esa-sceva/satcom-chunk-collection huggingface dataset:
# Solid Phase-Based Microextraction Techniques in Therapeutic Drug Monitoring
## 3 Sample Pretreatment--Solid Phase-Based Microextraction Techniques
### Magnetic Solid-Phase Extraction and Solid-Phase Dynamic Extraction
[77; 79] carried out two studies for the simultaneous determination of a large number of anti-tumor drugs in 100 \(\upmu\)L plasma samples and analysis by UPLC-MS/MS. In both articles, the authors concluded that TDM is of great significance for the individualized treatment of cancer patients, and that the developed methodologies are suitable for TDM and pharmacokinetics studies. Also using only 100 \(\upmu\)L of plasma, [PERSON] et al. [78] developed a method for the determination of eight antimicrobials (linezolid, vancomycin, teicoplanin, tigecycline, impenem, meropenem, voriconazole and micafungin), which they believe to be suitable for application in routine TDM in critically ill ICU patients. With UPLC-MS/MS analysis, the authors obtained LOQ values between 0.1 and 0.2 \(\upmu\)g/mL and linearity intervals between 0.1 and 50 \(\upmu\)g/mL. [PERSON] et al. [81] developed a method for the determination of folic acid and riboflavin in urine samples. The methodology was applied to real samples, showing a promising application in the rapid analysis of free folic acid and riboflavin for clinical drug monitoring and treatment. [PERSON] et al. [82], on the other hand, established a methodology for the quantification of free testosterone and free androstenedione in serum samples, with the aim of improving the diagnosis accuracy of polycystic ovary syndrome in infertile women when combined with other clinical indicators. Concerning SPDE, this technique uses stainless steel needles covered with a PDMS film and 10% of activated carbon and can be applied for the preparation of liquid and vapor samples. The procedure is performed by passing the headspace using a syringe through the tube, so that a fixed volume is pulled and pushed an optimized number of times, allowing the process to take place under dynamic conditions, keeping the headspace volume constant. The analytes under study are retained in the stationary phase and are subsequently desorbed by injection into a GC instrument. The capillary used is more robust than the fibers used in other extraction techniques, and it is not mechanically damaged. However, the analytes tend to be retained on the inner wall of the needle during the thermal desorption process, and the length of this coating can also be a problem for this same injection process [32; 83]. For the SPDE procedure, between the years 2010 and 2022 and for the same research method in the databases described above, it was possible to find only one work, carried out by [PERSON] et al. [84], for the biomonitoring of n-heptane and metabolites in blood samples. Using the headspace extraction technique and analysis by gas chromatography and mass spectrometry, they obtained LODs between 0.006 and 0.021 mg/L and concluded that these data could be helpful for the evaluation of a biological exposure limit of n-heptane in blood. Table 8 summarizes some of the advantages and disadvantages of these two approaches [32; 85; 86].
## 4 Discussion  
This study employed an airborne laser scanning (ALS)-derived digital terrain model (DTM) and Hmax as reference data to evaluate the accuracy of the Carbon Sinks and Aerosol LiDAR (CASAL) system in two key areas. First, CASAL's performance in DTM estimation was assessed across varying slope, vegetation cover, and tree height conditions. Second, its capability in canopy height estimation was examined under different slope, vegetation cover, and forest type scenarios. These analyses provide a comprehensive understanding of CASAL's accuracy in diverse environmental settings.
# CPF-UNet: A Dual-Path U-Net Structure for Semantic Segmentation of Panoramic Surround-View Images
## 1 Introduction
### Contribution  
This paper's important contributions are summarized below:  
* We propose the CPF-UNet network, which integrates the dual-path structure into the UNet network, achieving a better balance between accuracy and the number of parameters.
* To overcome the problem of blurring and jagged edges in BEV images, we propose a cross-layer connection-based feature-fusion method that connects the third and last layers of the decoder in the UNet network, thereby enhancing the fusion of deep and shallow features.
