esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Mixed AC/DC Power Flow Algorithm in Hybrid AC/DC Grids with MT-HVDC Systems
## 4 Results and Discussions
### Case 2: AC/DC Power Flow Considering MT-Systems with Constant Active Power and DC Voltage  
In the second case, the algorithm is applied to solve the AC/DC PF problem for the studied system considering that converter stations #1 and #3 are operated in constant \(P\)-mode and converter station #2 is operated in constant \(V_{DC}\)-mode. The algorithm is converged in 0.82 s and in three iterations. Tables 3-7 show the obtained results of the mixed AC/DC PF calculations in Case 2.  
Compared to Case 1, in this case, both the active and reactive PF on each line have decreased and accordingly, the total power losses are decreased. It is observed that the generator connected to bus 2 injects its maximum active power. In addition, compared to the previous case, the total injected reactive power by the two generators is decreased.
## 3 Materials and Methods
### Platform and Flight Planning  
In the current study, the Dji Matrice 600 Pro multi-rotor hexacopter was used (Figure 2), which has an A3 Pro flight controller and is compatible with UgCS mission planning software. This device has a total take-off weight of 9.6 kg and a maximum payload capacity of 6 kg, and is powered by 6 lithium polymer batteries with a capacity of 4500 mAh.  
Figure 2: Registration system. (**A**) Mavic Matrice 600 Pro hexacopter drone; (**B**) base magnetometer for diurnal corrections; (**C**) static drone position with the magnetometer hanging below; (**D**) Terra-Tem equipment.  
The flight speed was set at 5 m per second, with a sampling interval of 200 milliseconds, allowing measurements to be taken at one-metre intervals along the recorded profile. A flight altitude of 20 m above ground level (AGL) was selected to maximise sensor resolution and ensure safety from potential obstacles. In addition, the previously acquired digital elevation model was incorporated into the flight plan. Finally, a tolerance level of 1 m was defined for altitude adjustments during the flight.  
The design and control of the project was carried out using UgCS flight software. Seven parallel survey lines of varying lengths were implemented, oriented in a N45E direction and perpendicular to the regional geological structure. A distance of 25 m was maintained between each line to achieve high spatial resolution and to capture variations in the target magnetic field (Figure 3). Given the vastness of the study area, the total flight was divided into two blocks, choosing a take-off and landing point considering the capabilities of the batteries.  
The resulting effect of the separation between the sensor and the surface leads to a slight reduction in spatial resolution and intensity, in contrast to ground-based investigations [16; 17]. However, this decrease is compensated by a constant and higher density data acquisition.
## 2 Materials and Methods
### Mixing-Settling Experiments  
Experiments were carried out using the Flocculation Tester JLT6 (VELPS Scientifica, Usmate Velta, MB, Italy). It is equipped with 6 positions for glass beakers, a digital display, an electronic speed regulation system (10-300 rpm), and a programmable timer (0-99 h). To evaluate the effect of the TSS concentration on MP retention efficacy, samples prepared with different thickened sludge:water ratios were supplemented with field-obtained MPs (collected from secondary sluges; see Table S1) so that the initial MP concentrations were within the same order in all cases (240-440 MPs/L). Two types of experiments were carried out:  
1. To test the effect of solid concentration, different sludge:water ratios were used. Six different weights of sewage sludge (1, 5, 10, 25, 50, and 100 g) were diluted with 500 mL of filtered distilled water (enriched with field-obtained MPs, within the range of 120 to 220 MPs) and were shaken for 30 min at 30 rpm. Afterward, the samples were left to rest for 3 h to favour the sedimentation process, and the supernatant was collected. The settled sludge and the supernatant were then treated following the same experimental procedure described in Section 2.3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Month** & **TSS (g/L)** & **pH** & **Moisture (\%)** & **Dry Matter** & **tCOD** & **TOC** \\  & & & **(\%)** & **(g DM/kg Sludge)** & **(g O\({}_{2}\)/kg DM)** & **(g C/kg DM)** \\ \hline June & 32.6 \(\pm\) 2.6 & 7.1 \(\pm\) 0.1 & 93.5 \(\pm\) 1.7 & 31 \(\pm\) 2 & 1050 \(\pm\) 30 & 420 \(\pm\) 3 \\ July & 32.6 \(\pm\) 4.1 & 7.4 \(\pm\) 0.2 & 94.9 \(\pm\) 2.1 & 32 \(\pm\) 3 & 1  
2. To test the effect of adding a coagulant (FeCl\({}_{3}\), 5%, Sigma Aldrich, Steinheim, Germany) and a flocculant (non-ionic granular PAM, 5 ppm, Thermo Scientific, Shangai, China), the same sludge:water ratio was used [25; 26; 27]. Specifically, 10 g of sewage sludge was diluted with 500 mL of filtered distilled water (enriched with field-obtained MPs, ranging from 164 to 174 MPs). Subsequently, 10 mL of coagulant and 10 mL of flocculant were added to two glass beakers containing the diluted sludge. A 5% solution of FeCl\({}_{3}\) (97%, Sigma-Aldrich, Germany) was used as the coagulant, while a 5 ppm solution of non-ionic granular PAM (97.5%, Thermo Scientific, China) was used as the flocculant. Jar test experiments and the sample collection were carried out as described above.  
