esa-sceva/satcom-chunk-collection huggingface dataset:
# Parts Semantic Segmentation Aware Representation Learning for Person Re-Identification
## 3 The Proposed Method
### Person Parts Localization and Parts Alignment  
Semantic segmentation associates each pixel of an image with a class label. Due to the ensemble effects of label of each pixel, bounding boxes inferred from semantic segmentation map are more stable and accurate than detection methods. This paper uses semantic segmentation map to find the bounding boxes of human body parts.  
U_Net [12] is a popular semantic segmentation method which is good at biomedical image segmentation. U_Net architecture consists of a contracting path to capture the context and a symmetric expanding path that enables precise localization. We make three modifications to adopt it for the person parts segmentation. At first, we reduce the number of pooling operators due to the small size of person image. Next, we add two residual structures to compensate for the depth reduction. Third, we do not reduce the size of feature maps by 2 when passing through convolutional layers; as a result, the output segmentation maps have the same size as input images. Figure 3 illustrates the U_Net structure we used. We use its segmentation maps to find the bounding boxes of human body parts. Person images are resized to 192 \(\times\) 88 and pass through the U_Net network. The size of the output semantic segmentation maps is also 192 \(\times\) 88, and then the segmentation maps are resized to the same size as the original person images. Figure 4 illustrates some examples of part segmentation by super-pixels.  
Bounding boxes of person parts are fixed by the parts semantic segmentation map. For the stable feature extractor, there are two points need to be considered: (1) Large scale differences make extracted feature instable; (2) Large aspect ratio changes lead to part misalignment. This paper gives up two kinds of part regions: (1) the part region whose area bellows \(5\%\) of its corresponding person image; (2) the part region whose aspect ratio beyond reasonable scope. We set reasonable scopes [0.75, 1.33] for head region, [1; 3] for torso region and [1; 3] for lower-body region. We crop the person images with minimum circumscribed rectangle of its corresponding parts if they are complete.  
After parts localization, the person image and three local patches are propagated forward through the proposed four-branch network, which completes parts alignment. An example illustrated in Figure 4. Figure 4a is an example of parts misalignment. It is the head region in red rectangle region of left image while it is background in the same location in right image. We locate three body parts and combine them with the whole image as the input of proposed four-branch CNN network. Figure 4b,c illustrate two input of the proposed network, which corresponds to two images of Figure 4a. As seen from Figure 4b,c, the input patches are well aligned.
## 2 Materials and Methods  
**Chemicals:** Zinc phthalocyanine (ZnPc), 4-hydroxybenzoic acid. poly (ethylene glycol) methyl ether-block-poly (lactide-co-glycolide), known as PEG-b-PLGA, with PEG average M\({}_{\text{m}}\) 2000 and PLGA average M\({}_{\text{n}}\) 11,500 and 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT), and 1,8-diazabicycloandec-7-ene (DBU) were purchased from Sigma Aldrich (St. Louis, MO, USA). Dimethyl sulfoxide (DMSO) was purchased from Alfa Aesar (Haverhill, MA, USA), while dimethylformamide (DMF) and acetonitrile were purchased from Thermo Fisher Scientific (Raleigh, NC, USA).  
**Biological Supplies:** Delbecco's Minimum Essential Media (DMEM)/High Glucose (SH30243.02) was purchased from Fisher Scientific. CellTiter-Blue(r) (CTB) cell viability assay reagent was purchased from Promega (Madison, WI, USA). NucBlue solution for staining nuclei in live cells was purchased from Thermo Fisher Scientific (Raleigh, NC, USA). Human A549 lung carcinoma cells were cultured in DMEM containing 10% v/v Fetal Bovine Serum (FBS). DMEM and Hyclone FBS were obtained from Thermo Fisher Scientific (Raleigh, NC, USA). Cells were incubated at 5% CO\({}_{2}\) at 37 \({}^{\circ}\)C. The phototoxicity of the encapsulated dye was evaluated using a Scope Light 2000-HORIBA and a 599 \(\pm\) 50 nm long bandpass filter (Edmond Optics, Barrington, NJ, USA)was used.  
Figure 2: Structure of PLGA-b-PEG used for encapsulation of ZnPcBCH\({}_{3}\), where n = 11,500, m = 2000, x = y.  