* We have explored methods to replace dense skip connections, aiming to achieve a better balance between network accuracy and real-time performance.
# EEG Signal Processing and Supervised Machine Learning to Early Diagnose Alzheimer's Disease
## 2 Materials and Methods
### Data Acquisition and Preprocessing  
Multi-channel EEG signals were recorded by using 19 electrodes placed according to the 10-20 system [8] in monopolar connection with the earlobe electrode as a reference.  
Raw electrical brain activity (\(\mu\)V) recordings last about 300 s. For more details on data collection, the reader can refer to the previous study [24].  
In the preprocessing step, the sampling rate is normalized to 256 Hz, and EEG are filtered at a 1-Hz low cutoff (high-pass) and at a 30-Hz high cutoff (low-pass). After filtering, artifacts are detected by visual inspection and rejected. One hundred and fifty seconds of cleaned EEG are considered for each subject, extracted from the central part of the EEG signal in order to maintain the maximum signal information to train classifiers on the same length signals but without losing too many instances. Thus, four subjects were excluded (i.e., 1 AD and 3 HC) due to an excessive number of artifacts, and the dataset dropped to 105 EEG recordings.
## 4 Results and Discussion
### Causes of Waterlogging  
Several factors contribute to waterlogging in the study area:  
1. **External factors:** Urban heat island and rain island effects play a significant external role in waterlogging events. The urban landscape is extensively paved, with limited green space. The heat island effect increases airborne dust particles, which act as condensation nuclei, affecting atmospheric conditions and altering the spatial distribution of urban rainfall, leading to more frequent heavy rain. As a result, university campuses located in city centers are particularly impacted.
2. **Land use and building layout:** The high-density campus population, combined with more than 75% of hardened surfaces with low rainwater absorption capacity, exacerbates waterlogging. Additionally, the campus sloped terrain causes prolonged water accumulation in low-lying areas.
3. **Aging infrastructure:** Constructed in the 1970s, the campus drainage network was designed to low standards, using a combined rainwater and sewage system. Over time, pollutant buildup, localized pipeline damage, and a reduced effective water passage area have significantly diminished the system's drainage capacity.
## 3 Experiment Results and Discussion
### Results and Discussion  
In this section, we introduce the indicators used for evaluating the performance of training. Among the performance indicators shown in Table 3, the segmentation performance was evaluated by pixelwise accuracy and average precision (_AP_). Moreover, to evaluate the detection performance, the critical value of mean intersection over union (_mIoU_) was set to 0.5 or greater (_[EMAIL_ADDRESS]) and 0.7 or greater (_[EMAIL_ADDRESS]). Then, the average of when the _IoU_ of detected objects was equal to or greater than the critical value was calculated. The inference speed of each network was assessed in terms of frames per second (_FPS_). To assess the performance of the proposed network, we conducted a quantitative evaluation using performance indicators and a qualitative evaluation using visual images of segmentation and detection results.  
Figure 8: An example of ground truth for segmentation.  
Tables 4 and 5 show the performance of the SSADNet. The segmentation performance had a pixelwise classification accuracy of 96.9%. The _mAP_ was 96.9%, while the detection performance had a _[EMAIL_ADDRESS] of 70.5% and a _mloU_@0.7 of 79.0%. The inference speed was 33 _FPS_. Figure 9 shows the visual results of SSADNet. From the left, each column in the figure represents an input image, segmentation result, detection result, and the results when segmentation and detection were performed simultaneously.  
Tables 6 and 7 show performance comparisons with other algorithms. Since our method was developed by converting 3D into 2D, we compared algorithms using frame image data which is same as LiDAR data.  
Figure 9: Qualitative results of SSANet: (**a**,**e**,**f**) input image, (**b**,**f**,**j**) segmentation result, (**c**,**g**,**k**) detection result, (**d**,**h**,**l**) results that confirm segmentation and detection at the same time.
