esa-sceva/satcom-chunk-collection huggingface dataset:
# Human-AI Collaboration for Remote Sighted Assistance: Perspectives from the LLM Era +
## 3 Identifying Challenges in RSA: Literature Review
### Challenges in Obstacle and Surrounding Information Acquirement and Detection  
The second notable challenge that agents face is obtaining information about obstacles and surroundings. RSA agents need to detect obstacles vertically at ground level to head height and horizontally along the body width [8; 42; 94]. They also need to provide information about dynamic obstacles (e.g., moving cars and pedestrians) and stationary ones (e.g., parked cars and tree branches) [10; 94]. However, agents found these tasks daunting due to the difficulties in estimating the distance and depth [13], reading signages and texts [43], and detecting/tracking moving objects [43; 94] from the users' camera feed. A number of research studies have also found that it is almost impossible for agents to project or estimate out-of-frame potential obstacles, whether moving or static [8; 94; 10; 11; 13; 42; 94]. Researchers [62] describe that navigation environments in the real world are dynamic and ever-changing. Thus, it is easier for agents to detect obstacles and provide details when users are stationary or moving slowly [43]. Two main causes are linked with theaforementioned problems: (1) limited field of view (FOV) of the camera and (2) limitation of using video feed.
## 2 Materials and Methods
### The Positioning of the Proposed Model in the Existing Literature
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Source** & **Method** & **Maintenance Integration** & **Unplanned Events** & **Small-Batch Focus** \\ \hline Proposed model (this paper) & MIP-based model with integrated dynamic planning and maintenance scheduling & Yes & Yes & Yes \\ [PERSON] et al. (2015) & Heuristic (genetic algorithm) for joint job-shop scheduling and maintenance & Yes & No & No \\ [PERSON] et al. (2021) [11] & NSGA-II multi-objective optimisation for flexible job shops & No & No & No \\ [PERSON] and [PERSON] (2010) [9] & Heuristic with fixed maintenance intervals & Yes & No & No \\ [PERSON] et al. (2012) [21] & Joint model for production scheduling and predictive maintenance & Yes & No & No answer \\ [PERSON] et al. (2022) [14] & Simulation and online rescheduling using Imperialist Competitive Algorithm (ICA) & Yes * (not preserving) & Yes & Yes \\ \hline \hline \end{tabular}  
*: Although the authors of [PERSON] et al. [14] formally address all three aspects, their model focuses solely on reactive maintenance and does not include preventive or condition-based strategies. Further differences can be observed in how production priorities and available time slots are handled.  
\end{table} 
Table 1: A comparison of the selected literature approaches with the model proposed in this paper regarding maintenance integration, the handling of unplanned events, and applicability to small-batch production. tion priorities. This ensures that high-priority jobs are not delayed by rigid maintenance schedules, and that maintenance is strategically timed according to real-time conditions.
# A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting
## 6 The Challenge and Future Direction of Solar Energy Forecasting Using Deep Learning
5. Hybrid physical forecasting models: In general, it is normal to include numerical weather forecasting data in short-term forecasting models to improve solar energy forecasting performance. However, it is challenging to determine an effective way to include the relationships between many ground measures in a deep learning-based forecasting model. Thus, this topic is considered one of the most important future directions of study.