**Instrumentation**: \({}^{1}\)H-NMR spectra were recorded on a Varian 400 MHz spectrometer (Palo Alto, CA, USA), Fluorescence spectra were recorded on a Fluorolog-3 spectrofluorometer (HORIBA Jobin Yvon Inc., Clifton Park, NY, USA) and NanoLED-625 was used. FTIR spectra were recorded on a Nicolet Nexus 470 FTIR spectrophotometer (Thermo Fisher Scientific, Raleigh, NC, USA). UV-VIS measurements were recorded on a Cary 300 spectrophotometer (Agilent, Santa Clara, CA, USA). Plate readers were a Molecular Devices Spectramax Gemini XS fluorescence plate reader (Molecular Devices, San Jose, CA, USA) and a Molecular Devices SpectraMax UV/Visible spectrophotometric plate reader (San Jose, CA, USA). Singlet oxygen quantum yields were measured using an Edinburgh Analytical Instrument (FS920) and NIR PMT from HAMAMATSU (H10330A series): West Lothian, UK.
# Improving IMES Localization Accuracy by Integrating Dead Reckoning Information
###### Abstract  
Indoor positioning remains an open problem, because it is difficult to achieve satisfactory accuracy within an indoor environment using current radio-based localization technology. In this study, we investigate the use of Indoor Messaging System (IMES) radio for high-accuracy indoor positioning. A hybrid positioning method combining IMES radio strength information and pedestrian dead reckoning information is proposed in order to improve IMES localization accuracy. For understanding the carrier noise ratio _versus_ distance relation for IMES radio, the signal propagation of IMES radio is modeled and identified. Then, trilateration and extended Kalman filtering methods using the radio propagation model are developed for position estimation. These methods are evaluated through robot localization and pedestrian localization experiments. The experimental results show that the proposed hybrid positioning method achieved average estimation errors of 217 and 1846 mm in robot localization and pedestrian localization, respectively. In addition, in order to examine the reason for the positioning accuracy of pedestrian localization being much lower than that of robot localization, the influence of the human body on the radio propagation is experimentally evaluated. The result suggests that the influence of the human body can be modeled.  
a 2016163390/s16020163 1  
Hitachi Industrial Equipment Systems Co., Ltd., 3 Kanda-neribei-cho, Chiyoda-ku, Tokyo 101-0022, Japan; [EMAIL_ADDRESS] Department of Modern Mechanical Engineering, School of Creative Science and Engineering,  
Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan; [EMAIL_ADDRESS] (H.A.); [EMAIL_ADDRESS] (W.W.); [PERSON]DDRESS] (Y.K.); [EMAIL_ADDRESS] (A.S.); [EMAIL_ADDRESS] (S.S.) Correspondence: [EMAIL_ADDRESS], Tel./Fax: +81-3-5285-0996  
Accademic Editor: [PERSON]  
Received: 16 November 2015; Accepted: 22 January 2016; Published: 27 January 2016
## 3 Device Measurement Results
The measurement frequency was selected to be around the 3-dB O-E bandwidth for both Devices A\({}^{\prime}\) and B at 25 GHz and 1 GHz, respectively. The ideal relation between the microwave power and averaged photocurrent (open symbol), with a 100% optical modulation depth under a 50 \(\Omega\) load, is also plotted for reference. Figure 11: (a,b) represent the measured bias dependent O-E frequency response of Device A\({}^{\prime}\) under an optical pumping power of 20 uW at the 1.31 μm wavelength. Figure 10: (a–c) top-views of the fabricated device before and after flip-chip bonding, and (d) dark current, photocurrent, and operation gain versus bias voltages measured under different optical pumping powers for Device A\({}^{\prime}\) at the 1.31 μm wavelength. We can clearly see that the photo-generated RF power of both Devices A\({}^{\prime}\) and B shows no significant saturation even when the output photocurrent is above their dc saturation currents of 5 and 3 mA, respectively. Their maximum output powers are limited by thermal failure due to the high bias voltages required. Contrast this behavior with that of the typical high-speed p-i-n PDs, which always show saturation in the photo-generated RF power when their average output dc current is near saturation. In addition, under a small output photocurrent, the near V\({}_{\rm br}\) bias of both devices would lead to a photo-generated RF power far below the ideal power values on a 50 \(\Omega\) load. As can be seen, the highest  
Figure 14: The measured photo-generated microwave power versus photocurrent of **(a)** Device A\({}^{\prime}\) and **(b)** Device B under different reverse biases. The open symbol line shows the ideal trace for a 100% optical modulation depth and 50 \(\Omega\) load. Figure 12: (**a**,**b**) represents the measured bias dependent O-E frequency response of Device A\({}^{\prime}\) under optical pumping power (1 mW) at 1.31 \(\upmu\)m wavelength. Figure 13: The measured DC output photocurrent versus input optical power of **(a)** Device A\({}^{\prime}\) and **(b)** Device B at the 1.31 \(\upmu\)m wavelength. output photocurrents for maximum output RF power for Devices A\({}^{\prime}\) and B occur at 8 and 12 mA, respectively. When the output photocurrent increases, all traces measured under different reverse bias voltages merge together, which can be attributed to the reduction in the avalanche delay time and enhancement of the O-E bandwidth under high-power operation, as discussed in Figure 7a,b.