## 2 Broken Rotor Bar in IMs  
The squirrel-cage rotor is the inner part of the motors, and it is rotated by electromagnetic field, which is induced in its coils by stator field. The rotor then applies the rotational force to the external equipment. Squirrel-cage rotor, depending on the construction of the cage, is divided into cast and fabricated rotor. The material used for cast cage is aluminum and cast cage rotors are generally used in small-sized motors, whereas the material used for fabricated cage is copper and fabricated cage rotors are used for high-power motors. Although the squirrel-cage rotor is rugged, rotor defects, such as broken rotor bar, cracked end-ring, and bent shaft, do occur. The percentage of motor failures attributed to rotor problems is not too large, but they can cause extensive damage to the motor if left undetected [24]. Broken bar faults may happen due to a variety of reasons, such as mechanical, thermal, or magnetic stresses; environmental stresses during motor operation; and defects in design of motor structure and its manufacturing [1]. Among different types of rotor fault, broken bar and end-ring are mainly caused by manufacturing defects and excessive start-stop cycles or frequent speed changes. Motors of low and medium power generally involve casting rotor bars. Small defects that may occur during the casting process cause important failures in the bar and the other reasons mentioned in [25]. In the rotor of a high-power motor, copper bars are generally connected to the end-rings through welding, and if this procedure are not performed carefully, some defects are generated [26]. Rotor design plays a key role in the severity of rotor irregularity. If the rotor has a closed bar design, the fault severity is expected to be low because of the iron acting of the rotor that holds the asymmetrical bar in place. Nevertheless, if the rotor has an open bar design, the asymmetrical severity enhances significantly [24]. Rotor bar failures bring about secondary failures in other parts of electrical machines. These secondary failures cause severe malfunctions of the motor and reduce the motor efficiency that increases the operational costs. For example, current in bars adjacent to the broken one increases up to 50% of rated current [27] and thus causes unbalanced currents and torque pulsation, which decrease the average torque [28]. When distribution of rotor current is changed, adjacent bars to the broken one are overheated, which causes other irregularities [29] and breakage of several other bars [30]. Variation of heating around the bars can also make a bow in the rotor and then generate eccentricity. Rotor eccentricity causes basic rotor unbalance and a greater unbalanced magnetic pull [31]. Moreover, if a broken rotor bar rises out of the slot due to the centrifugal force, the bar will contact the
# Optimization of the Multi-Facility Location Problem Using Widely Available Office Software
###### Abstract  
Multi-facility location problem is a type of task often solved (not only) in logistics. It consists in finding the optimal location of the required number of centers for a given number of points. One of the possible solutions is to use the principle of the genetic algorithm. The Solver add-in, which uses the evolutionary method, is available in the Excel office software. It was used to solve the benchmark in 4 levels of difficulty (from 5 centers for 25 points to 20 centers for 100 points), and one task from practice. The obtained results were compared with the results obtained by the metaheuristic simulated annealing method. It was found that the results obtained by the evolutionary method are sufficiently accurate. Their accuracy depends on the complexity of the task and the performance of the HW used. The advantage of the proposed solution is easy availability and minimal requirements for user knowledge.  
multi-facility location problem (MFLP); Excel; Solver +
Footnote †: journal: Applied Softwares  
18 February 2021  
24 March 2021  
**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.  
the classic location theory, which addresses the need to minimize the cost of transport. The mathematical model of the exercise is based on the Euclidean coordinates metrics.  
Article [3] concentrates on facility location problem, focusing on the linear programming model and a genetic algorithm in the location problem analysis and analytical method. In the linear programming model, because the given complex table calculation method is too complicated and the workload is very large, the Excel software is proposed to solve the location problem, which can greatly improve the efficiency of enterprise facility location problem. In addition, a genetic algorithm based on MATLAB toolbox is applied to another type of facility location problem, which provides a referential method for location decision under different conditions and different facilities.