The field-obtained MPs used to enrich the suspensions were isolated from thickened sludge collected in the DAF thickener of the studied WWTP (data available in Table S1). The MPs isolated from these samples were obtained by recovering them from the glass microfiber filters following the protocol described by [29].
## 3 Proposed Methodology
### Setup Phase  
In this paper, the sensor field is distributed into n sections, and relay nodes are arbitrarily positioned in the particular network area as shown in Figure 1. There is one fixed sink at the surface area and mobile sinks are positioned inside the sparse region. A ping message (control packet) is advertised by sink nodes across the network. This message has all the related facts of the sink node. Receiving nodes get the ping message from the fixed Sink and update its distance, when the routing path is setup, in the form of the number of hops from the sink fixed at the surface. Moreover, the receiving node sets a suitable Transmission Power Level (TPL) with the sink based upon the Received Signal Strength Indicator (RSSI). Along these lines, each node gets its distance from the sink, sets a suitable TPL, and then broadcasts this information in its transmission range. The same procedure is followed by other nodes. If a node receives a value greater than what it already has, it disposes of the ping message otherwise, it refreshes its distance information, sets a suitable TPL, and again broadcasts the ping message to nodes in its transmission range. This procedure proceeds till each sensor node sets a suitable TPL with the selected neighbors and obtains its distance from the sink. In short, control packets in the form of ping message contains a cost field which reflects the number of hops to reach the sink. In the setup phase, such control packets are propagated throughout the network. This results in establishing a minimum or shortest route from source to sink along with TPL adjustment of each node with its selected neighboring nodes. The parameters of simulation are shown in Table 1.
# Evaluation of IEEE802.15.4g for Environmental Observations
## 3 Results
### Smart Agriculture Scenario  
In this scenario, the experiment is run twice during the same day and with equal weather conditions. RX nodes are located at 213 m, 439 m and 615 m from the TX node on the first run and at 337 m, 538 m and 715 m on the second run. Table 8 shows the PDR for all the RX nodes, for packets of 127 B and 2047 B. High data rates radio setting with high PDR are achievable at least up to 337 m.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **RX at 420 m** & **RX at 700 m** & **RX at 1000 m** \\
**PHY Alias** & **PDR** & **PDR** & **PDR** \\  & **127 B–2047 B** & **127 B–2047 B** & **127 B–2047 B** \\ \hline
2 FSK-50 & 100\%–100\% & 83\%–58\% & 0\%–0\% \\
2 FSK-100 & 100\%–100\% & 76\%–0\% & 0\%–0\% \\
4 FSK-200 & 100\%–99\% & 0\%–0\% & 0\%–0\% \\
2 FSK-FEC-50 & 100\%–100\% & 95\%–94\% & 0\%–0\% \\
2 FSK-FEC-100 & 100\%–100\% & 100\%–79\% & 0\%–0\% \\
4 FSK-FEC-200 & 100\%–100\% & 73\%–38\% & 0\%–0\% \\ \hline OFDM1-100 & 100\%–100\% & 31\%–5\% & 0\%–0\% \\ OFDM1-200 & 100\%–100\% & 0\%–18\% & 0\%–0\% \\ OFDM1-400 & 100\%–100\% & 0\%–0\% & 0\%–0\% \\ OFDM1-800 & 100\%–100\% & 0\%–0\% & 0\%–0\% \\ \hline OFDM2-50 & 99\%–98\% & 23\%–59\% & 0\%–0\% \\ OFDM2-100 & 100\%–98\%At 615 m, the radio link allows communication of at least 50 kbps with high PDR value. At 715 m, only OQPSK-12.5 present a high PDR for short packets.
