esa-sceva/satcom-chunk-collection huggingface dataset:
# SCM-IoT: An Approach for Internet of Things Services Integration and Coordination
## 5 Discussion
### Limitations  
The SCM-IoT model has the following limitations:  
* The model is constrained to applications with a fixed interaction structure among producers and subscribers of information. Further study is needed to address the problem of fast-changing interaction structures;
* The model is oriented to IoT systems with low message traffic, low speed, and moderate latency. The complexity of the rules can be high, although it is limited by the storage space available to represent the appropriate size of the notebooks;
* The model proposes an architecture with a broker that provides storage services, analysis, and coordination between clients. The service architecture uses SOA interoperability standards for the composition of web services;
* The participant interaction rules with the storage medium should be designed to reduce competition for shared data so that they are mutually exclusive in updates and notifications that do not include co-readings. This limits the applications as the SCM-IoT model lacks tools to analyze conflicts between the interaction rules. Although tools such as CPN tools can help remedy this limitation, it is necessary to continue studying the properties of the representation of the SCM-IoT model with CPN;
* The application of the rules must always end, leading the environment to a stable state where no further changes are observed. Otherwise, the medium would necessarily have to be modified by the application without terminating at least one rule. For this reason, the SCM-IoT model excludes some applications such as complex event processing [86] that process potentially infinite flows of events;
* The platform-dependent computational model reveals considerable inefficiency for database CRUD operations implemented on XML and XSL The inefficiency is because the entire XML document is almost entirely copied except for the updated field. Fortunately, XML document databases [87] offer a more efficient alternative in implementing CRUD operations.
# Machine Learning-Driven Calibration of Traffic Models Based on a Real-Time Video Analysis
## 4 Simulation Results of the SMTF Calibration Algorithm and Comparison of Results with PTS
To test the stability of the phased array antenna beam steering algorithm when working with data from the PTS, input data, including the specified generated parameters, were created to train the ML model. A special algorithm was developed to generate the target variable--the number of the sector in which the main lobe of the phased array antenna should be directed--in order to minimize connection delays and maintain a stable communication channel for the car. This algorithm associates clusters with sectors based on the vehicle's location at the time of connection to the VZI network or its movement through the simulated area. The algorithm generated random coordinates within the sector area and assigned properties to simulate a request to connect a vehicle. Two identical ET models were trained after generating two SMTF datasets--before and after calibration. Their accuracy was assessed on ELM327 data using metrics to measure regression performance: mean absolute error (MAE), root mean square error (MSE), root mean square log error (RMSLE), coefficient of determination (\(R^{2}\)), and mean absolute percentage error (MAPE). The average cross-validation results on ELM327 data are presented in Table 1.  
Figure 9: Cumulative distribution functions of the speed from the PTS and its generated values before and after calibration. A decrease in the MAE, MSE, RMSE, RMSLE, and MAPE metrics indicates a reduction in the discrepancy between the predicted directions of the main lobe of the radiation pattern and the vehicle's location. A significant increase in \(R^{2}\) indicates a decrease in the variance of the dependent variable relative to the independent ones, which indicates a decrease in data drift for the ML model integrated into the VTS. The results confirm the effectiveness of the calibration algorithm based on global and local regression models and the vehicle data synthesis algorithm using YOLOv5-processed video stream data. Tests also show that the proposed method for extracting vehicle characteristics from a video stream allows data collection without creating a WSN, which requires upgrading city infrastructure and installing sensors on vehicles. This could reduce the costs of implementing urban ITSs.
## 6 Wavelength Tunable 100G Transmitter Module
Compared to higher-order modulation formats, the serial OOK approach is also an economical solution in terms of system complexity, number of components needed for signal generation and detection, latency, footprint, overall power consumption, and so on. The wavelength tunability can further resolve contentions, improve efficiency, and bring flexibility to the network. To exploit this integration platform further, the MZMs and the drivers can form arrays and scale up to 400 Gb/s transmission. Combined with coherent transmission schemes and higher-order modulation formats, the transmitter can provide 1 Tb/s data rate and beyond, in which case a 3D opto-electronic integration is to be implemented, in order to distribute the electronic signals smoothly and evenly among the modulator array [3]. Besides the demonstrated wavelength tunability and polarization management, the polyboard can bring a switching matrix based on thermo-optic switches and enable the multi-flow control of the optical circuits, a function that empowers the software defined network (SDN) directly down to the optical circuit level but is deemed too expensive to be implemented on any other integration platforms. This hybrid integration platform is bound to bring new insights on the general photonic integration paradigm and serves to impact the strategic plans for the deployment of the advanced photonic devices in the telecom and datacom industry.
