a) 0 \({}^{\circ}\)C, (b) 35 \({}^{\circ}\)C, (c) 70 \({}^{\circ}\)C.  
70 \({}^{\circ}\)C (112.49 kPa). This result indicated that the temperature significantly degrades and deforms the CMS. In addition, for a given confining pressure, the peak deviator stress of the CMS sample decreased gradually with increasing temperature.
## 3 Results
The blue curve represents the valid positive rate (sensitivity) as a function of the false positive rate, while the gray dashed line indicates the random classification baseline. The model's area under the curve (AUC) score is 0.939, indicating high precision and strong discriminative capability in detecting P waves within seismic waveforms. Figure 9: Optimal time window length identification. Relationship between time window length (in seconds) and classification accuracy (%). A confusion matrix visually represents the classification model's performance, illustrating the number of correctly and incorrectly classified instances. Figure 11 presents the confusion matrix for the model that predicts the presence or absence of P waves. The main diagonal (0.987 and 0.891) represents the correctly classified cases:  
* 98.7% of noise samples were correctly identified as noise;
* 89.1% of P-wave occurrences were accurately detected. The off-diagonal elements indicate classification errors:  
* 1.3% of noise samples were incorrectly classified as P waves (false positives);
* 10.9% of actual P waves are misclassified as noise (false negatives). Although the model demonstrates high accuracy, a small proportion of errors remains. These misclassifications suggest areas for potential refinement, such as improving feature extraction techniques or fine-tuning decision thresholds to further enhance the performance of P-wave detection. In addition to numerical evaluation metrics, visual analysis of results plays a crucial role in assessing model performance (Figure 12). This approach provides a qualitative understanding of how the model detects P waves within the seismic waveform over time. The key moments of signal detection are highlighted on the seismograms. This visualization allows for a direct assessment of the model's ability to pinpoint P-wave arrivals accurately, facilitating an intuitive evaluation of detection reliability and potential misclassifications. Figure 12 presents a seismogram recorded using the HHZ channel. The blue-shaded region represents the time interval analyzed by the model, while the red line marks the exact moment when the model detected the P-wave. Before this threshold, the signal exhibits low amplitude, indicative of seismic. Following the detection point, a sharp increase in amplitude and distinct changes in waveform characteristics indicate the onset of a seismic event. This visualization method represents the model's performance, allowing for an intuitive assessment of signal behavior and the precision of P-wave arrival detection. Figure 10: ROC curve of the classification model.
## 5 Conclusions  
The effect of loading frequency on soil liquefaction is not well understood, and test results have been contradictory. Most results have been derived from cyclic triaxial tests conducted on saturated sand, and studies have rarely been carried on cyclic direct simple shear tests, which can better simulate the vertically propagating shear wave caused by earthquake loading. In this study, a series of CDSS tests were performed to investigate the effect of the loading frequency on the liquefaction resistance of clean, dry Nakdong River sand. The major findings of this study are as follows:  
1. The liquefaction resistance of dense sand was greater than that of loose sand for the same loading frequency and initial vertical effective stress. Furthermore, dense sand was more sensitive to the test conditions. While an increase in the loading frequency and/or a decrease in the initial vertical effective stress caused a significant increase in the liquefaction resistance of dense sand, its influence on loose sand was small.
2. An increase in the initial vertical effective stress resulted in a decrease in the liquefaction resistance. The liquefaction resistance of dense sand decreased significantly because the high initial vertical effective stress inhibits the dilative behavior of sand particles.
3. The accumulated shear strain was influenced by the loading frequency. For given experimental conditions, the number of cycles required to attain a given level of accumulated shear strain increased with the loading frequency.
4. The loading frequency affected the rate of excess pore pressure accumulation. For a constant amplitude of cyclic stress, an increase in loading frequency led to a decrease in the accumulated excess pore pressure.
5. The loading frequency exerted a strong influence on the cyclic resistance for both loose and dense sand. Thus, it is recommended that more than one loading frequency be used to assess the cyclic resistance of sand in laboratory studies through CDSS tests.  
