esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions and Outlook  
In this article, we demonstrated many fall detection algorithms, methods and systems based on IoT technology in previous studies. These proposed fall detection systems (FDSs) mainly used artificial neural networks as automated decision-making algorithms to identify fall situations using the measured readings of sensing devices. The advantage of smartphones and/or smart watches has been widely exploited to build a wearable FDS, because they already have the required built-in hardware resources, such as accelerometers and gyroscopes with wireless mobile communications, e.g., Wi-Fi and 5G networks. Other physiological signals, namely heart rate, electromyogram (EMG) and blood pressure have been also applied for FDSs in some particular research works.  
Smart devices like smart phones and watches provide a good opportunity to realize the developed FDSs based on the available hardware resources and wireless sensors in IoT environments. Evaluating fall datasets generated by different sources and accelerometers may contribute to the robustness capabilities of the proposed detection algorithm, because it can deal with variable resolution and sampling rates, verifying its generality. Furthermore, employing artificial intelligence techniques presents the main module of current FDSs. Nevertheless, a pre-processing stage of sensing data, such as thresholding, is needed to reduce error possibilities in identifying fall cases by machine learning algorithms. The computational and power costs of implementing machine learning techniques for fall detection should be kept at minimum levels during the design and implementation stages of practical FDSs.  
Moreover, there is a new trend for applying deep learning techniques and convolutional neural network (CNN) architectures [90] as fall detection algorithms, because of their advantages over classical machine learning methods such as automated feature extraction at different levels with external extractors. Additionally, lightweight deep models have been proposed [91] for mobile-based applications. That presents potentially a new version of smart IoT-based classifiers for detecting fall accidents of elderly patients.  
M.E.K., conceptualization, writing, idea proposal, methodology, results, submission and preparation; H.I.S., writing, data curation, editing, review, supervision; and O.R., writing, review, and visualization. All authors have read and agreed to the published version of the manuscript.  
This research received the support from the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia through the project number (IFP2021-043).  
**Institutional Review Board Statement:** Not applicable.
# _remote sensing_
## 4 Calibration Results
### Sentinel-3A  
S3A is part of the Sentinels constellation of the Copernicus program. It has been in orbit since February 2016 with an orbital cycle of 27 days (14 + 7/27 orbits per day, 385 orbits per cycle) [7]. The synthetic aperture radar (SAR) mode Radar Altimeter (S3A-SRAL) of S3A helps to provide better measurements of coastal areas. The S3A-SRAL was improved along track resolution (approximately 250 m) and, in SAR mode, facilitates SSH measurement close to the coast. S3A has two orbits in this area, of which the descending Pass No. 260 flies over Zhi'wan and Er'zhou islands, and the ascending Pass No. 309 flies over Dangan island (Figures 2 and 12a).  
Figure 12: The SSH and the bias of S3A when it flies over the WSCS. Image (**a**) shows the example of S3A flying through the WSCs. The red dots and magenta-filled area represent the SSH of Pass No. 260 and the Zhi’wan island. The green dots and khaki-filled area represent the SSH of Pass No. 309 and the Dan’gan island. Image (**b**) shows the bias of S3A Pass No. 260 calibrated by the ATGs of the three islands of WSCS, and (**c**) shows the biases of Pass No. 309 calibrated by the hydrological station and ATG of Dan’gan island. The red dotted lines in image (**b**,**c**) represent the drifts of the SSH.  
The SSH of S3A altimeter was calibrated using the ATGs of WSCS. The Jason-3, the MSS, and tidal differences were determined from the CLS 2015 MSS model and HAMITDE12 tide model. The SSH was less contaminated by the islands of WSCS than HY-2B, HY-2C, and Jason-3 as the SAR mode was used. The descending Pass No. 260 was calibrated using the ATGs of Dan'gan, Zhi'wan, and Wai'ling'ding islands. However, the ascending Pass No. 309 was calibrated only using the ATG of Dan'gan island, as the other two islands were too far from the footprints of the altimeter. The sea level data of the hydrological station placed on Dan'gan island were also used in the Cal/Val of the altimeter from January 2017 to April 2020.  
The absolute bias was determined to be \(-16.5\pm 46.3\) mm with a drift of -0.6 mm/year and \(-9.8\pm 30.1\) mm (Figure 12b) with a drift of 0.5 mm/year (Figure 12c) for Pass No. 260 and 309, respectively. The results showed that the SSH of S3A displays a negative sign for the two calibrating passes. Such results are equivalent to the bias calibration in [7].
