esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Prototype Validation Firmware
### Testing Commands
#### 5.1.2 Device-oriented Commands  
Three hardware components of the MSP board were tested and verified with respect to the microprocessor: the _u-box(tm) SARA-R5_ radio module based on the UART interface, the _Linear Technology(r) LTC2942_ battery gas gauge based on the I2C interface, and the _STMicroelectronics(tm) IIS2 DH12_ accelerometer based on the SPI interface.  
SARA-R5 Module: Setup/Write  
The function  
\[cmdErr\_t\,sRadioSaraCmd(cmd\_t*\,cmd)\]  
is implemented for the radio module 'Setup'. On the terminal, it returns the state of the device: ON/OFF.  
The function  
\[cmdErr\_t\,wRadioSaraCmd(cmd\_t*\,cmd)\]  
is implemented for the radio module 'Write'. The _wradio <cmd>_ sends the AT command. When correctly executed, an _OK_ is received. Otherwise, a general AT command can be used. It returns, for example, the manufacturer, model, and serial number IDs.  
Accelerometer: Axes Reading  
The function  
\[cmdErr\_t\,rAccCmd(cmd\_t*\,cmd)\]  
provides the Accelerometer \"Axes Reading\". It is implemented to read the three axes with a decimal number in the sequence X, Y, Z. The acceleration level is specified through the sensitivity end-scale options of 2 g, 4 g, 8 g, and 16 g.  
Battery Gas Gauge: Read/WriteThe function  
\[cmdErr\_t\ wGaugetCmd(cmd\_t^{*}cmd)\]  
provides the battery gas gauge \"Write\". The battery gas gauge data are stored in internal memory, composed of 16 registers subdivided into: status register ('A'), control register ('B'), accumulated charge register ('C', 'D'), threshold registers ('E', 'F', 'G', 'H', 'K', 'L', 'O', 'P'), and voltage and temperature registers ('I', 'J', 'M', 'N'). When _wGaugeCmd_ runs on the terminal, _urreg[0... 15]_ sets the number of the register to be written; _regvalue[A... P]_ sets the register value.  
The function  
\[cmdErr\_t\ rGaugetCmd(cmd\_t^{*}cmd)\]  
provides the battery gas gauge \"read\". On the terminal, _regvalue[A... P]_ sets the register value to be read.
## 2 Materials and Methods
### Antimicrobial Activity  
In order to determine the antibacterial activity of the extract, a viable cell assessment via fluorometric reading was employed. EUCAST inhibitory assay considerations were followed with slight modifications to avoid pH and bile salt resazurin interferences [23; 24]. A bacterial suspension was prepared by diluting a 0.5 McFarland standard (-10\({}^{8}\) CFU/mL) in CAMHB to a final concentration of 10\({}^{6}\) CFU/mL. Subsequently, 100 \(\mu\)L of this bacterial suspension was mixed with 40 \(\mu\)L of extract or digestate at different concentrations (0%, 0.625%, 1.25%, 2.5%, 5%, and 10%, \(v/v\)), and 60 \(\mu\)L of PBS (1 M), resulting in a final well volume of 200 \(\mu\)L. The final number of viable bacterial cells per well was 5 \(\times\) 10\({}^{5}\) CFU, as recommended by the EUCAST guidelines. Concentrations of the extract and digestates were calculated as a percentage relative to this final volume. The microplate was incubated for 21 h at 37 \({}^{\circ}\)C. A blank of the extract was employed by incubating 100 \(\mu\)L of CAMH broth instead of the bacterial inoculum. After the incubation, 100 \(\mu\)L of fresh culture broth, 60 \(\mu\)L of phosphate-buffered saline (PBS, 1 M), 20 \(\mu\)L of AlamarBlue, and 20 \(\mu\)L of each well from the overnight incubated plate were mixed in a new 96-well microplate. The plates were incubated at 37 \({}^{\circ}\)C until blue to pink colorimetric change was observed, normally from 30 min to 2 h. Fluorometric reading was performed to determine the viability of the cells, as previously described by multiple authors [25; 26; 27; 28; 29; 30]. Fluorescence assays were conducted with measurements taken at an excitation wavelength of 544 nm and an emission wavelength of 590 nm, utilizing the FLUOstar microplate reader (BMG Labtech, Ortenberg, Germany). The same procedure was followed for the evaluation of the gastrointestinal content and control samples. Each sample was tested in triplicate in order to obtain statistical significance data to calculate the IC50 (minimum concentration required to inhibit 50% of the bacterial population) and MBC values (minimum bactericidal concentration) [25; 26; 27; 28; 29; 30].