# Correction of Field-Measured Wind Speed Affected by Deterministic Interference Factors
###### Abstract  
The observations of meteorological wind speed may be biased due to the influence of various distractions. Therefore, the original measured data should be corrected in a targeted manner. Wind tunnel tests (WTT) and computational fluid dynamics (CFD) numerical simulation methods are used to study the local wind environment characteristics of observation sites in Noi Ling Ding Island (NLDI) and Ping An International Financial Center (PAFC) in the Shenzhen area. The interference effects of NLDI and the building components and instruments in the hollow-layer atop PAFC on their corresponding anemometer measurement results were analyzed, and a quantitative description was provided with the wind speed influence coefficient (\(C_{\text{VI}}\)), which is the wind speed ratio in disturbed and undisturbed cases. Results show that the \(C_{\text{VI}}\) of WTT at the NLDI site is slightly higher than that of CFD, and the wind speed in the wind-sensitive direction is accelerated by 12% due to the influence of NLDI. The large pillars at the corner of PAFC have a considerable occlusion effect on the wind speed under the wind direction of 45\({}^{\circ}\). An acceleration effect with \(C_{\text{VI}}\) of 1.163 is found in the dominant wind direction when instruments are absent atop the hollow-layer, whereas a sheltering effect is observed with the \(C_{\text{VI}}\) of 0.593 when instruments are present. These results are used to correct the recorded wind speed at the NLDI and PAFC sites during Super Typhoon Manghut with No. 201822, and then converted into the 10 min mean wind pressure value at 10 m height under the standard lalform. The reference wind pressure values obtained are 0.526 and 0.505 kPa. The analogous conversion values achieve the purpose of mutual verification, and the effectiveness and reliability of the methodologies are presented.  
field measurement; distraction; wind speed correction; WTT; numerical simulation +
Footnote †: journal: Applied Sciences
# Genomic and Metabolic Characteristics of the Pathogenicity in Pseudomonas aeruginosa
## 4 Metabolic Pathways Associated with Pathogenicity
### Antibiotic Resistance  
The use of antibiotics to treat infectious diseases has proven clinically successful, although researchers say that success comes at the expense of resistance. There are several reasons for acquiring the various types of resistance presently encountered. The direct cause of drug resistance is exposure of microorganisms to antibiotics, which leads to the natural selection of individuals with resistance genes to various antibiotics [158]. The plasticity of the bacterial genome, through spontaneous or induced mutations, also represents one of the main factors for the acquisition of antibiotic resistance. The horizontal transfer of genes or transmissible plasmids are also responsible for the acquisition of resistance genes among bacteria [159]. Therefore, recently, several studies have been conducted to develop new strategies to combat multidrug resistance pathogens. For example, [PERSON] and collaborators demonstrated in their studies that two metal complexes, Ag-phendione and Cu-phendione, as powerful inhibitors of lasB, since these complexes are able to interact with the active site of lasB and decrease the activity of purified lasB and from the secreted lasB. These complexes can be a promising avenue for the treatment of antibiotic-resistant _P. aeruginosa_ through the lasB target, since this enzyme promotes several pathophysiological events during bacterial-host interactions [160]. Metal-based antibacterial complexes have been studied as an alternative to conventional antibiotics due to their differentiating modes of action and effective in combating pathogens [161]. Namely, silver, gold, bismuth, ruthenium, copper and manganese are the most studied compounds whose effects show a promising impact in combating antimicrobial resistance. These metal-based antibacterial complexes act essentially at the level of the DNA, RNA and bacterial membrane, disrupting the basic survival functions of the bacteria [161; 162]. In addition to these more modern alternatives, the use of phages and bacteriocins has also been studied as an alternative to antibiotics [163; 164].  
_P. aeruginosa_ infections are increasingly difficult to treat, as this pathogen has a natural resistance to multiple antibiotics, and the number of its multidrug-resistant strains is increasing globally [165]. Many antibiotics are used to treat _P. aeruginosa_ infections, including a number of \(\beta\)-lactams such as cephalosporions (e.g., ceftazidime), carbapenem (e.g., meropenem, impienem), and monobactams (e.g., aztreonam), aminoglycosides (e.g., gentamicin, tobramycin, amikacin), fluoroquinolones (ciprofloxacin, levofloxacin), and, recently, polymyxins (e.g., colistin, polymyxin B). Normally, the major mechanisms used to counter antibiotic attack can be classified into intrinsic, acquired, and adaptive resistance [151].
## 5 Simulation Experiments and Numerical Results Analysis
### Experiment Setup  
To evaluate the efficiency of urban signalized intersections, this study selected two indicators: average vehicle delay and average number of stops. These indicators were used to analyze the effectiveness of the speed guidance strategy proposed in this study.  
(1) Simulation Scenario Construction  
Figure 10: Time domain optimization strategy of CAV.  