# Toward QoS Monitoring in IoT Edge Devices Driven Healthcare--A Systematic Literature Review
## 4 Preliminary Concepts
### Quality of Service  
Given how delicate the subject of health is, accuracy and perfection are paramount. Because health facilities cannot accept network performance difficulties or downtime caused by QoS solutions that were earlier suitable, providing QoS is a crucial obligation for smart healthcare apps. Since the information is so important to the patient's health, the system needs to quickly gather reliable data. The QoS requirements must be met when a packet is sent from one place to another as shown in Figure 12. The throughput, bandwidth,  
Figure 11: Machine learning is the process of using carefully crafted code to build systems that learn and evolve on their own [30].  
transmission delay, availability, jitter, latency, and packet loss metrics are among them, as pointed out by [PERSON] in [34]. They are defined as:  
* [noitemsep,topsep=0 pt]
* Throughput: within the time frame specified, data transmission traveled between two points.
* Bandwidth: the optimum rate at which data can move through a network.
* Delay: elapsed time for data traffic to reach its destination.
* Availability: ratio of network's accessibility to inaccessibility by its users.
* Jitter: rate of change in data packets' delays.
* Latency: the sum of the time it takes a data packet to travel from its source to its destination plus any computational delays.
* Packet loss: network issues preventing data packets from reaching their destinations.  
A fundamental QoS model considers the overlay apps and the properties of the underlying network. These views need to be managed consistently because healthcare requirements are constantly changing. Based on their requirements, the network components are in charge of giving the apps a certain level of QoS. Applications can better track QoS metrics, such as the access network, battery life, client preferences, and delays.  
The performance of a network is evaluated using QoS measures. The physical, link, network, transport, and application layers of the network stack are only a few of the layers where these parameters can be considered. Signal strength, signal-to-noise ratio, and bit error rate are examples of QoS parameters that can be applied at the physical layer. Packet loss rate, latency, and jitter are examples of QoS parameters at the link layer. Throughput, routing effectiveness, and congestion control are examples of QoS criteria at the network layer. Round-trip duration, retransmission rate, and error correction are examples of QoS parameters that can be used at the transport layer. Response time, availability, and reliability are examples of QoS factors that can be applied at the application layer. According to the needs and objectives of the network and applications, certain QoS factors may generally be more relevant or significant at different tiers of the network stack.
## 3 Experiment
### Experimental Model  
In this study, three realistic breast phantoms were utilized to evaluate the imaging performance of the system on breast tumors. The fabrication and measurement of each breast prosthesis was conducted following the methodology described in references [33; 34]. Phantom (I) was a homogeneous phantom consisting of a single medium, as shown in Figure 8a. The materials and proportions are listed in Table 1. In homogeneous media, after a reverse time delay, the signal at the focal point where a tumor is present has higher coherence. The model consisted of a 50 mm hemisphere filled with a homogeneous medium simulating normal breast tissue. Based on the clinical investigation and analysis of patients with early breast tumors, it has been found that the diameter of breast tumors can range from 9 to 58 mm with an average diameter of 22.1 mm [35]. Additionally, it has been observed that the closer the tumor is to the skin, the higher the likelihood of lymph node metastasis, and the rehabilitation outcomes tend to be relatively poor [28]. Therefore, a cylindrical medium with a diameter of 10 mm and a thickness of 10 mm was inserted at appropriate coordinates (X = 17 mm, Y = \(-\)23 mm, Z = 45 mm) for simulating a near-skin tumor. Phantom (II) used a three-layer skin-fat-tumor structure to simulate a heterogeneous phantom that more closely resembled a real human breast (Figure 8b). The materials and proportions are listed in Table 2. The outer surface of the model was a hemispherical hollow medium for simulating the skin layer, and the interior was filled with a hemispherical medium for simulating the fat layer. It was noted in a previous clinical study that the uneven thickness of a patient's skin can greatly affect the detection of suspicious areas within the breast [36]. Therefore, we set the simulated skin media thickness to 9-12 mm to simulate uneven skin thickness. The phantom diameter and the tumor diameter and location were the same as for phantom (I). Phantom (III) had an additional 10 mm in tumor diameter compared to phantom (II) and was located at (X = \(-\)25 mm, Y = 18 mm, Z = 45 mm) to simulate the case of multiple targets (Figure 8c). The presence of multiple targets can result in multiple internal reflections that may affect the imaging system.  
Figure 7: The antenna used in the imaging system: (**a**) Top view. (**b**) Bottom view.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Normal Tissue** & **Tumor** & **Purpose** \\ \hline distilled water (mL) & 420 & 420 & solvent \\ polyethylene powder (g) & 480 & 430 & adjusts the dielectric constant \\ agar (g) & 20 & 20 & maintains model shape \\ NaCl (g) & 6.8 & 28.3 & increases electrical conductivity \\ xanthan gum (g) & 6.25 & 6.25 & thickening agent \\ sodium dehydroacetate (g) & 0.25 & 0.25 & preserving \\ \hline \hline \end{tabular}
\end{table}
Table 1: Phantom (I) composition: materials and scale.  
