esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment One
### Analysis and Results
#### 4.4.2 Vertical Analysis  
In the longitudinal direction, we divided the data into three parts according to \"primary\", \"middle\" and \"recency\" for testing, respectively. The analysis method used three patterns as samples, and we used the Kruskal-Wallis test of k independent samples. The test results are shown in Figure 8. The subjects' preference for primacy in the three modes was significant (\(p=0.001<0.05\)), the middle was not significant (\(p=0.285>0.1\)) and recency was significant (\(p=0.035<0.05\)). The longitudinal data test results show that the decision bias induced by the serial position effect was affected by different evaluation modes, and primacy was the most affected, followed by recency. In order to compare the impact of different variables on user decision making in more detail, we used the Mann-Whitney test of two independent samples to compare the data results of the three modes in pairs. Comparison results for Mode one vs. Mode three, and Mode two vs. Mode three show significant differences in primacy and recency (Mode one vs. Mode three: primacy \(p=0.001\)\(<0.05\); recency \(p=0.025<0.05\). Mode two vs. Mode three: primacy \(p=0.006<0.05\); recency \(p=0.025<0.05\)), none in the middle (Mode one vs. Mode three: middle \(p=0.217>0.1\)  
Figure 6: (a) is a statistical line graph of user decision-making preferences in three modes, (b) is the distribution of extremely irrational decisions among different groups.  
Mode two vs. Mode three: middle \(p\) = 0.141 > 0.1) and no significant differences in the three positions of Mode one and Mode two (Mode one vs. Mode two: primacy \(p\) = 0.394 > 0.1); middle \(p\) = 0.773 > 0.1; recency \(p\) = 0.954 > 0.1).
## 1 Introduction
This approach involves creating smart packaging to detect pathogens and transforming this information into a detectable signal that would allow for the early detection of food contamination. Due to the fact that there is insufficient research on the safety of AgNPs in food, the EU does not allow their use in dietary supplements or food packaging [15]. However, in 2021, the EFSA published information in which it was concluded that the use of AgNPs as an additive in an amount of up to 0.025% \(w/w\) in polymers such as polyolefins, polyesters, and syrenes that do not swell in contact with hydrated food does not raise concerns regarding their use. A summary of the most important applications of AgNPs is shown in Figure 1.  
The antibacterial properties of silver nanoparticles are well known and have been confirmed by numerous studies [7; 16]. AgNPs have been shown to inhibit the growth and multiplication of such bacteria as _Escherichia coli_, _Bacillus cereus_, _Staphylococcus aureus_,  
Figure 1: Applications of AgNPs. Pseudomonas aeruginosa, Citrobacter koseri, Salmonella typhi, Klebsiella pneumoniae, and Vibrio parahaemolyticus[14]. However, especially recently, a lot of research has been published on the antifungal activity of silver nanoparticles against many species of fungi. Nanoparticles synthesized by chemical, physical, and green synthesis methods are active (Figure 2). Testing the activity of AgNPs against fungi is particularly important due to the enormous resistance of pathogenic strains to systemic drugs. We can observe that silver nanoparticles accompany us in everyday life in household and biomedical products and possess significant positive properties, including the ability to eliminate pathogenic microorganisms. However, it should be noted that their use also raises concerns about their potential threat to human health and the environment. The major routes of entry of AgNPs are ingestion, inhalation, and dermal contact. Their toxicity is due to the fact that AgNPs are able to induce inflammation and oxidative stress at the site of exposure. Despite numerous toxicological studies, long-term toxicity data are still lacking. However, an occupational respiratory exposure limit value of 0.19 \(\upmu\)g/m\({}^{3}\) for AgNPs has recently been proposed based on a subchronic inhalation toxicity study in rats [17]. Research using in vitro cell cultures indicates that AgNPs are toxic to several human cell lines (human bronchial epithelial cells, human umbilical vein endothelial cells, red blood cells, human peripheral blood mononuclear cells, immortal human keratinocytes, liver cells). Furthermore, tests conducted in vivo using mice, rats, and zebrafish have demonstrated that AgNPs can penetrate the blood-brain barrier and accumulate in organs such as the liver, kidneys, and spleen. The induced cytotoxicity, however, largely depends on the size of the nanoparticles, dosage, and duration of exposure [17; 18].