Road Network: Utilizing the \"netedit\" tool, a city road network with a two-lane intersection was created. The road lengths for the east, south, west, and north approaches to the intersection were set to 300 m, 50 m, 50 m, and 50 m, respectively, with traffic signals installed at the intersection.  
Vehicle Definition and Generation: Three types of vehicles were defined: CAV, DCAV, and HDV, each with a length of 5 m. At the beginning of the simulation, the generation probabilities of CAV and HDV were controlled to obtain traffic flows with different CAV penetration rates, ensuring a random distribution of CAV and HDV.  
Simulation Process: The simulation step length was set to 1, updating vehicle positions and speeds within each time step.  
Traffic Parameter Settings  
Road Parameters: The maximum speed for all roads in the simulation was set to 11.1 m/s, with each lane width set to 3.5 m, the standard width for urban roads. To simulate realistic conditions at urban road intersections, white solid lines were placed within 60 m of the intersection to prohibit lane changes in this area.  
Vehicle Behavior Model: Vehicles entered the road at random speeds with their departure positions set to zero. The car-following model used was the mixed traffic flow car-following model established in Section 2 Formulas (1)-(3),while the lane-changing model employed was the Krauss model.  
Traffic Signal Configuration: Traffic signals in the simulation had fixed phases, with red light time, all-red time, green light time, and yellow light time set to 14 s, 3 s, 30 s, and 3 s, respectively. The simulation scenario is depicted as illustrated in Figure 11.
## 2 Study Area and Dataset  
A study area located near Lake Ontario in Scarborough, Ontario, Canada was tested in this research. The study area covers a variety of land features, objects, including bare soil, roads, grass, shrubs, trees, and buildings, as well as part of Lake Ontario (Figure 1), and it includes a land/water interface with a gentle slope. A flight mission was conducted on 3 September 2014 to acquire multispectral LiDAR data using the Optech Titan. The acquired data were received from the supplier as a time-tagged 3D point cloud file with multiple returns in LASer file format (LAS) for each channel. The LAS data files contain xyz coordinates, raw intensity values, scan angle, return number, number of returns, and the GPS time of each LiDAR point.  
A 400 m by 200 m subset of LiDAR data was clipped for the experimental testing. Table 1 summarizes the specifications of the data subset. The LiDAR data of the three channels are shown in Figure 2. The difference in the number of points between channels is attributed to the interaction of each feature with different wavelengths (e.g., reflection from the water surface and/or water benthic layer, and greenness of the vegetation).  
As shown in Figure 2, the highest elevation in the three channels is very close, while the lowest elevation in C3 is lower than that in C1 and C2 by about 5 m. This is attributed to the recorded points from the water benthic layer. The intermediate part of the water body has high intensity variation in all channels. In C2, the high vegetation (i.e., trees) has a relatively high intensity variation. The water points have single returns in C1 and C2, while most of them have double returns in C3 due to the reflectance from the water surface as well as water benthic layer as it is a shallow water area.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Specification** \\ \hline Wavelength & Channel 1 = 1550 nm; Channel 2 = 1064 nm; Channel 3 = 532 nm \\ Altitude (Above Ground Level) & \(\sim\)430 m \\ Scan Angle & \(\pm\)15\({}^{\circ}\) \\ Beam Divergence & Channel 1 and 2 = 0.35 mrad; Channel 3 = 0.7 mrad \\ Pulse Repetition Frequency & 200 kHz/channel; 600 kHz total \\ Scan Frequency & 40 Hz \\ Number of Returns/Pulse & Up to 4 returns \\ Number of Points & Channel 1 = 971,490; Channel 2 = 1,123,418; Channel 3 = 1,403,386 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data specifications.  
Figure 1: An image of the study area. The red rectangle indicates the LiDAR data coverage.  
Figure 2: _Cont._
## 3 Results and Discussion
### Synthesis of NODAGA-Asp-CFM Conjugate  
A total of 1 mg (1.58 \(\upmu\)mol) of Asp-CFM(\(\beta\)) was dissolved in 25 \(\upmu\)L of DMF. Then, the solution was transferred to an Eppendorf tube with 1.5 mg (2.04 \(\upmu\)mol) NODAGA-NHS before 0.86 \(\upmu\)L (6.21\(\upmu\)mol) of triethylamine (Et\({}_{3}\)N) was immediately added (Scheme 3). The tube was closed and left overnight on the magnetic stirrer at room temperature. The final conjugate was purified by HPLC (Figure 4) and lyophilized for later use: yield 90-95%, purity 95%. The presence of the conjugate was confirmed by MS analysis.