## 2 Related Work  
In this section, we review related works to our study. Intrusion detection methods have ranged from simple lookup tables [6] to time-series ML models [7]. Non-ML approaches, which focus on static defense methods like lookup tables, are computationally simple enough to run on the limited resources available on CAN bus hardware. Several papers have shown that with time series data alone, it is possible to accurately defend fabrication and suspension attacks [8]. Several others have found vulnerabilities in such simplistic methods using masquerade [9] or cloaking attacks [8]. These attacks mimic or alter the timing between the messages to attempt to bypass the defense.  
ML models use computationally intensive training algorithms to maximize performance and typically increase ressiliency to attacks. Many previous works have investigated intrusion detection systems for the automotive CAN bus using ML [10]. Most existing ML work frames this problem as a supervised classification task with methods like random forests [11], extreme gradient boosting (XGBoost) [12; 13], support vector machines [14], and deep learning architectures such as multilayer perceptrons (MLPs) [10; 15], convolutional neural networks (CNNs) [16], long short-term memory (LSTM) networks [17], and generative adversarial networks (GANs) [18]. Recurrent networks are also common among related tasks, such as online purchase error classification [19]. In contrast to classification, an RL approach maximizes long-term success through a reward function, explicitly choosing actions while allowing more specific tuning for desired behavior.  
Other related works in intrusion detection for a vehicle network primarily focus on the classification task and do not show how the system recovers [20; 21; 22]. In this paper, we develop an agent that detects attacks, responds by taking actions, and evaluates its performance using the aforementioned QMoCR measurement.  
Previously, RL has been applied to cybersecurity [23] and intrusion detection systems [24]. For anomalous network traffic, [PERSON] et al. [25] used deep RL for intrusion detection using Network Security Laboratory Knowledge Discovery and Data Mining (NSL-KDD) [26] and Aegean WiFi Intrusion Dataset (AWID) [27] datasets. In contrast, our work investigates using RL for maintaining cyber resiliency in a vehicle CAN bus.  
Similar to our work, [PERSON] et al. [28] explore RL for physical layer authentication in a CAN bus. However, this work focuses on analog physical layer characteristics of the message sender's transceiver and does not protect against malicious trusted ECUs.
## 2 Investigation of Subgrade Distresses in Test Area  
### Description of the Test Area  
In the first decade of the 20 th century, an unprecedented upsurge of highway construction was initiated in Guangdong, China, where the economy developed rapidly, and the construction of the backbone highway network was carried out on a large scale. By 2016, most highways had been operating for approximately ten years. The subgrade performances were relatively stable during the first 10 years of the operational period, but after the 10-year operational period, subgrade distresses were aggravated by typhoons and heavy rainfall activities in recent years. Therefore, it is necessary to select typical roads for performing an effective subgrade distress investigation, evaluation, and prediction, to reasonably plan subgrade maintenance.  
The locations of the four typical first-class highways that are crucial to the local highway network are given in Figure 1. Two different highway classifications are included: one National Highway (G105) and three Provincial Highways (S223, S270, and S356). (1) G105 is located in Conghua City, in the central part of Guangdong Province. The highway is located in the transitional zone from the Pearl River Delta to the mountain area in the north of Guangdong Province. The Quaternary weathered layer and residual soil along the line are relatively well developed, and the regional bedrock is mainly granite. (2) S223 is located in Meizhou City, in the northeast of Guangdong Province. This line is mainly distributed in mountainous and some hilly terrain areas. Broken silty sandstone is widely distributed along the route with developed joints and fissures. (3) S270 is located in the south-central part of Guangdong Province, adjacent to the Pearl River Delta. The terrain along the line is mainly plains, with a widely distributed soft foundation. This route is mainly located in a marine sedimentary zone, with flat terrain and developed regional water systems. The joints and fissures of the rock mass are developed, the joints are crisscrossed, and the degree of integration is poor. (4) S356 is located in Huizhou City in the southeast coast of Guangdong Province. There are numerous high and steep slopes along the line with broken weathered shale rock and soil distributed alternately. Five typical unit sections are selected from each highway, and the length of each unit section is 1 km. In total, twenty representative test sites from four highways are included: G105 from K2451 to K2456, S223 from K91 to K96, S270 from K100 to K105, and S356 from K5 to K10.