## 2 Related Work
### Segmentation for the Multi-Source RS Data  
Existing segmentation methods for image and point cloud data mainly concentrate on planar (or other geometrically primitive) feature extraction, most of which are computationally expensive and often utilize 2D or 3D information alone. Based on the above consideration, several segmentation schemes combining 2D features from digital image with 3D features from point cloud data are proposed.  
Reference [39] builds 3D models of urban environments based on 3D range scans and 2D images. This method utilizes a range segmentation algorithm to extract planar regions and linear features so that a 3D range scan could be converted to a set of bounded planes and finite lines. However, the applicability of the algorithm is limited due to planar regions and linear features are only common for urban environments, but geological objects are often characterized as irregular features. Reference [40], for segmenting a colored laser point cloud, an extension method for camera-only graph-based segmentation is proposed. In this method, segment unions based on spatial proximity is proposed, and a dynamic segment union criterion based on color and surface normal produces a quality segmentation. However, this method requires a fixed positional relationship between camera and laser scanner in order for the co-registration. Reference [41] takes both colorimetric and geometric data as input, clusters pixels and divides the colorimetric data by use of SLIC algorithm, then produces normal vector for each superpixel with a plane-fitting technique, and last performs classification of superpixels by a multi-class support vector machine technique. This scheme in reference [41] needs to place several colored markers in the scene prior to data acquisition, which is infeasible for some geological objects, such as alpine and gorge region which is often dangerous and inaccessible. Besides, although the SLIC algorithm with high efficiency is utilized in this reference, it still performs segmentation only by color vector and planar position of pixels, and ignores the local rock surface attitude, which is an important feature for geological objects. It may affect the rock surface extraction precision, especially for those regions with similar color and texture.  
The MFF-SLIC algorithm developed in this paper performs superpixel segmentation not only by color and planar position features but also by spatial and some geological features, which guarantees the accuracy of complicated rock surface extraction.
## 3 MEC Implementation in Vehicular Networks
### MEC Layer  
Gathering and processing data from VTs are performed in this layer. MEC servers are installed in proximity to RSUs, and their computation and storage resources are usually larger than the VTs'. Moreover, MEC servers have more communication resources in order to access the core network, such as fiber/cable connections, which enables them to access the core network at a high speed. After processing the data from the VTs, some of the collected data and computation results may be transferred to the core network layer. Deploying MEC servers aims to reduce communication overhead and end-to-end delay. Moreover, MEC servers manage local demands and tasks produced by vehicles and respond to them.
# SAET: The Non-Verbal Measurement Tool in User Emotional Experience
## 4 Validation of the SAET Images
### Pilot Study
#### 4.1.5 Discussion  
The image designs with emotions that have green color blocks in each vertical column of Table 2 are considered to require modification and iteration. The PAD values of words can effectively guide the design element characteristics of expressions. For example, the PAD values for anger are (\(-\)0.74, 0.36, 0.26), which shows that people's emotional tendency for anger is strongly unpleasant, moderately activated, and moderately low dominance, and the design direction can be eyes open like an inverted eight, eyebrows furrowed, and mouth tightly closed and pulled down. In contrast, the PAD values of sadness were (\(-\)0.75, \(-\)0.27, \(-\)0.55), it can be seen that people's emotional tendency towards sadness is strongly unpleasant, moderately low activated and moderately low dominated. The designed direction can be eyes slightly squinted, eyebrows slightly furrowed, eyes spaced wider and mouth slightly closed and slightly pulled down. In addition, in interviews with participants, it was found that many expressions struggle to represent differences in emotion relying only on the eyes and mouth, suggesting the inclusion of another key feature, the eyebrows. In a study by [PERSON] [25], eyebrow movement was found to be associated with communication and the type of facial emotion-expressive movements depended to a large extent on the ambient eyebrow dynamics. This shows that eyebrows are an element that cannot be ignored in the design of facial emotion expression, so the design iteration adds the element of eyebrows to the first round of experiments to further enhance the embodiment of different emotions in the images.  