Conceptualization, Z.N. and S.-S.P.; methodology, Z.N. and S.-S.P.; experiments, Z.N.; validation, S.-S.P.; S.-W.J. and D.-E.L.; formal analysis, Z.N., S.-S.P., and S.-W.J.; investigation, Z.N. and S.-S.P.; resources, Z.N. and S.-S.P.; data curation, Z.N. and S.-S.P.; writing--original draft preparation, Z.N. and S.-S.P.; writing--review and editing, Z.N. and S.-S.P.; visualization, Z.N., S.-S.P., and S.-W.J.; supervision, S.-S.P. and D.-E.L.; project administration, S.-S.P.; and D.-E.L.; funding acquisition, S.-S.P. and D.-E.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by grants from the National Research Foundation of Korea (NRF-2018R1A5A1025137) funded by the Korea government, and the Basic Science Research Program through the National Research Foundation of Korea (NRF-2019R1F1A1058869) funded by the Ministry of Education.  
The authors declare no conflicts of interest.
# Simulation of Achievable Data Rates of Broadband Power Line Communication for Smart Metering
## 6 Simulation--Basic Topologies
### Summary--Influence of Individual Parameters on Throughput  
According to simulation results in the previous section, the following parameters influence the throughput significantly:  
* **Background noise:** The most significant source of throughout degradation caused by the different types of noise in LV networks.
* **Direct line length:** The capacity of the cable channel decreases almost logarithmically with increasing length of the cable between transmitter and receiver. The capacity decreases approximately by 4 Mbps from 22 Mbps to 18 Mbps for 150 m of cable between transmitter and receiver. This capacity drop is less significant for longer distance between transmitter and receiver. For example, some simulation results [61] show capacity decrease with increasing length of the cable between transmitter and receiver.
* **Number of branches:** The capacity decreases approximately by 2 Mbps with an increase in the number of branches (two and three).
* **Branch (tap) length:** The increasing length of a branch caused decreasing channel capacity, but for a large distance between transmitter and receiver it is not significant. The drop of 0.23 Mbps from 3.15 to 2.92 Mbps is caused by increasing length of a branch from 15 m to 45 m.
* **Terminal load branch impedance:** The changes of terminal load impedance lead to deep notches in CTF, therefore the throughput changes slightly.
# Harvesting the Aggregate Computing Power of Commodity Computers for Supercomputing Applications
## 3 Design and Implementation
### High-Performance Data Analytics Cluster  
The low-cost, distributed, and data-intensive cluster, known as the HPDA cluster, has been set up in the designated laboratory. Distributed computing is just a distributed system where multiple machines perform specific work simultaneously. While performing the work, machines communicate with each other by passing messages between them. Distributed computing is practical when fast processing (computation) is required on big data. Apache Hadoop [20] and Apache Spark [21] are well-known examples of big data processing systems. The HPDA cluster comprises Apache Hadoop and Apache Spark. The cluster specifications and diagram are illustrated in Table 2 and Figure 2 respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Used Values** \\ \hline Number of Nodes: & 10 (1 Master, 9 Slaves) \\ Cores in Use: & 72 \\ Available Memory: & 24.9 GB \\ Available HDFS: & 6.4 TB \\ \hline Master Machine & CPU 2.25 Ghz \\  & RAM—4 GB \\  & HDD 1 TB \\  & OS—Ubuntu 16.04 LTS \\ \hline Ethernet Switch & 16 Port 10/100 Mbp \\ \hline Software Tools & Hadoop 2.7, Spark 2.1.0, \\  & Scala 2.12.1, OpenJDK 1.8 \\  & Hive 2.0.0, HBase 1.1.4 \\  & Flume 1.6.0, Anaconda3 4.3.0 \\  & R 3.3.2, RStudio 1.0.136 \\ \hline Language Support & Scala, Java, Python, R, \& MapReduce \\ \hline \hline \end{tabular}
\end{table}
Table 2: HPDA Cluster Specifications.  
Figure 2: HPDA cluster diagram.