## 3 Design of the Master and Auxiliary Compound Control  
The function of the MCCS system is to meet the requirement of multiple water sources in supplying a piece of water-requiring equipment stably and continuously. According to the requirements of the MCCS system, the pressure of the MCCS device should not undergo a big sudden change on the premise that the flow rate after gathering meets the requirements, so as to avoid the adverse impact on the subsequent equipment. Especially when a channel must be switched due to the exhaustion of the water source, the pressure of the main pipe will inevitably fluctuate, causing instability in the entire water supply system and causing trouble in the use of terminal equipment. For example, pressure changes can cause cavitation in the centrifugal pump, and deviations in the drop points of water jets from fire monitors. Based on this, the control objective is obtained as follows: under the premise of meeting the water supply requirements, the terminal equipment can obtain continuous medium water and stable pipeline pressure. For switching conditions, the main pipe pressure can remain stable at the maximum value, reducing the pressure loss of the multi-channel concentrated water supply system and improving its efficiency.  
The pressure adjustment of the MCCS system seems simple, but it is difficult to achieve better results with conventional industrial technology. The reason is that this system has unfavorable factors such as nonlinearity, large lag, and large interference. Therefore, this article provides a compound pressure control strategy, including two parts: the main controller and the sub-controller. The main controller controls the main control valve and quickly adjusts the pressure on the main pipe to near the set value. The sub-controller is selectively opened for non-switched channels to control the pressure fluctuations caused by changes in the resistance of the switched channels and interference of the front water supply system, playing an auxiliary role in the pressure stability of the main pipe. The sub-controller adopts incremental PID control, because the incremental PID itself will not cause valve jitter and is more suitable for the small-range adjustment of branch circuits. The main controller adopts variable universe fuzzy PID control. This is due to the uncertainty of the equipment connected  
Figure 8: Co-simulation interactive console program code.  
to the MCCS system. The conventional fixed fuzzy PID control cannot effectively adapt to the changes in system characteristics, and it is difficult to achieve the requirement of the main pipe pressure control under variable working conditions. The variable universe fuzzy PID controller can adjust the fuzzy universe via the expansion factor according to the difference in main pipe pressure, and overcome the limitation of the conventional fuzzy PID controller's limited adaptive ability. In summary, the MCCS compound control structure is shown in Figure 9.
## 4 Discussions
When predicting only the interval growth of the atrophic lesions in FAF using ReConNet-Interval, in the setting of Stargardt atrophy, a median Dice coefficient of 0.557 was achieved. For GA, a median Dice coefficient of 0.601 was achieved. As mentioned, the ReConNet-Interval can only be performed for the first step, as the second step will need an additional visit after 18 months to obtain the interval that is not available for this project. Based on our investigation of the two steps' schema for ReConNet and ReConNet-Ensemble, we can reasonably expect that the performance of the median Dice coefficient for the intervalgrowth of Stargardt and geographic atrophic lesions for ReConNet2-Interval should be both greater than 0.80. In Figure 4, it is possible to compare the initial and final predictions of the ReConNet neural network to the input FAF images and ground truths. Of interest in these images is the improved detail of the final prediction compared to the initial prediction. This indicates that in predicting the FAF image, the neural network may capture features important to the growth of atrophy that are not captured by segmentation alone. In Figure 5, we demonstrate a set of comparisons with the initial and final predictions based on the ensemble neural network alongside the input OCT feature maps and their corresponding ground truths. Once again, there is an improved final prediction after the incorporation of an initially predicted FAF image and ground truth. This indicates that the benefit of incorporating a predicted FAF image for the final prediction is consistently present across imaging modalities. In Figure 6, it is possible to compare the predicted interval growth to the input-edited FAF images. It can be seen that the predicted interval growth tends to underestimate the ground truth. This is also apparent in the high specificity and relatively low sensitivity. While these results are promising, this study is not without limitations. First, while our ReConNet architecture performs well with a limited sample size, the dataset sizes for both Stargardt disease and GA are relatively small, particularly for the GA dataset. Second, the longitudinal patient visits are limited, especially for the goal for the evaluation of the growth interval. Either more longitudinal patient visits or denser visits for ReConNet-Interval should be included to achieve a reasonably good atrophy progression prediction by applying two steps of ReConNet-Interval (i.e., ReConNet1-Interval and ReConNet2-Interval) similar as the ReConNet and ReConNet-Ensemble. Third, the registration process may introduce some errors due to the relatively low image resolution in one dimension. It results in slightly sheared, rotated, or translated registered OCT enface maps in comparison to the corresponding baseline FAF images. Fourth, while the Dice coefficient has been widely used as a standard metric for the evaluation of macular atrophy [51; 52], the investigation of additional new metrics, for instance, as used in [53], may be useful, which can be a future direction of investigation.