The iterative design of the image refers to the PAD value of the initial text on the one hand, and adds eyebrows and modifies emojis to the facial feature elements on the other hand such as red blush, yellow stars, and hand gestures. For example, the \"satisfied\" expression is expressed through the \"OK\" gesture, while \"proud\" is expressed through the one-sided rise of the mouth and the addition of yellow stars at the end of the eye. Finally, according to the relationship between the size of PAD values, positive and negative shapes, and facial features, the design of 20 emotion expressions was modified as shown in Figure 3.  
Figure 3: Iterative image design of 20 emotions. The emotions are, from left to right, top to bottom: happy-for, hate, satisfaction, gratitude, reproach, distress, pride, fear, mildness, pity, boredom, shame, disappointment, hope, resentment, love, gloating, anger, relief and admiration.
# Experiencing LoRa Network Establishment on a Smart Energy Campus Testbed
## 7 Discussion
### Application Aspects of LoRa Deployment  
LoRa will enjoy a variety of IoT applications, such as smart agriculture, smart cities, smart environments, smart healthcare, and smart buildings, all of which are expected to enhance the efficiency of business and improve our quality of life [30]. For instance, smart farming integrated with IoT technologies can measure the environmental conditions that affect crop production and to continuously monitor the health of livestock. This eventually reduces the environmental impact, maximizes yields, and minimizes costs. A smart city provides a framework in which advanced services for citizens are connected in such a way that they are expected to enhance the comfort of city life. These city services include lighting control, parking management, waste management, environmental monitoring, and smart metering. It is noted that different applications and services have dissimilar requirements for communication networks, indicating that different strategies for network deployment must be established. Smart traffic, for instance, requires a network to handle users' mobility, whereas a network in the smart city must be able to support long-distance communications [31].  
This study proposes a strategy for deploying gateways and nodes to configure LoRa networks for a smart energy campus. The smart campus scenario can be easily expanded to the smart city concept; a countless number of sensors, deployed in a wide area, generate data streams that are then collected and analyzed, enabling the management of city assets and resources more efficiently. In such a large-scale application, it may not be easy to find path loss models for every geographical section, analyze communication performance accurately, and combine the results to establish a network. Instead, our approach of adopting a simplified model to evaluate performance in a more comprehensive manner can benefit the application.
## 3 Validation and Numerical Analysis
### Time-Varying Effect of the Bearing Stiffness  
In order to analyze the bearing stiffness, three cases of bearing stiffness are discussed in this section. Case 1 is for bearing stiffness \(k_{xx}=k_{yy}=k_{zz}=1\times 10^{7}\) N/m, and cases 2 and 3 are \(k_{xx}=k_{yy}\) = \(k_{zz}=5\times 10^{7}\), \(1\times 10^{8}\) N/m, respectively. Figure 9 shows the time-varying dynamic responses for the helix angle, transverse pressure angle, gear pair center distance, and total contact ratio. If the bearing stiffness is increased, the difference values between the time-varying values and the constant values are enhanced. The bearing stiffness is closely linked with the difference values between the time-varying values and the constant values of these dynamic characteristics. This means that the time-varying variables have a significant influence on the ordinary bearing stiffness, but have a small effect on the large bearing stiffness, especially for the total contact ratio.  
As seen in Figures 7-9, it is interesting that the results of the dynamic characteristics of the helical-geared rotor-bearing system with time-varying variables are very different from those which are considered as constant when the rotating shaft is sufficiently soft, thinner, or the bearing stiffness is sufficiently small. This implies that the time-varying effect has a significant influence on the gear vibration analysis and cannot be ignored.  
Figure 8: Time-varying and constant (**a**) helix angle, (**b**) transverse pressure angle, (**c**) gear pair center distance, and (**d**) total contact ratio under different outer diameters of the shaft.
## 4 Simulation Analysis of PER and EE for Three-Stage Incremental Cooperative Communication
### Ee  
The EE of direct and the incremental cooperative communication schemes is observed in Figure 3a,b. A series of experiments are performed in [6] to find the best distance between the source and relay and between the relay and destination. The hop lengths of \(S-R\) and \(R-D\) links are selected to be 0.5-times the distance between the source and the destination. Figure 3 shows the results for the EE of on-body NLOS and LOS scenarios, respectively. EE is plotted against \(S-D\) hop length, \(r_{sd}\), for the direct and cooperative communication schemes.  