## 3 Methodology
### Generative Adversarial Network
The generation loss is based on the performance of distinguishing the generated check-in records to the discriminator:  
\[loss_{gen}=log(1-D(\hat{\v}|u,V_{u})), \tag{7}\]  
where \(D(\hat{\v}|u,V_{u})\) represents the result of discriminator whether the predicted check-in vector \(\hat{\v}\) (i.e., the fake sample) is considered as a real or fake sample given a specific user \(u\) and a temporal check-in records \(V_{u}\). In general, the generator is updated based on \(loss_{gen}\), which directly shows the quality of produced fake samples. In this paper, due to the sparsity of actual check-in vector based on an individual and specific temporal check-in records, \(loss_{pred}\) is used to accelerate the convergence of capturing the actual distribution to the generator. In addition, due to the significance of geographical influence in POI recommender systems, \(loss_{loc}\) is utilized to take the distance from the previous visited POI to the next recommended POI into account. Therefore, the objective function of the generator is calculated based on the following equation:  
\[J^{G}=\min_{\theta}\sum_{i=1}^{m}(\alpha loss_{pred}+\beta loss_{loc}+(1-\alpha -\beta)loss_{gen}), \tag{8}\]  
where \(\theta\) is the parameter of generator while \(\alpha\in(0,1)\) and \(\beta\in(0,1)\) is the predefined weight of \(loss_{pred}\) and \(loss_{loc}\), respectively. The objective function of discriminator is similar to that of general binary classification, which can be calculated as below:  
\[o_{c}=softmax(ReLU(o_{a}\times w_{c_{1}}+b_{c_{1}})\times w_{c_{2}}+b_{c_{2}}), \tag{2}\]0  
where \(\v\) and \(\hat{\v}\) are the real and predicted check-in vector given a specific user \(u\) and temporal check-in records \(V_{u}\), respectively. Note that we exploit the general adversarial setting in this paper where the generator and the discriminator will be alternatively updated once based on the corresponding objective function until both of them converge. The GANs are trained to be good at understanding the known information. In our proposed model, the user and POI embedding are used to capture the characteristics of each user and POI. Due to the sparsity of check-in records, it is hard for the model to train the embeddings sufficiently. Therefore, the GANs are useful for the model to capture the embeddings of users and POIs well. In addition, the GANs can be good at providing appropriate prediction. Even without the consideration of \(loss_{pred}\) and \(loss_{loc}\), the generator in this paper is trained to provide the next POI. In detail, the input of the generator is the latest three visited POI (i.e., 3-size sliding window) which describe the current situation of the corresponding user, while the output of the generator is the next POI which would be interested for the user in the current situation (i.e., pattern). Therefore, the structure of GANs provides the effective strategy for our model to capture the user's preference based on the contextual check-ins.
# Hand Gesture Recognition on Edge Devices:
## 10 Evaluation of HGR According to Focus Areas
### Focus Sensor Fusion  
All reviewed modalities have advantages and drawbacks as summarized in Table 4. Combining different sensor types with sensor fusion for hand gesture classification leads to improved robustness and accuracy according to [52; 86; 87; 88; 89; 90]. Table 7 compares approaches that implement combinations of several sensor types. Only [52] works without vision data and relies on depth data to evaluate the accuracy of radar data and for calibration. Two of the revised systems are implemented as real-time systems. The majority of systems, Refs. [88; 89; 90], use an online data base that provides skeleton data points. The authors of [87; 89] compare several different fusion structures. Ref. [89] found that score-level fusion resulted in highest accuracy. In this case, the scores of classification on skeleton data and of depth data are combined by calculating their average. Since both sensor types are initially processed individually and the classification result is combined, this can be seen as late fusion. The authors of [87] also found the best results when combining the information from different modalities late in the classification process. More specifically, radar, optical, and skeletal data are first separately classified. The output of these classifiers are vectors that contain a score for each possible gesture relating to the probability that this gesture is the performed one. In a subsequent step, these vectors are further combined and processed by another network. The output of the second network provides higher accuracy values. The authors of paper [86] pre-process depth and RGB data individually, but use depth data to extract angular velocity values from the radar data only in the region of the moving hand. In their evaluation, they compare accuracies from classifying each of the modalities individually with classifying the joined input from all modalities. If all modalities are used, the input of the network already contains all information. This approach therefore relies on early fusion.  