# SR-GNN: A Signed Network Embedding Method Guided by Status Theory and a Reciprocity Relationship
## 3 A Directed Signed Network GNN Model
### Status Theory and Node Status Score  
Status theory draws from sociological concepts; it conceptualizes an individual's \"place\" within the society as characterized by edge-creation intentions and triangle patterns of individuals in a directed signed network. In this theory, the sign of an edge depends on the difference in status between nodes. If User A perceives User B as having a higher status, [PERSON] is likely to establish a positive edge to [PERSON], which can raise [PERSON]'s status score in a network; in other words, [PERSON] has made a positive evaluation of [PERSON]. Conversely, when User C gives a low evaluation of User D, a negative edge is created from C to D, reducing [PERSON]'s status score, and so [PERSON] has created a negative impact on [PERSON]'s status. The transfer of status scores leads to the formation of the triadic closure patterns in a directed signed network.  
Figure 1 illustrates a simple example where initially each node's status score is set to 0. When a node receives a positive edge, its status score increases by 1, and vice versa. Given these status scores, we may anticipate the sign of the edge between two nodes based on their status scores. For instance, if Node E has a lower status score than Node C, the edge from E to C is expected to be positive.  
Beyond sign prediction, status scores also play a crucial role in node ranking. A node with a higher status score has received more positive edges from its neighbor nodes, indicating that it is regarded favorably in the network and thus ranks higher. From the perspective of the sender, although transmitting negative edges can enhance their status rating, such nodes also tend to receive negative edges in return. From this standpoint, the evaluation of a node should be evaluated from the two aspects of being a receiver and a sender. Status theory provides the best foundation for designing GNN models: based on the direction of edges, it effectively captures the distinct behavioral features of nodes at both ends of an edge.  
Compared to (weak) structural balance theory, status theory has an additional advantage: it can describe both pairwise relationships in a signed network and higher-order triangle structures through transitivity. Notably, in sparse networks where triangles are scarce, (weak) structural balance theory fails, but status theory remains effective.
## 5 Experiments  
In this section, we evaluate the effectiveness and positioning accuracy of the proposed rescuer localization system through experimental validation. The reliability and accuracy of the proposed algorithm are assessed by comparing its trajectory with those generated by the independent APDR and VIO algorithms. Experiments were conducted in a variety of environments, including indoor corridors, underground parking lots, and tunnels. This section also provides a detailed description of the experimental setup, procedures, results analysis, and discussion.
## 5 Feasibility Study
### Performance Evaluation of Raspberry Pi Boards as Asterisk Server for LTC Applications
#### 5.1.2 Methodology  
Figure 6 depicts the flowchart of the proposed performance testing algorithm:  
1. A music-on-hold (MOH) extension was configured on the embedded Asterisk server of the PoT gateway. MOH maintained each originated call to that extension live until the calling party hung up the call. MOH allows one to programmatically originate an increasing number of simultaneous calls at the PoT gateway.
2. After each call origination, the operating system metrics were captured by the testing script and compared against predefined threshold values defined in the script. Thresholds were set such that %CPU utilization <75% for single-core ARM boards and <300% for quad-core ARM boards (equivalent to 75% per core) and the load average (5 min interval) < 4. These thresholds aimed at: (1) protecting the embedded platform from potential hardware damage due to the excessive CPU utilization by the Asterisk process, (2) maintaining the responsiveness of the operating system in order to be able to carry out the evaluation measurements, (3) maintaining the VoIP call quality metrics at acceptable values, and (4) determining the maximum number of simultaneous VoIP calls that the embedded platform can gracefully serve.
3. Another VoIP call was originated at the beginning of the test using an IP phone set. It called an MOH extension, and it was kept alive throughout the testing procedure. The traffic of this undergoing VoIP call was captured using Wireshark. The traffic capture was used to monitor the VoIP call quality metrics while originating an increasing number of VoIP calls during the test procedure to assure that both jitter and packet loss of the established VoIP calls still fall within the standard acceptable limits.