## 2 Related Works
#### LoRa Transmission over Water Basins
Indeed, LoRaWAN coverage experiments were performed both on land and sea: in the latter case, a range that was slightly less than 30 km was observed. However, pretty high packet loss ratios were recorded. Finally, the authors of Reference [32] showed two sets of measurements testing LoS and non-LoS conditions and exploiting either the 434 MHz and the 868 MHz ISM bands. In LoS setup, both the bands were evaluated and 22 km range was assured with off-the-shelf components. On the other hand, during non-LoS setup, only the lower band was adopted and, by taking advantage of expensive high gain antennas, 28 km were covered. However, both the end points of the LoRa links were placed some tens of meters above the sea level thus allowing to exploit, in the LoS condition, the 60% clearance of the first Fresnel zone.
## 2 Materials and Methods
### Methodology
#### 2.1.3 Data Preparation  
In this phase, the data are selected according to the most critical attributes to train and test the algorithms that will be chosen for the study.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & **Age** & **Weight** & **Height** & **Waist** & **BMI** & **Cadence** & **Steps** & **Speed** & **Stride** \\ \hline mean & 12.48 & 47.57 & 1.49 & 71.93 & 20.74 & 59.58 & 910.64 & 2.99 & 79.73 \\ std & 2.75 & 16.18 & 0.16 & 11.37 & 4.27 & 34.46 & 504.13 & 1.93 & 10.35 \\ min & 6 & 18.00 & 1.140 & 50.00 & 11.71 & 10.00 & 126.00 & 0.310 & 53.00 \\
25\% & 10 & 34.00 & 1.370 & 63.00 & 17.66 & 30.00 & 499.00 &Data selection: Through data selection, it became feasible to identify and emphasize those fields that would provide valuable contributions to the analysis of physical activity for motor skill tests. Each data record has the following attributes within the database: a. Anthropometric data: age (years), weight (kg), height (m), sitting height (cm), and waist circumference (cm). b. Average pace: the time the person can walk a kilometer; they are a number in minutes and seconds format. c. Average cadence: these are the steps per minute you can do; they are raw numbers. d. Steps: these are all the steps the person has taken during the activity; they are raw numbers. e. Calories: the calories the person has burned during the activity; they are numbers without formats. f. Average speed: the average speed at which the person has moved during the activity in kilometers/hour; it is in number format with decimals without arrangements. g. Average stride: it is the average distance taken by each step; they are numbers without formats. h. Heart rate: these are the beats per minute the heart has given during the activity; they are numbers without formats. i. Maximum heart rate: this is the maximum number of beats per minute the individual has given in the activity; they are numbers without formats.
2. Data cleaning: Data cleaning tasks allowed us to discover correct and sometimes eliminate erroneous data records or outliers and convert and standardize the data types necessary for processing in machine-learning algorithms. The Jupyter dashboard [23] was used with the Python 3 programming language, with its Pandas library; it is a rapid, robust, adaptable, and user-friendly open-source tool for data analysis and manipulation. The Seaborn library was used with its boxplot function to visualize the classes, as shown in Figure 1, where the high class has the highest proportion for both sexes. Points outside a boxplot are visual indicators of values that may be unusual or outliers compared to the rest of the data in the set.  
Figure 1: Types of motor competence according to the tests the male and female schoolchildren carried out.
# Data Attributes in Quality Monitoring of Manufacturing Processes: The Welding Case
## 4 Results  
As the data for each case study have been captured using the same hardware, their differences can be mainly found in the duration. For the first case study, the duration of a single welding event is 200 ms, thus, with the triggering signal for recording slightly preceding and succeeding the corresponding starting and finish points, the overall duration of the video is around 300 frames. On the other hand, in the RSW case, the cool-down of the spot, due to the larger welded area and different heat capacity of the specimens, the recording time is prolonged. This results in videos with a duration of up to 5000 frames.  