The majority of research up to the present relies on HGR based on one sensor type, but the results of the available research on sensor fusion approaches for HGR show increased robustness and accuracy through sensor fusion. The reason for this effect is probably that different sensors complement each other. The drawback of one sensor can be made up for by using another that is especially well suited to make up for this drawback. Table 4 can serve as a starting point for selecting modalities for optimal combinations. It must be mentioned that sensor fusion models require several sensors and thus more--and potentially bigger--hardware, power, and processing requirements. It follows that sensor fusion is a good way to improve the robustness of an HGR system. Therefore, sensor fusion is recommended for systems that require high reliability. Simultaneously, it may not be the right choice in cases where the main focus lies on reducing system size or processing and power requirements.
## 3 Results  
We constructed a ViT model using a total of 598 exams, 512 of which were small bowel CE exams (457 were PillCamTM SB3 and 55 were OMOM(r) HD Capsule), and the remaining 86 were colon capsule CE exams (PillCamTM Crohn's Capsule).  
After triple validation, a total of 7982 frames were used, with 2942 including pleomorphic esophageal lesions (protruding lesions, ulcers and erosions, vascular lesions, hematic residues, and esophageal diverticula). The number of frames and patients, as well as types of CE device are displayed in Table 1.  
The metrics calculated for each run are displayed in Table 2. The median sensitivity, specificity, and accuracy were, respectively, 75.8% (range 63.6-82.1%), 95.8% (range 93.7-97.9%), and 93.5% (range 91.8-93.8%). The median positive and negative predictive values were, respectively, 71.9% (range 50.0-90.1%) and 96.4% (94.2-97.6%). The median AUC-ROC and the AUC-PR for the detection of pleomorphic esophageal lesions were, respectively, 0.82 and 0.93 (Figure 4).  
In the test set, each frame took 26 \(\pm\) 3 milliseconds to process.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Frames (_n_)** & **Patients (_n_)** & **Types of Devices (_n_)** \\ \hline \multirow{2}{*}{Run 1} & Train/Validation & 7320 & 538 & 3 \\  & Test & 662 & 60 & 3 \\ \hline \multirow{2}{*}{Run 2} & Train/Validation & 7508 & 538 & 3 \\  & Test & 474 & 60 & 3 \\ \hline \multirow{2}{*}{Run 3} & Train/Validation & 6912 & 538 & 3 \\  & Test & 1070 & 60 & 3 \\ \hline \end{tabular}
\end{table}
Table 1: Number of frames, patients, and types of CE device per group, divided in a patient-split design: training/validation (90% of patients) vs. test group (10% of remaining).  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Sensitivity (\%)** & **Specificity (\%)** & **PPV (\%)** & **PPN (\%)** & **Accuracy (\%)** \\ \hline \multirow{2}{*}{Run 1} & 75.8 & 97.9 & 90.1 & 94.2 & 93.5 \\ \multirow{2}{*}{Run 2} & 82.1 & 93.7 & 71.9 & 96.4 & 91.8 \\  & 63.6 & 95.8 & 50.0 & 97.6 & 93.8 \\ \multirow{2}{*}{Median} & 75.8 & 95.8 & 71.9 & 96.4 & 93.5 \\  & (63.6–82.1) & (93.7–97.9) & (50.0–90.1) & (94.2–97.6) & (91.8–93.8) \\ \hline \end{tabular}
\end{table}
Table 2: Data were divided in training/validation and test groups, in a patient-split design. Three runs were conducted, each with unique patient sets. Metrics were calculated in test set of each of run.
# Software-Defined NB-IoT Uplink Framework--The Design, Implementation and Use Cases
## 1 Introduction  
Sensors and sensor networks are nowadays identified as a ubiquitous solution present in everyday life. The development of the Internet of Things (IoT) is rapid, mainly due to the demand for automation and constant monitoring. The large amount of data generated by the IoT devices determines the need for network and data processing optimization and further designing a global solution for large business entities. In general, the scientific research papers involving the IoT networks may be divided into various academic branches of knowledge, such as radiocommunication, informatics, or electronics.  