Figure 8: (**a**) Single-tumor homogeneous phantom (I). (**b**) Single-tumor heterogeneous phantom (II). (**c**) Two-tumor heterogeneous phantom (III). (**d**) The profile of phantom (III).
# Targeted Protein Profiling of In Vivo NIPP-Treated Tissues Using DigiWest Technology
## 3 Results
### Protein Profiles of Patients and Cell Culture following In Vivo and In Vitro NIPP Treatment
Additionally, protein profiling immediately after in vivo NIPP treatment (0 h) demonstrated a transient significant increase in cytokeratin 8 (CK8) and 18 (CK18) (Figure S2). After 24 h this effect was reversed. CK18 and the co-expressed complementary partner, CK8, maintain the physiological cell function against external stress and play an important role in apoptosis and the cell cycle [27]. Furthermore, we found a significant decrease in protein kinase B (Akt) expression 0 h and 24 h following in vivo NIPP treatment. The Akt signal transduction pathway promotes cell survival and cell growth in response to extracellular signals by regulating apoptosis and cell cycle [28; 29]. Among other functions, Akt regulates the CDK inhibitor p21 and the proto-oncogene Src, promoting cell cycle progression.
## 1 Introduction  
Assessment of wireless signals, with the intention of determining the precise broadcast parameters of the employed emitter, has been a popular topic of study for decades. The term \"signal identification\" is commonly used to describe to this type of investigation, which has both military and civilian purposes [1; 2; 3]. Signal hacking; wireless surveillance; interference diagnosis and neutralization; and electronic warfare are just a few military applications that have made extensive use of this technology for a long time [4]. The development of smart radios has recently resulted in increased involvement of signal recognition technologies in the frame of reference of industrial uses, such as cellular mobile systems and wireless Local area networks. These radios are reprogrammed, giving them the capacity to change their broadcast configurations, including things such as modulation schemes [5; 6; 7; 8] and channel error-correcting code types [9; 10; 11; 12].  
The likelihood-based (LB) and the feature-based (FB) strategies are two of the most used ways to identify wireless signals. The LB solutions have great identification performance because they construct a probabilistic representation of the collected signals [13; 14]. However, they suffer from a high computing overhead and demand prior awareness of channel parameters. On the other hand, FB methods are designed to identify signals by first extracting their most distinctive properties, such as cyclostationary patterns and signal statistics [15; 16; 17; 18]. Typically, FB-based algorithms are able to reduce complexity while maintaining a satisfactory level of identification performance.  
When designing an identifier for multiple-input multiple-output (MIMO) antenna systems, it is essential to take into consideration the specific technical challenges that are presented by the sort of antenna elements layout. STBC stands for space-time block coding, whereby a numerous duplicates of an input data are emitted in various time slots using many broadcast antennas. This allows for diversity to be achieved while maintaining a basic receiver topology. STBC identification methods require prior knowledge of the modulation type in order to perform properly [19] and modulation detection techniques for STBCs typically serve based on the information that they are aware of the code form [20; 21]. As a consequence, it is possible that sequencing different kinds of these algorithms is not viable in practice. This demonstrates the importance of providing co-recognition algorithms, which can determine the STBC and the modulation schemes in a single operation.
## 3 MG-Based BTS Operation
### Wireless Weather Station  
A wireless weather station has been developed and installed close to the PV modules. The architecture of the wireless weather station is shown in Figure 7. The main purpose of this weather station is to collect environmental data, which will be used in the smart sensing strategy focusing on the prediction of PV generation as part of the VPP control strategy.  
The weather station is made up of two modules: (i) the sensing module, which consists of a set of sensors used to measure environmental parameters related to the PV modules, such as irradiance, wind direction and speed, and PV module temperature; and (ii) the  
Figure 6: **(a)** sudden change in the currents through the non-critical loads; (**b**) measurement error caused by an approximately 40 A change in the DC current of the BESS; (**c**) sudden change in the currents through BESS.  
Figure 7: Wireless weather station architecture.  
acquisition electronics based on an ESP32 microcontroller and a BME 680 sensor. Regarding the sensing module, a SR05 pyranometer [48] is used to measure the horizontal irradiance [49]. This sensor provides an analog voltage output ranging from 0 V to 2 V for measured irradiance values from 0 to 2000 W/m\({}^{2}\), respectively. The wind direction and speed are obtained by a Davis Instruments anemometer with an operating range from 1 to 322 km/h for wind speed with a resolution of 1 km/h using a sensor based on a feed switch whose output is directly connected to the microcontroller. For wind direction, the operating range goes from 0\({}^{\circ}\) to 360\({}^{\circ}\) with a resolution of 1 [50]. Finally, the PV module temperature is measured by a PT100.  
Figure 8 shows the PT100 fixed to the PV module, the anemometer with PV modules at the background and the acquisition electronics of the weather station.  