## 3 Experimental Testing Results
### Crack Formation of Specimens  
The crack pattern and width of the specimens were monitored during the lateral cyclic loading test to assess the effect of the increased number of shear links and anchorage length. Figure 5 illustrates the crack formation on the selected joint area for BCJ-1 (control), BCJ-2 (strengthen by Eurocode 8), BCJ-4 (representing additional shear links), and BCJ-5  
Figure 4: Load-drift hysteretic relationships of test specimens.  
(representing anchorage length) at the drift ratio level of 0.25%, 1.25%, and 2.25%, including at the failure. In the case of control specimen BCJ-1, the primary flexural crack initially formed at a drift ratio level of 0.25% from the top corner of the joint area and then propagated downwards to create two new secondary cracks at the intersection area between the beam and the joint panel. Moreover, numerous hairline cracks at 45\({}^{\circ}\) appeared at the joint interface, which extended from the primary and secondary cracks in specimen BCJ-1. For specimens BCJ-2 and BCJ-4, the primary/flexural crack was initiated at a drift ratio level of 0.25%, similar to specimen BCJ-1. However, the width and length of the crack were smaller than those of the control specimen. For BCJ-5, two primary cracks were formed at the early stage of the loading protocol. At the drift ratio level of 1.25%, a shear crack formed at 60\({}^{\circ}\), and connected to the primary crack.  
Figure 5: Crack patterns and damage of respective specimens.  
With repeated loading cycles, new cracks and extension of tertiary cracks increased at the joint panel zone for all specimens. The test was terminated after a drift ratio level of 3.00% for BCJ-1, due to severe crushing of concrete at the beam-column joint area. For BCJ-2 and BCJ-5, concrete cover spalling was observed at the area of primary crack beyond the drift ratio level of 3.00%. However, in BCJ-4, small concrete surface spalling was seen near the joint interfaces before failure, due to shear action beyond the drift ratio level of 3.25%. The maximum crack widths were recorded at the primary/main cracks residing at the critical zone (joint region). In general, the results indicated that the additional shear links and increased anchorage length had a significant effect on strength and on reducing the crack width.
## 3 Underwater Channel Characterization
### EM Propagation in Freshwater
#### 3.3.1 Propagation Velocity of EM in Freshwater  
The propagation speed of EM waves [11] can be expressed in Equation (5):  
\[c\approx\frac{1}{\sqrt{\mu\times\epsilon}} \tag{5}\]  
where \(\epsilon\) is the dielectric permittivity and \(\mu\) is the magnetic permeability. The dielectric permittivity \(\epsilon\) is further given as the product between permittivity in air (\(\epsilon_{o}\)) and relative permittivity (\(\epsilon_{r}\)) of a medium. The relative permittivity (\(\epsilon_{r}\)) is dimensionless quantity, which, in the case of water (fresh or saline) is about 81 [4].
# Crop Land Change Detection with MC&N-PSPNet
## 4 Discussion
### Application Development, Directions, and Limitations
Agricultural regions exhibit significant variability in field sizes, shapes, and crop arrangements, as influenced by regional agricultural practices and local policies. Current models may face challenges when applied to regions with different agricultural systems than those represented in the training data. Non-photorealistic rendering of remote sensing images poses additional challenges. Non-photorealistic images, often resulting from preprocessing techniques aimed at enhancing specific features, can introduce artifacts and distortions that affect the model's performance. These distortions might lead to misclassification and reduce the model'saccuracy. To mitigate this, the model should be trained and tested on both photorealistic and non-photorealistic datasets, ensuring it can handle various image processing artifacts. Additionally, developing advanced preprocessing methods that preserve essential image features while minimizing distortions can enhance the model's performance in real-world applications. In summary, while the MC&N-PSPNet model demonstrates significant potential in detecting changes between agricultural and non-agricultural areas, addressing these limitations through the integration of diverse data sources, expansion of training datasets, consideration of non-photorealistic rendering effects, and optimization of computational requirements will be crucial for enhancing its accuracy, reliability, and generalizability. These improvements will provide a more comprehensive and scientifically robust foundation for agricultural resource management and policy formulation.