The new technology introduced in the 5G networks, where the narrowband Internet of Things (NB-IoT) interface is assumed to be present [1], is mostly based on the digital signal processing. The elements of the core network are designed to be the virtual components, and some parts of the terminals may be realized digitally. It seems natural to propose a software-defined framework that will provide the functionality of the NB-IoT terminal with the internal operations split into functional modules, which is the main goal of the conducted research. Decomposition of the uplink or downlink path gives an ability to distinguish educational and potential research and development possibilities. The proposed framework may also play the role of a novel laboratory testbed (even totally virtual), as a part of the software-defined radio (SDR) laboratory stand, including the radio frequency (RF) measurement equipment or over the air (OTA). The NB-IoT signals generated based on proposed framework may be verified regarding reference vectors, software base stations (BSs) simulators, or measurement devices.  
In the presented framework, the signal processing path for the the NB-IoT uplink interface is implemented. The proposed modular structure of the software may support the learning process, including both advanced aspects of radiocommunication and programming in general. Additionally, such a framework is a fine tool to teach students how toextract theoretical assumptions based on the technical specification (e.g., the 3 GPP technical documents) and further implement them. The advantage of the framework is its operation in the physical (PHY) layer of the NB-IoT interface, with low-level decomposition, which gives the ability of using simple and flexible tools for evaluation of the student implementations, without any auxiliary advanced protocol analyzers, interpreters, or even commercial licensed software.  
The rest of the paper is structured as follows. The general overview of the NB-IoT interface is provided in Section 2. In Section 3, the related works and similar approaches are described. In Section 4, the proposed software-defined framework structure is presented. In Section 5, the uplink physical layer modular implementation in accordance with 3 GPP standardization is proposed, while in Section 6, the analysis of the obtained results is presented. The paper is concluded briefly in Section 7.
# A Deadline-Aware Scheduling and Forwarding Scheme in Wireless Sensor Networks
## 2 Related Work
(2016) introduced a fast time-dependent shortest path algorithm (FISP) based on the Bellman-Ford algorithm. Messages are exchanged between nodes, and they try to build time-dependent paths with the least delay. However, time and message complexity are high when the total number of nodes increases in WSNs. In particular, when there is a change in the network (_i.e._, dead nodes), FTSP takes more time to again calculate the path of the least delay. In contrast, our algorithm uses only local information for packet forwarding and, hence, can quickly adapt to the network changes. There are a few routing protocols on meeting a given delay bound in WSNs [PERSON] et al. (2016); [PERSON] et al. (2016); [PERSON] et al. (2016). [PERSON] _et al._[PERSON] et al. (2016) introduced DutyCon to solve the delay problem. Initially, they build \(w\) disjoint paths, each of which is generated by one of the \(w\) sources in the network. After that, they apply feedback control theory to adjust the receiver's sleep interval according to a collected single-hop delay. However, they assume that each node must know global information of its path in the WSN, such as all communication delays of each hop. In contrast, our algorithm does not require global information and nodes do not even maintain scheduling of their neighbors in advance. [PERSON] _et al._[PERSON] et al. (2016) proposed a localized forwarding algorithm with asynchronous scheduling that does not maintain neighboring node information in advance. First, by assuming that a simple forwarding strategy is used, a source node evaluates a desired mean and variance of the E2E delay that can meet a given delay requirement. After that, in order to satisfy the desired mean of the E2E delay, the authors design a forwarding strategy, such that a relay node makes a forwarding decision as to whether to wait for a better next hop or forward the packet immediately. However, in their work, when a node actually forwards a packet, it only tries to meet the desired mean of the E2E delay without considering variance. As a result, it may not achieve the required DCSR. In contrast, our scheme can achieve the required DCSR by using the optimal duty cycle interval obtained from the estimated distribution of E2E delay. To sum up, even though there are several studies on scheduling and routing that address delay problems in WSNs, none of them can estimate the distribution of E2E delay before the actual node deployment. However, our algorithm can adjust the duty cycle interval to satisfy delay requirements according to the real routing strategy. In addition, our work only needs local information and does not require time synchronization.