It is concluded from the results that increased distance between the source and destination, \(r_{sd}\), decreases EE. For lower distances, direct transmission proves to be considerably more energy efficient than cooperative transmissions. When the hop length, \(r_{sd}\), exceeds a certain threshold, cooperative communication turns out to be more energy efficient than direct communication. Although cooperative communication improves reliability, as it has lower PER, its EE is significantly affected, because of the increased energy consumption due to additional transmissions and decoding by the relays. When \(r_{sd}\) is above the threshold, the PER of direct communication is so high that its EE is significantly affected. As the PER of three-relay communication is lower than the other two relaying schemes, it has the lowest EE due to more energy consumption by the relays.  
Figure 3: Energy efficiency (EE) analysis. (**a**) EE for on-body NLOS communication; (**b**) EE for on-body LOS communication.  
Figure 2: Packet error rate (PER) analysis. (**a**) PER for on-body NLOS communication; (**b**) PER for on-body LOS communication.
## 3 Method
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Application** & **Task** & \begin{tabular}{c} **Image** \\ **Modality** \\ \end{tabular} & **Dataset Size** & **Remarks** & \begin{tabular}{c} **Anatomical** \\ **Site** \\ \end{tabular} \\ \hline \begin{tabular}{c} Cervical \\ cancer \\ classification \\ \end{tabular} & Image & \multirow{2}{*}{Colposcopy} & 45,009 images from & Labeled images: & \multirow{2}{*}{Organ} \\  & & & the NCI dataset & 978; unlabeled & \\ \hline \begin{tabular}{c} Skin lesion \\ classification \\ \end{tabular} & \begin{tabular}{c} Lesion \\ classification \\ \end{tabular} & \begin{tabular}{c} Dermoscopy \\ \end{tabular} & \begin{tabular}{c} 10,015 images from \\ the HAM10000 \\ dataset \\ \end{tabular} & Labeled images & Skin \\ \hline \begin{tabular}{c} Pressure ulcer \\ segmentation \\ \end{tabular} & Segmentation & \begin{tabular}{c} Digital \\ Camera \\ \end{tabular} & \begin{tabular}{c} 138 images from \\ Dongguk University \\ Ilsan Hospital \\ \end{tabular} & Labeled images & Skin \\ \hline \begin{tabular}{c} Chest X-ray \\ classification \\ \end{tabular} & \
\begin{tabular}{c} Object \\ detection and \\ classification \\ \end{tabular} & X-ray &
\begin{tabular}{c} 987 images from \\ public dataset \\ \end{tabular} & Labeled images & Organ \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overview of medical image analysis tasks used for experiment. channel of input images, are input images of the source task and target task, respectively. \(Y_{s}\in\mathbb{R}^{k}\), where \(k\) is the number of classes, is the output of the source task and \(Y_{t}\in\mathbb{R}^{k^{\prime}}\) is the output of the target task. \(f\) is an encoder of images for the source task, and \(f^{\prime}\) is an encoder for the target task, but the initial weight of \(f^{\prime}\) is the model weight trained with the proposed transfer learning method \(T\). \[ \begin{split} Y_{s}=&\,f(X_{s})\\ Y_{t}=&\,f^{\prime}(X_{t})\\ f(X)\xrightarrow{\intercal}&\,f^{\prime}(X)\end{split}  \tag{1}\]  
For the transfer learning method, we propose the four following methods: (1) transfer learning with similar images, (2) transfer learning with self-supervised learning, (3) transfer learning with RoIs, and (4) transfer learning with multi-task learning. We experimentally verify that the proposed transfer learning can be applied to the representative applications. Furthermore, optimal ensemble learning appropriate to each application is presented. The performance is improved by constructing an ensemble model appropriate for the applications.
## 3 Results  
### Radar and ADCP Comparisons  
In order to assess the correction effect on radar data, the radar data at the coordinates of 113.66\({}^{\circ}\) E, 22.2\({}^{\circ}\) N were initially compared with bottom-mounted ADCP measurements. Note that the above data were averaged to hourly data and smoothed using a four-hour running mean to eliminate very-high-frequency signals. The analysis, shown in Figures 3 and 4, indicated a notable resemblance in the variations between the bottom-mounted ADCP and radar data. Specifically, the correlation between radar and bottom-mounted ADCP exceeded 0.4 for the first five days, but decreased to 0.2665 on the tenth day, followed by a marginal recovery. The abrupt decline in radar data correlation during the period of 14-15 June in the graph could potentially be attributed to anomalies in the radar station or the interpolation process. Subsequently, as the radar data returned to normal, the correlation also demonstrated a slight recovery.  