Considering that the camera's resolution is 32 by 32 pixels and each pixel is represented by a 10-bit (2-byte variable), the total amount of data for a single instance is 600 Kb and 9.76 Mb for the two cases. Apparently, this type of classification problem could create enough data volume that is problematic for subjectively big data. However, it is also shown hereafter that other Vs, such as veracity and value, can be accounted for during data modeling. For simplicity, smaller datasets and simpler cases have been taken into consideration.  
To cope with the inherent thermal noise, the pixel noise was dynamically modeled, and a criterion that is capable of separating background and foreground areas was derived. By constructing a confidence map (pixel-wise confidence), a foreground mask was extracted. The initial and final results of noise cancellation are depicted in the following figure (Figure 4).  
In the case of RSW, except for noise cancellation, drift correction was also required due to the prolonged recorder duration. Thus, a heuristic procedure was used to compensate for the linear trend of the data (Figure 5). Herein, the algorithm calculates the mean of each frame, constructs a signal, and fits it into a linear equation. Then it uses the coefficients of this equation to filter all the video pixels, which were defined as vectors (arrays) in a 2D space based on their value and frame number (Figure 5).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Veracity Code** & **Veracity Factor** & **Value Code** & **Value Factor** \\ \hline P1 & All steps included & F1 & PCA: 3 PC \\ P2 & Remove video stabilization & F2 & PCA: 5 PC \\ P3 & Remove thermal-drift correction and & & \\  & noise cancelation & F3 & PCA: 10 PC \\ \hline \end{tabular}
\end{table}
Table 4: Case study II—Factors affecting the Veracity (grey) and Value (white) of data.  
Figure 4: Noise cancelation.
## 2 Related Work
### QGS Based on LLMs and Intelligent Agents  
Inspired by the successful application of LLMs in test question generation [26; 27], the generative core of our Multi-Examiner system consists of LLMs and intelligent agents, enabling sophisticated natural language question formulation. However, they often lack the necessary domain-specific knowledge and may produce homogeneous questions, resulting in insufficient diversity and weak relevance among distractors. This section examines how these technologies have evolved and identifies these challenges as opportunities for improvement that our system addresses.  
Building on knowledge representation technologies, integrating LLMs and intelligent agents has transformed question generation capabilities [28; 29]. This progress has evolved through three phases: initial transformer-based architectures, specialized agent integration, and hybrid systems. LLM-based systems now achieve significant accuracy in generating contextually relevant questions, a notable improvement over traditional methods [30].  
Advances in LLM architecture have enhanced contextual awareness and semantic understanding, achieving improved educational relevance compared to prior systems [31]. Improved attention mechanisms enable these models to better align questions with learning objectives [32]. Multi-agent architectures bring further improvements by decomposing question generation into specialized tasks, enhancing question diversity and alignment with learning outcomes [33; 34]. Domain-specific knowledge integration has also improved precision in specialized subjects [35]. Research continues to optimize the synergy between LLMs and reasoning components, improving the generation of higher-order cognitive questions [33]. Neural reasoning integration enables enhanced causal reasoning and conceptual understanding [36].  
Despite these advancements, significant challenges persist that our Multi-Examiner system aims to address. Performance variability remains across question types and difficulty levels [37]. This inconsistency directly relates to our second research question on improving question diversity. Complex reasoning tasks show decreased performance, and factual accuracy remains a challenge in specialized educational domains [38]. These limitations impact the generation of higher-order thinking questions and contextually relevant distractors--central concerns in our first and third research questions. Our Multi-Examiner system addresses these challenges through specialized agents that focus on distractor generation and cognitive alignment, working in concert with domain-specific knowledge structures.
## 2 Materials and Methods
### Data Analysis Methods  
#### 2.4.1 Correlation Analysis  
To compare the correlation between two signals or vectors, the cosine similarity formula was used for calculation. Suppose there are two vectors of dimension \(K\) with \(g\) and \(l\), respectively. Their correlation coefficients are \(S_{gl}\) with values in the range \((-1,1)\), \(1\) being positive correlation and \(-1\) being negative correlation.  
\[S_{gl}=\frac{\sum_{k=1}^{K}g(k)l(k)}{\sqrt{\sum_{k=1}^{K}g(k)^{2}\times\sqrt{ \sum_{k=1}^{K}l(k)^{2}}}},1\leq k\leq K \tag{7}\]