# Improving Serial Detection Using MAP Algorithm for Bit-Patterned Media Recording
###### Abstract  
Bit-patterned media recording (BPMR) is a promising technology for increasing the areal density (AD) of magnetic data storage systems. In BPMR, the non-magnetic zone is reduced to increase AD, which causes serious problems such as two-dimensional (2D) interference and track mis-registration (TMR). Therefore, in previous studies, a serial detection, which exploits the Viterbi algorithm (VA), was proposed to combat 2D interference. Because the VA lacks prior information from the inner detector to the outer detector, in this study, we use the maximum a posteriori (MAP) algorithm to supply prior information from the inner detector to the outer detector. Simulation results show that the proposed model can improve the bit error ratio (BER) performance of serial detection, especially in cases where the BPMR suffers from TMR effects.  
bit-patterned media recording; detection; interference; maximum a posteriori (MAP) +
Footnote †: journal: Applied  
by the 2D soft-output Viterbi algorithm (SOVA) for BPMR systems [12]. The 2D SOVA is composed of two parallel 1D SOVAs. One SOVA is used for horizontal detection, and the other is used for vertical detection. Because serial detection is implemented via the Viterbi algorithm (VA), the detection lacks prior information from the inner detector to the outer detector. In the previous studies, serial detection using VA keeps the information of a candidate symbol and ignores the other symbols. The information of these symbols is called the prior information. Therefore, in this study, to solve the above-mentioned problem, we use the maximum a posteriori probability (MAP) algorithm to preserve the information of the ignored symbols to the final decision part (Log Detection).  
In the proposed model, the output of the inner detection was split into two branches. One was supplied to the outer detection to achieve a soft output. The other was combined with the soft output of the outer detection to detect the original signal using log detection. The results of the simulation show that the proposed model can improve the performance of serial detection by exploiting the MAP.  
The remainder of this paper is organized as follows. In Section 2, we present the proposed model. In Section 3, we present the simulation results and discussion. In Section 4, the conclusions are presented.
## 3 Methodology and Data Collection
### Data Pre-Processing
#### 3.2.1 Hourly Aggregation  
The frequency of measurements in individual segments varies significantly. Information from a single FCD measurement about the achieved speed in a given segment will be strongly affected by several individual factors--type of vehicle, nature of load or style of distribution, individual skills of the driver, etc. Consequently, the authors proceeded to aggregate all of the measurements within \(1\) h of \(1\) segment into \(1\) record. This record stores information on the average value of achieved speeds per this specific hour, as well as the range of speeds per given hour (minimum and maximum measured speed). It also stores the number of measurement records acquired for this hour.  
Aggregation performed this way decreases significantly the amount of data and, from the perspective of the prediction model, it has a higher information value without any significant loss of detail. Figure 4 illustrates the course of the average hourly speed for the Nusle Bridge in Prague (_Tmcld_: TS04395T04396) in the week from Monday, 24 February, to Sunday, 1 March 2020. The dark blue highlight signifies the average speed per given hour, while the light blue highlight signifies the minimum and maximum speed per given hour. The graph shows the typical daily and weekly development. On weekdays, the measured speed dropped to less than 10 km/h (here, it was entirely due to the traffic density), but it went up at night. On the right, there are data for Saturday and Sunday where the traffic was lower, and the speed was not reduced as much during the day (with the exception of road traffic accident).  
The range between the lowest and highest measured speed per given hour (light blue lines) represents the variability which cannot be explained by the day of the week or by the hours of the day. Therefore, it makes sense to try to integrate other explanatory variables in the predictions (e.g., weather). Even after aggregation by hours, it must be taken into consideration that, sometimes, there were no measurements available at all per a given hour. This lack of measurements occurred mainly during the night. Sometimes, there were no measurements available per weekends, especially for lower types of roads.