## 2 Related Works and Contribution
### Energy-Driven Cluster-Head Rotation Methods  
Energy-distance aware clustering method (EDAC) [25] elects CHs based on two parameters: the residual energy of sensor nodes and the energy expended for transmitting data from sensor nodes to potential CH. These two parameters are combined in a metric, which quantifies how good a sensor node would be as a CH. Using the metric evaluated for all sensor nodes in a cluster, each node decides whether it will be the new CH. Selection of new CH is made when the residual energy of current CH drops below a predetermined threshold.  
In case of energy-distance aware clustering scheme for wireless sensor networks (E-DACS) [26] the CH is selected for each cluster depending on its residual energy, distance from other sensor nodes in cluster, and distance to sink. New CH selection is executed periodically, once energy level of current CH becomes less than the energy of any other sensor node in its cluster.  
Energy-driven cluster-head rotation (EDCR) is an algorithm where the selection of CHs is based on relative residual energy level of sensor nodes in a given cluster. The sensor node with the highest energy level becomes the CH. A dynamic threshold is used to trigger the CH rotation process. The threshold is calculated as \(P\cdot EC\), where \(0<P<1\) is a constant parameter, and \(EC\) is the residual node energy, measured when the node is selected to be CH. In this method, the selection of new CH takes place when the residual energy of CH drops below the threshold value. The lower is the residual energy of sensor nodes, the more often the change of CH node takes place. This rotation strategy was intended to balance the energy consumption in sensor network. An extension of the EDCR algorithm is the EDCR-MH [27], which has been adapted to the requirements of multi-hop networks.  
For sensor networks with energy harvesting capabilities, a method was proposed in [28] to select CH nodes by taking into account predicted rates of energy harvesting. That method uses back propagating neural network for the prediction purposes. The sensor nodes with more available energy and closer to the center of a cluster are more likely selected to become CHs. As a result, the node energy consumption is balanced among the cluster.
# Cognitive State Classification Using Convolutional Neural Networks on Gamma-Band EEG Signals
## 2 Methodology
### Evaluation  
To assess the model's proficiency in capturing these subtle cognitive variations and evaluate its overall performance, we employed a comprehensive set of metrics. The model's training and subsequent evaluation were assessed using three key metrics, each providing unique insights into different aspects of its classification performance, with accuracy serving as a fundamental measure of overall classification effectiveness. It measures the overall correct classification rate, providing an assessment of how often the model's predictions align with the actual outcomes. Accuracy is calculated as the ratio of correct predictions (both true positives and true negatives) to the total number of cases evaluated. The formula for calculating accuracy is [32]:  
\[\mathrm{Accuracy}=\frac{\mathrm{TP}+\mathrm{TN}}{\mathrm{TP}+\mathrm{TN}+ \mathrm{FP}+\mathrm{FN}} \tag{4}\]  
where TP = true positives, TN = true negatives, FP = false positives, and FN = false negatives. While accuracy provides an overall measure of the model's performance, it is important to consider more specific metrics that focus on the model's ability to correctly identify positive instances. This leads us to consider precision and recall. Precision is a metric for assessing the reliability of positive predictions in classification models. It quantifies the proportion of true positive predictions among all instances predicted as positive by the model. Precision provides insight into the model's ability to avoid falsely labeling negative instances as positive, which is particularly important in applications where false positives could have significant consequences. The formula for calculating precision is [32]:  
\[\mathrm{Precision}=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}} \tag{5}\]  
Complementing precision, recall offers another crucial perspective on the model's performance, particularly in situations where identifying all positive instances is critical. Recall is a metric for evaluating a classification model's sensitivity to positive instances. It represents the proportion of actual positive cases correctly identified by the model, providing insight into the model's effectiveness in detecting all relevant instances. In our study, recall becomes especially important for assessing the model's performance in identifying the less frequent, but potentially more significant, positive instances. The formula for calculating recall is [32]:  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{6}\]  
Following the individual metric analyses, a comprehensive evaluation of the CNN model's performance across different network architectures was conducted using the test set.