## 2 Optical Inspection Technologies in Railways
### Sensor Systems
#### 2.3.4 Pantographs  
The most common damage to pantograph is abnormal wear and cracking. These often occur in combination. Abnormal wear increases the replacement interval of the graphite strips and leads to defects such as eccentric wear, marginal wear, crack chipping, strip burns, concave wear, and arc erosion (Figure 6). Studies have shown that cracks in the pantograph significantly increase the wear rate [57].  
SelectraVision offers a measuring system to check pantographs. Cameras and laser systems attached to masts or other infrastructures are used. The aim is to check geometry, wear, tension and temperature, for instance. Unfortunately, the supplier does not provide any technical information [28]. In [58], a thermal imaging camera is used to detect electrical arc discharges caused by the interaction between the current collector and the OCL.  
When installed outdoors, the camera- and laser-based system is dependent on the ambient lighting and weather conditions (except in the case of artificial lighting). Dirt on the system must be removed manually or an automatic system must be installed. The positioning of the carrier vehicle, including the pantograph, will never be exactly in the same place, which must be considered when aligning and calibrating or focusing in order to obtain sharp images without underexposure or overexposure. The accuracies here will also depend on the distance of the system to the pantograph and the sensor technology used.  
Figure 5: Non-contact optical track position/geometry measurement system using the light-section method by Fraunhofer IPM, installed on a two-way vehicle.
## 3 Proposed Methodology
### The Proposed Att-ResBiGRU Model  
The initial phase of sensor-based HAR involves gathering activity data through intelligent wearable devices. Subsequently, these data undergo preprocessing before being recognized as processed sensor data. Current identification methods are time-consuming and need help distinguishing similar activities, such as ascending and descending stairs.  
To address these issues, we propose a novel architecture named Att-ResBiGRU. The model's network structure, depicted in Figure 3, consists of three key components: convolution, ResBiGRU, and attention blocks. The convolution block, the first component, is responsible for extracting spatial features from the preprocessed data. Adjusting the convolution stride efficiently reduces the time-series length, accelerating the recognition process. Furthermore, the improved ResBiGRU network is employed to extract time-series features from the data processed by the convolution block. The ResBiGRU component enhances the model's capability to capture long-term dependencies in time-series data by combining the strengths of a bidirectional GRU (BiGRU) neural network and incorporating residual connections. This integration enhances the model's grasp of complex temporal patterns, thus improving recognition accuracy.  
We also introduce an attention mechanism to optimize the final recognition features' efficiency. This mechanism calculates weights for the feature information generated by the ResBiGRU network, enabling the model to prioritize the most informative parts of the input data. By emphasizing critical features, the attention mechanism bolsters the  
Figure 3: Detailed architecture of the proposed Att-ResBiGRU model.  
Figure 2: Illustration of fixed-length sliding-window technique used in this work.  
model's ability to differentiate between various activities, ultimately enhancing activity recognition accuracy.  
The classification process of behavior information utilizes a fully connected layer and the SoftMax function. The outcome of this classification process is the recognition result, predicting the specific activity being performed. In the subsequent sections, we thoroughly explain each component, elucidating their functions and contributions within our proposed model.
# Effect of Ambient Temperature on Impedance-Based Physiological Activity Evaluation of Zelkova Tree
## 3 Tree Physiological Activity Monitoring System
### TPAM System Hardware
The data packet constructed by the microprocessor consists of an 8-bit header (0 xF2), 8-bit data ID, 8-bit data length, pay load, and 8-bit tail (0 xF2). The length of the pay load consists of 48 bits for the impedance sensor, and 16 bits for the optical sensor, temperature sensor, and battery voltage level. The 48-bit payload for the impedance sensor consists of a 16-bit reference value, 16-bit channel measurement value, 8-bit channel information, and 8-bit injection current frequency information. The 16-bit payload of a temperature sensor consists of all 16 bits of the temperature value. The 16-bit payload of the optical sensor and battery voltage level consists of both 4 bits of dummy data and 12 bits of sensor value. The hub system consists of a ZigBee wireless communication module and software (TPAM_SW) for the TPAM system. The PC program TPAM_SW extracts sensor data from wirelessly transmitted data packets, rearranges them in chronological order for each sensor, plots them on the screen, and saves them as a file in the storage device. Additionally, if there is no wireless transmission continuously for a preset number of times, a function is implemented to send a system error message to the administrator. The PC program TPAM was developed in-house based on MATLAB R2023a. Figure 5b shows the implemented measurement board. The size of the board is 94 mm wide and 94 mm tall. The main blocks of the measurement board, which are the power supply unit, sensing unit, microprocessor, and wireless communication unit, are marked with boxes. Table 1 shows the supply voltage and current consumption of TPAM system. the fabricated TPAM system operates at 5 V and consumes 172.5 mW of power. The main units, the sensing unit, microprocessor, and wireless communication unit, were measured to consume a power of 36 mW, 22.5 mW, and 106.5 mW, respectively. To measure the tree impedance, four probes were connected to the measurement board. The probe is made of gold-plated Cu, and has a length of 33 mm and a diameter of 1 mm. The corrosion-resistant nature of gold makes it a suitable metal for long-term monitoring applications. When probes are used in the four-point measurement method, current is passed through two probes and the voltage difference between the remaining two probes is measured. On the other hand, when a probe is used in a two-point measurement method, current flows through two probes and the voltage difference between the two probes through which current flows is measured, and the remaining two probes are not used.