# Fault Detection in Power Transformers Using Frequency Response Analysis and Machine Learning Models
## 5 Key Innovations and Contributions  
This study introduces a novel machine learning-driven diagnostic framework that significantly enhances power transformer fault detection using Frequency Response Analysis (FRA). Unlike previous studies that rely on limited fault types and manual interpretation of FRA signatures, this work leverages advanced machine learning models to achieve automated and highly accurate classification of transformer faults across multiple frequency bands. By systematically analyzing FRA data, the proposed approach overcomes traditional limitations, such as the dependency on expert knowledge and the challenges of noisy environments.  
A key contribution of this research is the development of a comprehensive, annotated FRA dataset, enabling supervised learning for diverse fault conditions. This dataset bridges the gap in transformer diagnostics by incorporating underexplored fault types, including axial displacement, core buckling, and contamination-related defects, which have received limited attention in prior studies. The integration of refined feature extraction techniques further enhances classification sensitivity, leading to an observed accuracy improvement of 80-100% across various fault classes.  
Performance evaluations demonstrate the superiority of the proposed methodology over conventional FRA interpretation techniques. The Decision Tree and Subspace KNN models outperform traditional methods by reducing false negatives and achieving frequency-independent fault detection, ensuring more reliable transformer condition monitoring. Additionally, the study identifies key frequency markers (7.6 MHz, 8.25 MHz, and 8.7 MHz) that serve as robust diagnostic indicators, reinforcing the practical applicability of the approach.  
By minimizing reliance on specific frequency bands and improving fault localization accuracy, this work presents a scalable and adaptable solution for predictive transformer maintenance. Future advancements will focus on deep learning integration and real-world deployment, further enhancing diagnostic precision and transformer health management.
## 3 Converting a Pyramidal Horn Antenna into a Circularly Polarized Antenna
### Radiating Systems with Circular Polarization Consisting of a Horn Antenna and FSS Layers
#### 3.2.1 FSS Structure with N \(\times\) N Cross-Shaped Elements  
The system operates on the principle of a Yagi-Uda antenna system consisting of \(\lambda\)/2 dipole antennas [29]. The horn antenna actually replaces the vibrators and the reflector and the FSS elements act as directors.  
An FSS structure with cross-type radiators (+), rotated at an angle of 45\({}^{\circ}\), was added in front of the pyramidal horn aperture. The simulations were carried out for several elements of the FSS structure, \(N\times N\) (Figure 10a-f): 2 \(\times\) 2, 3 \(\times\) 3, 4 \(\times\) 4, 5 \(\times\) 5, 6 \(\times\) 6 and 7 \(\times\) 7, respectively. The radiators have an electrical length of \(\lambda\)/2 at 12 GHz, that is, 12.5 mm, and a width of 1.5 mm. The FSS structures with 2 \(\times\) 2, 3 \(\times\) 3 and 4 \(\times\) 4 elements have the same size as the pyramidal horn aperture, i.e., 81 mm \(\times\) 61 mm; the distance between elements implicitly decreases with increasing \(N\). For the FSS structures with 5 \(\times\) 5, 6 \(\times\) 6 and 7 \(\times\) 7 elements, the gap between radiators is kept the same as for the structure with 4 \(\times\) 4 elements, and therefore, the FSS size for these structures changes as follows: 100 mm \(\times\) 76.25 mm for 5 \(\times\) 5 elements, 116 mm \(\times\) 91 mm for 6 \(\times\) 6 elements, and 140 mm \(\times\) 106.75 mm for 7 \(\times\) 7 elements. In this study, the distance between the pyramidal horn aperture and the FSS structure was set to zero. The resulting figures of merit for the six types of structures are given in Table 1.  
Figure 8: Gain of the pyramidal horn antenna in the main direction of radiation.  
Figure 9: Input reflection coefficient for the pyramidal horn antenna.  
Following the performance analysis of the radiating system consisting of the horn antenna and different types of FSS structures, described above, it turned out that the best option would be an FSS structure with 4 \(\times\) 4 radiators.