## 4 Discussion
Thus, the degassed fluid allows the energy generated by multiple wavelengths of sound in a broad range of frequencies to be more efficiently delivered throughout the root canal system (main canal, fins, isthmuses, lateral canal, etc.). The constant refreshing of NaOCl ensures a more effective breakdown and removal of tissue, bacteria, and biofilm [35]. The console pressurizes and delivers the fluids into the procedure instrument (PI). When the PI is placed in position, 1 mm above the pulp chamber floor, the irrigation solution flows at a high speed (9000 PSI). Inside the procedure instrument there is an orifice that turns the pressurized fluid into a high-speed stream of fluid. The stream of fluid interacts with the accumulated fluid inside the chamber, causing continuous hydrodynamic cavitation. The stream of fluid is deflected to generate vortical fluid dynamics and negative pressure. The implosion of bubbles inside the cavitation cloud generates shock waves and acoustic energy that propagate throughout the root canal system [64]. [PERSON] et al. [34] demonstrated that the technology minimizes the possibility of extrusion of the treatment fluids. Our findings showed the minimal presence of remaining bacteria within dentinal tubules. The importance of the chemical disinfection of the root canal system in in vitro models has been previously described [4; 65]. It is recognized that bacteria inside dentinal tubules can be associated with persistent periapical disease [66; 67]. Even though histological sections demonstrated cleaning of the root canal system, bacteria remained within the dentinal tubules. [PERSON] et al. [68] did not find strong evidence to formulate a clinical recommendation on the disinfection and healing effect of apical periodontitis in teeth with multiple root canals when ultrasonic irrigation was applied during primary root canal treatment. Future studies need to evaluate the ability of the multisonic technology to decontaminate the root canal space using microbiological methods. One of the limitations of the present study is the lack of control groups with conventional irrigation technique. This study was designed as an observational study to understand the compatibilities of the multisonic technology and to verify that the correct protocols and procedures were used. Based on this study, an in vivo study with extraction was designed to compare the GentleWave procedure to the conventional irrigation and instrumentation technique.
# Optimizing Agricultural Data Analysis Techniques through AI-Powered Decision-Making Processes
## 1 Introduction
### Technologies Used in Smart Farming  
Smart farming covers a variety of technologies [4], including (i) AI algorithms that perform data analysis to recognize patterns, aiding the farmers in making informed decisions about fertilization, irrigation, and pest control; (ii) Internet of Things (IoT) sensors that are used to gather data (temperature, soil moisture, humidity, and crop health) from the fields; (iii) Geographic Information System (GIS) software that is used to map and analyze spatial data, allowing farmers to optimize resource usage, field layout, and crop selection, which refers to the process of choosing the most suitable crops to cultivate, based on various data-driven insights and advanced technologies; in smart farming, this selection process involves the use of sensors, IoT devices, data analytics, and sometimes AI algorithms; and (iv) robotics and automation, which are used to perform tasks such as weeding, harvesting, and milking. Figure 1 above shows the different technologies that can be used in smart farming such as autonomous tractors, farming drones, field sensors, etc.