## 3 Implementation of The Proposed System
### Data Storage  
#### 3.1.1 Metadata  
Metadata is the data about data, which is used to describe the characteristics and attributes of other data for experimental protocol, data interpretation, management, preservation, retrieval and sharing. In different scenarios, the metadata has completely different meanings.  
In this work, there are two types of metadata, i.e., the file metadata of the distributed file system HDFS and the plant electrophysiological metadata. HDFS metadata is used to describe the file and directory information of the HDFS file system and data block information. The plant electrophysiological metadata belongs to the scientific data metadata, which is mainly used to describe the experimental object, the experimental conditions, the stimulus way, the experimental data, the experimental target and the record information. The electrophysiological metadata of plants are shown in Table 3.
## 3 Parametric Studies
### Effect of Substrate Materials  
An analysis is carried out to select the appropriate dielectric for the substrate of the metamaterial unit cell, which is shown in Figure 5. Two Rogers dielectrics, namely, RT 5880 and RT 6002, together with a flame-retardant dielectric FR4, were separately investigated to observe their responses. RT5880 and FR4 produced three resonance peaks with narrow bandwidths. Contrarily, Rogers RT6002 (dielectric constant 2.2 and loss tangent 0.0012) yielded tri-resonance peaks and adequate bandwidth covering the S-, C- and X-bands with higher EMR 12.61. Hence, the proposed unit cell was designed on Rogers RT6002 (lossy) substrate.  
Figure 4: Transmission coefficient (S\({}_{21}\)) for different dimension of the unit cell.  
Figure 5: Transmission coefficient (S\({}_{21}\)) for different substrate materials.
# Two Designs of Automatic Embedded System Energy Consumption Measuring Platforms Using GPIO
## 1 Introduction  
With the rapid development of System-on-Chips (SoC) and the demands of modern smart life, large numbers of embedded systems are being deployed everywhere in our daily life. A smart Internet of Things (IoT) system may consist of thousands of embedded devices, rendering the system expensive and hard to rebuild. Both operators and users expect these devices to last for long periods of time with little maintenance. Many embedded systems are deployed without an ease of maintenance and are self-powered with batteries or solar cells. There are also many embedded systems that require constant power with very compact designs. Energy consumption has significant impacts not only on battery life, but also on the hardware thermal design and the device's security and reliability. To make sure that designed embedded systems are eligible for low energy consumption requirements, many methods to measure or model power consumption have been proposed. However, power consumption is not enough for hardware design and code optimization; the impact of \"time\" should also be carefully considered.  
Herein, we propose two slightly different designs of energy consumption measuring platforms for embedded systems, one for higher precision and the other for simpler deployment. To measure the energy consumption accurately, we used General Purpose Input/Output (GPIO) in our designs for timing and synchronization. We also designed a series of test sets and verified the capabilities of our designs.
# NESO-Based Path Following Control for
###### Abstract  
In this brief, the path following control problem of underactuated hovercrafts subject to nonlinear time-varying uncertainties and a safety limit constraint (SLC) is addressed. A novel homogenous nonlinear extended state observer (NESO)-based safe motion controller for a path following control scenario of underactuated hovercrafts is proposed. First, a NESO is constructed to estimate and compensate the nonlinear time-varying uncertainties for the underactuated hovercraft. Then, a NESO-based backstepping sliding mode control (BSMC) law with a turning SLC is proposed to achieve the yaw control for underactuated hovercrafts, which improves both safety and maneuverability of the underactuated hovercraft during the path following control scenario. The nonlinear time-varying turning SLC is first directly taken into the control system design, which is achieved by introducing an auxiliary dynamic system to limit the virtual input control during the backstepping design process. A NESO-based backstepping surge control law is also designed to achieve the surge control for underactuated hovercrafts. Furthermore, all error signals of the proposed closed-loop control system are proven to be bounded. Finally, an application case is tested on an underactuated hovercraft to illustrate the effectiveness and superiority of the designed control scheme.  
underactuated hovercrafts; nonlinear time-varying uncertainties; turning safety limit constraint; homogenous nonlinear extended state observer; backstepping sliding mode control +
Footnote †: journal: Applied  
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Footnote 1: _Applied_