# Reliability Modelling and Evaluation for LTD System Based on Load-Sharing Model
###### Abstract  
Based on power adding technology, the linear transformer driver (LTD) scheme is widely used to generate high-energy pulsed outputs and adopts a hierarchical and modular structure. Although robust design and fault analysis for basic components have been conducted recently, there is still a lack of enough reliability analysis studies of the whole system. Taking an actual LTD system as an object, this paper presents a system reliability model based on a load-sharing mechanism. A unified load-sharing rule structure is established and four typical rules corresponding to equal, linear, exponential, and local-equal relationships are discussed in detail while evaluating the impact of the load-sharing mechanism. Subsequently, simulation experiments are performed to illustrate the effects of different load-sharing rules as well as analyzing the system reliability in which we simultaneously propose a self-adaptive Monte Carlo simulation flow to achieve the sampling probability adjustment according to the random failure sequence. The simulation results can serve as a suggestion for further improvement of the system reliability. Moreover, the model framework and the simulation analysis method described here are universal and can be applied to evaluate the reliability of other LTD-based systems with tiny modifications.  
L +
Footnote †: journal: Applied Sciences  
_Article_
## 3 Methodology  
### System Architecture of CLSTM-MT  
We will explain the system architecture of the CLSTM-MT model in this section. The encrypted traffic classifier structure of the CLSTM-MT model is illustrated in Figure 1. The model is divided into two parts, namely the student model and the teacher model, which has the same network model architecture, but the parameters are updated in different ways. The CNN component of the model is composed of six layers: the first convolutional layer, the first pooling layer, the second convolutional layer, the second pooling layer, a flattening layer, and a fully connected Softmax layer. The BiLSTM component of both models defines two bidirectional classifiers to process the sequential data of the traffic. The parameters include the input dimension, the hidden layer dimensions, and the number of layers. The full connection layer is used to map the output to the categories required for the classification task.  
The input data for the model consist of four npy feature files, representing the preprocessed traffic data. In the training process, the maximum and minimum value approaches are used to normalize the data, mapping the data values from the range [0, 255] to [0, 1], and the output of the model is the classification result for the samples.
# Optimization Method of Square Hole Measurement Based on Generalized Point Photogrammetry
## 3 Generalized Point Photogrammetry Based on Lagrange Multiplier Method  
When using the principle of photogrammetry to calculate the coordinates of space points, there are usually redundant observations. When using multiple observations for adjustment, it is necessary to build an adjustment equation group [37]. The error equation is shown below.  
\[V=BX-l \tag{18}\]  
where, \(V\) is the error, \(V\) is also an explicit function of \(X\), \(B\) is the coefficient matrix, \(X\) is the correction of unknown number, and \(l\) is the difference between the measured value and the observed value. The constraint equation is as follows.  
\[CX+W_{X}=0 \tag{19}\]  
where \(C\) is coefficient matrix, \(W_{x}\) is a constant. From the above error Equation (18) and constraint Equation (19), the Lagrange multiplier method can be used to construct the following equation for solution.  
\[F(X)=VPV+2\lambda(CX+W_{X}) \tag{20}\]  
The above Formula (20) has the following formula for the derivative of \(X\).  
\[V^{T}PB+\lambda^{T}C=0 \tag{21}\]After transposing the above Formula (21), there is the following formula.  
\[B^{T}PV+C^{T}\lambda=0 \tag{22}\]  
The following formula can be obtained by substituting Formula (18) into Formula (22).  
\[B^{T}PBX+C^{T}\lambda-B^{T}Pl=0 \tag{23}\]  
The following formula can be obtained from the above formula.  
\[N_{B}X+C^{T}\lambda-W=0 \tag{24}\]  
where, \(N_{B}=B^{T}PB,W=B^{T}Pl\).  
The following formula can be obtained by multiplying \(CN_{B}{}^{-1}\) left by Formula (24). The following formula can be obtained by combining the above Formulas (19) and (24).  
\[CN_{B}{}^{-1}C^{T}\lambda-CN_{B}{}^{-1}W+W_{X}=0 \tag{25}\]  
The following formula can be obtained from the above formula.  
\[\lambda=CN_{B}{}^{-1}C^{T}(CN_{B}{}^{-1}W+W_{X}) \tag{26}\]  
The following formula can be obtained by substituting the above formula into (24).  
\[X=(N_{B}{}^{-1}-N_{B}{}^{-1}C^{T}N_{C}CN_{B}{}^{-1})W-N_{B}{}^{-1}C^{T}N_{C}{ }^{-1}W_{X} \tag{27}\]  
where, \(N_{C}=CN_{B}{}^{-1}C^{T}\).