###### Abstract  
Background: Neck pain (NP) is a frequent condition in women, characterized by exhibiting distinct clinical manifestations such as the presence of deep neck (DN) muscle weakness. Endurance and ultrasonography of the DN muscles, and patient-reported outcome measures, are commonly used outcomes in clinical practice. The aim of this study is to assess and correlate the endurance of the DN muscles and their morphological characteristics with pain intensity, neck disability and headache impact. Methods: An observational and correlational study was carried out. Eightty-two women were recruited, and endurance tests of neck flexor and extensor (chin tuck flexion test and neck extensor muscles endurance test), ultrasonography of the DN muscles, pain intensity, disability (neck disability index) and headache impact (HIT-6) were measured. [PERSON]'s rho was used to evaluate the correlation between the outcome variables, and a simple linear regression analysis was carried out to explain the model in detail. Results: Statistically significant negative correlations between the chin tuck neck flexion test and neck disability index (NDI) (\(r=-0.38\); \(p<0.001\)) and HIT-6 (\(r=-0.26\); \(p=0.02\)) were found. The neck extensor muscles endurance test showed a negative correlation with NDI (\(r=-0.27\); \(p=0.01\)) and HIT-6 (\(r=-0.26\); \(p=0.02\)). The simple linear regression analysis showed an R squared of 26.7% and was statistically significant (NDI: R squared = 0.267; F = 3.13; \(p=0.004\)) for NDI. Conclusion: A negative correlation between deep neck muscle endurance test results and self-reported outcome measures in women with low cervical disability and neck pain were observed. This suggests that lower endurance in the deep neck muscles may be associated with poorer self-reported symptoms and functionality in these patients. The chin tuck neck flexion test and deep extensor muscles endurance test could predict self-perceived neck disability in women with low cervical disability and NP.  
neck pain; deep neck muscles; muscle endurance; ultrasonography 2023
## 3 Results and Discussion
### CFTX Electrochemical Degradation Mechanism  
A mechanism of CFTX pure degradation is proposed, without speculating on the reaction products that are formed due to the interactions between CFTX and chlorine, bromine and iodine species.  
In the ground state there are no n-\(\pi\) or \(\pi\)-\(\pi\) electronic conjugations between the thiazole side chain (R\({}_{1}\)-CONH-) or triazine side chain (R\({}_{2}\)) and the cephalospororin core  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**CFTX/Supporting Electrolyte** & **Applied Kinetic Model** & **k/min\({}^{-1}\)** & **Final Degradation Time/min** \\ \hline CFTX/SO\({}_{4}\)\({}^{2-}\) & Zero-order reaction kinetics & 0.0044 & 204 \\ CFTX/F\({}^{-}\) & Zero-order reaction kinetics & 0.0096 & 84 \\ CFTX/Cl\({}^{-}\) & Zero-order reaction kinetics & 0.1677 & 5.5 \\ CFTX/Br\({}^{-}\) & First-order reaction kinetics & 1.361 & 3 min (degradation percentage close to 100\%) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Kinetic data for CFTX degradation reaction in different supporting electrolytes.  
Figure 6: The kinetic models applied for CFTX degradation reaction: (**a**) in presence of F\({}^{-}\); (**b**) in presence of Cl\({}^{-}\); (**c,d**) in presence of Br\({}^{-}\).  
structure (R) (Figure 7). During the processes of electrochemical transformation of CFTX, intermediate structures with n and/or \(\pi\) electrons delocalized over the R\({}_{1}\), R\({}_{2}\) and the cephalospororin core structure can be formed. In the thiazole side chain (R1-CONH-), the thiazole ring has a high stability, so that the process of electrochemical degradation can be initiated in the acetylamino group (amide) and/or in the methoxyimino group.  
Scheme 1 shows a possible mechanism of electrochemical transformation of the substituent R\({}_{1}\)-CONH-. The sulfur atom of sulfide groups (thioether) is electrochemically oxidized to the corresponding sulfoxide [7,19].  
The lateral chain (R\({}_{1}\) and R\({}_{2}\)) of cephalospororin core structure may influence both the potential and the current density value of electrochemical degradation [43,44]. Nitrogen atoms as amino, imine, imide, aza or azo groups, also are oxidized to higher oxidation state like nitroso or nitro derivatives (Schemes 1-3).  
The mechanism of electrochemical mineralization of a biologically active compound involves successive, concurrent or parallel electrochemical and chemical steps that depend on the electrode potential or current densities [43,44,45,46].  
Figure 7: Molecular structure of ceftaxone (CFTX).  
Partial electrochemical mineralization and even more, total electrochemical mineralization of such a compound leads to total/partial of its antimicrobial activity decrease, while intermediate or final degradation compounds can affect human health [19; 32].