## 3 Results and Discussion
### Effect of Building Orientation  
The building orientation was studied for all the three tested buildings. Buildings were rotated 360\({}^{\circ}\) and the best angle for the building was selected based on their energy use. The effect of orientation mainly depends on the symmetry of the building. In the case of a house building, the energy use difference of up to 25 MJ/m\({}^{2}\)/year was observed when changing the building orientation (Figure 19a). This was mainly due to less symmetry in tested house building in comparison with the other tested buildings. The tested office building was more symmetrical and it showed an energy use difference of around 7 MJ/m\({}^{2}\)/year (Figure 19b). The warehouse was a rectangular structure and it showed a minimal difference of 0.7 MJ/m\({}^{2}\)/year for various building orientations (Figure 19c). It  
Figure 16: Effect of window position on energy use in tested office building. (**a**) Energy use; (**b**) Efficiency.  
Figure 17: Effect of W/W ratio in energy use in tested house. (**a**) Energy use; (**b**) Efficiency.  
should be noted that new construction can take advantage of orienting a building in a way that considers energy benefits and cost savings, while modifying the already existing building is more challenging.  
Figure 19: _Cont._  
Figure 18: Effect of W/W ratio in energy use in tested office building. (**a**) Energy use; (**b**) Efficiency.
## 4 Discussion
### Effect of Smartphone Use on Static Posture
#### 4.1.3 Effect of Smartphone Use on Lumbar Spine Posture  
Four studies have examined lumbar spine posture in relation to smartphone use [15; 30; 32; 33]. Prolonged smartphone use has been associated with alterations in lumbar lordosis [15] and an increased prevalence of lumbar musculoskeletal disorders, including lumbar disk herniation, spinal stenosis, and chronic lower back pain [30; 32]. The prolonged maintenance of static postures during work has been identified as a significant risk factor contributing to the increased prevalence of lumbar disorders [30]. Office workers who sit for prolonged periods may face a heightened risk of lumbar disorders due to the increased frequency and duration of smartphone use [30]. Forward trunk flexion during smartphone use increases posterior displacement forces on lumbar intervertebral disks, heightening the risk of herniation and joint stress [15]. Additionally, treadmill-based studies have shown that smartphone use can increase lumbar repositioning errors by approximately 3\({}^{\circ}\)likely due to proprioceptive deficits [32]. These findings were attributed to deficits in the proprioceptive sense associated with lumbar repositioning errors [32]. Considering these results, smartphone use may pose risks by potentially causing lumbar dysfunction through proprioceptive deficits and increased joint stress. A study examining the association between spinal curvature and smartphone use found that using a smartphone with one arm restrained reduced lumbar lordosis angles by approximately 0.07 degrees. This reduction in the lumbar lordosis angle led to increased erector spinae muscle activation, potentially causing various lumbar musculoskeletal issues, such as lumbar disk disorders [33].
# Reduction of Video Capsule Endoscopy Reading Times Using Deep Learning with Small Data
## 3 Results
Video 1 has 22 abnormal segments with a total of 429 abnormal frames, video 2 has 86 abnormal segments with a total of 5050 abnormal frames, and video 3 has 11 abnormal segments with a total of 988 abnormal frames. This means when testing on video 2 and training with videos 1 and 3, we have a significantly lower number of abnormal frames for training than the other two folds. Thus, to compensate for the large variance in abnormal frames per video, we decided to use augmentation to increase the number of frames in videos 1 and 3 as described previously. We then re-trained the networks using the videos with the augmented abnormal frames, again saving the ten intermediate models. Using the first nine models, the same voting procedure was used such that all networks needed to agree a frame was normal for it to be given that label. The results of this experiment with augmentation on the abnormal frames can be seen in Table 4 below. The average reduction in number of frames is 47%, while the number of abnormal segments detected is 119 of 119. A paired \(t\)-test with a confidence interval of 95% showed that this ensemble method with augmented data had a statistically significant improvement in the detection of abnormal segments in the three-fold cross validation over the ensemble without augmentation as shown in Table 5. The same voting procedure was repeated using the last nine models. It was slightly worse in detecting 118 of 119 abnormalities while reducing the videos on average by the same 47% as voting the first nine models. Despite working with a small dataset of only two videos for training in each fold, we were able to develop an algorithm that successfully detected 100% of abnormal segments while reducing the reading time for a physician by 47% on average.