As far as the second module is concerned, the ADC ADS1115 converts the voltage information from the pyranometer and the anemometer (wind direction) to digital values which are sent to the microcontroller through the I2C protocol. Additionally, the BME680 sensor measures and processes the ambient temperature, humidity and pressure. This information is also sent to the ESP32 microcontroller via an I2C protocol. A fan has been installed to keep the ambient temperature of the electronics stable and minimize any fluctuation in the temperature measured by the BME680.  
The two-core ESP32 microcontroller software architecture is based on the real-time operating system FreeRTOS, which allows to allocate tasks in a particular microcontroller core. One core executes a task which reads the sensor measurements. The other core is responsible for sending the information to the SBC via MQTT (more details will be given in the next subsection), and for sending the information to the IoT cloud of Matlab, named ThingSpeak.  
The variables stored in Thingspeak (panel and environment temperature, humidity, irradiance, wind speed and wind direction) are shown in Figure 9.
# Extending Partial Domain Adaptation Algorithms to the Open-Set Setting
## 5 Experiments
### Experiments and Methodology  
We started by running experiments to test whether outlier target labels were a source of negative transfer in DA. ADDA was run for each task for 50 trials on a full DA scenario with five source labels. The labels were chosen randomly for each task and trial. We then repeated for a scenario where there were 10 target labels and five source labels, and the accuracy was only measured on transfer-relevant instances. The source network was kept the same for each scenario, and the hyperparameters were tuned to optimize the performance for each task. A convolutional architecture was used for the source representer network. We performed this experiment for all four tasks and plotted average accuracies \(\pm 2\sigma\) against the number of iterations.  
For the second experiment, for each task, we varied the number of outlier instances in both the source and target domains, and for each combination, we reported the mean results of four algorithms on ten trials. The algorithms that we used were ADDA, IWPDA, DIWAN, and the source model (without any adaptations). For each trial, the target and source labels were selected randomly. Note also that the number of source labels was always equal to 5.  
For our final experiment, recall that once training was finished, we could use \(\mathcal{W}^{*}\) to calculate (2), where the expectation was replaced by the empirical average over the entire target dataset. We present histograms of weights for each task, where representative setups of labels are chosen. Our aim was to illustrate that the weights tended to be larger for transfer-relevant instances. We then went on to compute the adapted model accuracies after a threshold was imposed. We kept the models from experiment 2 for the case in which there were 10 target labels. The average over the ten trials is presented for each threshold.  
For each task, we used four different thresholds, and for each, we computed the accuracy of our model on the subset of the target dataset that we obtained after the cut-off procedure. We further gave the percentage of total transfer-relevant target instances that remained in the dataset after applying the threshold and the percentage of transfer-relevant instances in the remaining dataset.
## 8 Limitation and Future Research  
The results of this study revealed that self-directed learning positively influences student satisfaction with distance learning. However, the research also revealed that factors associated with the e-learning system, such as system quality, service quality, information quality, and peer interaction quality, did not show statistical significance. While the results concerning self-directed learning might be suitable for generalization to other universities, the conclusions regarding e-learning systems may not be as easily generalized. This limitation arises from the diverse range of e-learning systems utilized by universities worldwide, each with its unique features and capabilities. Consequently, certain crucial functionalities present in one university's e-learning system may be absent in others, possibly affecting the link between student satisfaction and online learning. Thus, this reduces the generalizability of the results to other universities within Saudi Arabia. Moreover, although the sample size was appropriate for using PLS-SEM, the results might still be affected by the limitations of a smaller sample, such as reduced statistical power, which increases the risk of missing significant relationships. This is evident in the study, as self-directed learning is the only factor that shows significant results.  
With regard to future research, a cross-sectional study involving the collection of data from different universities will be conducted. This approach would provide a diverse and representative sample, capturing a range of experiences and perspectives across various institutional settings. Furthermore, collecting data from multiple universities could improve the generalizability of the results, providing a more comprehensive understanding of the factors affecting student satisfaction with online learning. Additionally, comparing the results across different institutions could identify best practices and areas for improvement in online learning, ultimately developing new strategies or modifying existing policies based on informed insight.  
In the discussion of the methodology, it was suggested that future surveys incorporate both positively and negatively worded questions to enhance validity and reduce potential response biases. However, it is important to clarify that this study did not implement this redesigned survey. As a result, the results presented here are based on the original survey design. Future research should consider using the redesigned survey to collect new data, which would provide a more solid foundation for assessing the impact of question design on survey responses and, in turn, on modeling student satisfaction.  
Future studies should also investigate the effect of balanced item wording across various student demographics, despite this study's use of reverse-coded questions to mitigate response bias. Additionally, future research could explore other forms of randomization to see if they lead to even more consistent and reliable results.