Figure 2: Schematic representation of the LSTM memory cell and net structure applied in this study.
## 3 System Models
### Latency Models of a Client
#### 3.2.2 Uploading Latency  
The achievable data rate of client \(i\) can be estimated by  
\[r_{i}=b\log_{2}\left(1+\frac{pg_{i}}{N_{0}}\right), \tag{5}\]  
where \(b\) is the amount of bandwidth allocated to each participating client, \(p\) is the transmission power of the client, \(g_{i}\) is the channel gain from client \(i\) to the BS calculated, and \(N_{0}\) is the average background noise and inter-cell interference power density. We assume that the size of the local model is \(s\), and so the latency of client \(i\) in uploading its local model to the BS is  
\[t_{i}^{upload}=\frac{s}{r_{i}}=\frac{s}{b\log_{2}\left(1+\frac{pg_{i}}{N_{0}} \right)}. \tag{6}\]Latency-awarE Semi-Synchronous Client Selection and mOdel Aggregation for Federated learNing (LESSON)  
In contrast to synchronous FL, the proposed LESSON method aims to allow all the clients to participate in the whole learning process while avoiding the straggler problem. The basic idea of LESSON is to cluster the clients into different tiers based on their latency and the deadline. The clients in different tiers would train and upload their local models at different frequencies.
# Pitch Channel Control of a REMUS AUV with Input Saturation and Coupling Disturbances
## 5 Conclusions  
In this paper, a control strategy is put forward to deal with challenges in underwater vehicle control practice. Considering unmodeled uncertainties and coupling disturbances in underwater vehicle system, \(L_{1}\) adaptive control is employed as the control architecture to guarantee robustness and fast adaption. A modern compensator based on Riccati equation is combined with \(L_{1}\) adaptive control to deal with the transient dynamics caused by input nonlinearities. The proposed method is successfully applied to the pitch channel of REMUS vehicle model. REMUS model is a six DOF nonlinear dynamic model extracted from experimental data. The pitch channel control vehicle is systematically coupled with other degrees of freedom. In this paper, the performance of the proposed control method is studied by tracking the desired command in several scenarios. Results show that \(L_{1}\) adaptive control with Riccati based AW compensator can tackle input saturation in practice as well as adapt to model uncertainties, coupling disturbances and noise. Hence, the proposed control scheme could be considered as a candidate for control practice on the physical platform in the future.  
**Acknowledgments:** This work is supported by the National Natural Science Foundation of the National Research and Development of major scientific instruments (41427806), ROV System For Exploration and Sampling (DY125-21-Js-06) funded by the State Oceanic Administration People's Republic Of China and National High Technology Research and Development Program of China (863 Program, Grant No. 2012 AA092103). The authors would also like to express their appreciation to [PERSON] and [PERSON] for their great help on completing this work successfully.  
**Author Contributions:** [PERSON] put forward the original concept, proposed the control approach and wrote the article. [PERSON], [PERSON], and [PERSON] gave their valuable suggestions on research design. Besides, [PERSON], [PERSON], along with [PERSON], analyzed and discussed the simulation results.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Related Works  
Security protocols for M2M are E2E (End to End) communications; initiated from M2M applications installed at application and device domain layers.  
At high level, the architecture (application domain) uses various M2M/IoT communication protocols (CoAP, HTTP, SIP, MQTT, etc.). Generally, security solutions are based on classical security protocols in the internet like SSL/TLS, DTLS, IPSEC, and HIP (Host Identity Protocol).  
At low level (device layer) of M2M and IoT architectures, different security protocols have been proposed based on classical symmetric cryptographic schemes, or lightweight asymmetric ECDH (Elliptic Curve Diffie-Hellman Key Exchange) [4], or based on lightweight operations addition, subtraction, hash function and XOR [5; 6].  
A large number of lightweight cryptographic algorithms such as HIGHT, LEA, and PRESENT [7; 8] have been proposed in literature for resource-constrained devices like RFID tag, sensors, etc.