esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Metabolic Responses to Different Antibiotic Classes
### Quorum Sensing, Nutrient Sensing, and Intra-Microbial Cooperation  
Under antibiotic pressure, bacterial metabolism is regulated by quorum sensing (QS) systems, which integrate signals related to cell density and nutrient availability [10].  
Quorum sensing systems finely modulate gene expression to optimize survival in hostile environments, coordinating collective processes such as biofilm formation, secretion of virulence factors, and metabolic adaptation to nutrient availability [128].  
In _Pseudomonas aeruginosa_, activation of the Las and Rhl quorum sensing systems directly modulates the expression of efflux pumps and lipid biosynthesis in response to cell density and the presence of antibiotics [129].  
By integrating environmental signals through quorum sensing pathways, _P. aeruginosa_ finely tunes its metabolism and virulence expression, efficiently adapting to hostile conditions and promoting survival in microenvironments characterized by nutritional, immune, or antimicrobial stress [130].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Immunometabolite** & **Bacterial Sensor or Effect** & **Metabolic Response or Impact** & **References** \\ \hline Itaconate & Inhibits isocitrate lyase & Blocks glyoxylate cycle, alters carbon flux & [124] \\ \hline Nitric Oxide (NO) & Inhibits respiratory chain & Induces anaerobic and & [125] \\ \hline Lactic Acid & Environmental acidification & Stress signal influencing gene regulation & [123] \\ \hline Succinate & Signal molecule for inflammation & Supports anaerobic respiration and & [123; 124] \\  & and bacterial energy use & stress adaptation & [123; 124] \\ \hline Regulatory Pathways & GacS/GacA, PhoPQ & Coordinate metabolic and virulence & [126; 127] \\  & & responses to host signals & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Host-derived immunometabolites and bacterial metabolic responses.  
In parallel, nutrient sensing mediated by the PTS system and the global regulator CodY enables bacteria to discriminate between favorable and stressful environmental conditions, determining whether to activate growth programs or enter dormancy [131].  
Through the integration of intracellular metabolic signals and environmental stimuli, regulatory systems such as PTS and CodY finely modulate gene expression, enabling bacterial metabolic and transcriptional adaptation to dynamic and hostile microenvironments, thereby optimizing survival and persistence [131].
## 2 Materials and Methods  
### Overall Study Design  
As shown in Figure 1, a wearable device was developed in which a microcontroller unit (MCU) was used to collect six-axis data and quaternion data from two IMUs (one on the wrist band and the other on the upper arm near the elbow) and heart rate data. In addition to the IMU and heart rate module, the MCU in the wrist band is equipped with a real time clock (RTC) and a Bluetooth Low Energy (BLE). The RTC provides the present time for MCU, and BLE is used to transmit IMU six-axis (6 degrees of freedom, 6-DoF) data, quaternion, heart rate, and time through data packaging to the smart phone. By integrating the existing techniques of motion tracking [19] and posture recognition [12], a weight training quantization algorithm is devised for smartphone to convert motion data into prescription variables. The smartphone App can communicate with the MCU through BLE using pre-defined commands. The App then parses the data received through Bluetooth (BT data packet) into IMU 6-DoF data, quaternion, and heart rate. The IMU 6-DoF data are used for posture recognition based on a pre-trained machine learning model (ML model) installed on the phone. This ML model is trained offline on a PC by feeding recorded IMU 6-DoF data, which are prepared in csv files. Once the ML model is trained, it can be installed on the smart phone for posture recognition to create posture labels. The parsed quaternion is used for motion tracking to calculate arm orientation vectors and to find out the elbow and wrist positions that are then fed to the Unity's Animation System (avatar, virtual humanoid) [20]. The parsed heart rate and the motion of the 3D virtual humanoid are displayed on the screen of the developed smartphone App. The posture label with the positions of elbow and wrist is input to the exercise state analysis, and the exercise event stream (including exercise state, rest state, repetitions, rest time between sets, and exercise event) is obtained for the exercise event process. The exercise prescription variables are then computed, based on the input dumb bell weight and the exercise event stream, by the exercise event process, shown on the phone screen of the App, and stored in SQLite database. Not only transmitted by BLE, the IMU 6-DoF, quaternion, and heart rate data collected by MCU can also be transmitted by wire and stored as csv files. These csv files can be stored in a cloud or in a PC.  
Figure 1: System architecture.  
The proposed artificial intelligence weight training quantization algorithm converts the raw motion data into exercise prescription variables step by step based on mathematical and physical theories, as shown in Figure 2. This part has two procedures, one for posture recognition and one for motion tracking using IMU 6-axis data and quaternion data of the wrist and the arm to calculate the posture label type and the joint position of the arm. The outputs of the two procedures are fed first to the exercise state analysis for calculating repetitions, state, and event, and then to the exercise event process for calculating the exercise prescription variables.
# Image Segmentation Approaches for Weld Pool Monitoring during Robotic Arc Welding
## 3 State of the Art Methods for Laser Line Segmentation
### Combination Method  
In Reference [23], a combination approach was used to segment the laser lines. The combined image processing algorithms include a difference operation, a two-dimensional spline fitting enhancement operation, a gradient feature detection filter and the slope difference distribution-based threshold selection. In both [21; 23], the laser lines are segmented by the slope difference distribution-based threshold selection method that could be summarized as follows. Firstly, the gray-scales of the original image is rearranged in the interval from 1 to 255 and its normalized histogram distribution \(P(x)\) is computed. Secondly, the normalized histogram \(P(x)\) is smoothed by the fast Fourier transform (FFT) [25] based low pass filter with the bandwidth \(W\) = 10. Thirdly, two slopes, the right slope and the left slope, are computed for each point on the smoothed histogram distribution. The slope difference distribution is computed as the differences between the right slopes and their corresponding left slopes. In the slope difference distribution, the position where the valley with the maximum absolute value occurs is selected as the threshold. The slope difference distribution-based threshold selection method is critical in this application. For the comparison with state of the art image segmentation methods, please refer to the related research work [21], where this threshold selection method is compared with the state of the art method using the same type of images. The comparisons with state of the art image segmentation methods showed that the slope difference distribution based threshold selection method is significantly more accurate in segmenting some specific types of images, including the laser line images.
# A Systematic Mapping Study on Research in Anemia Assessment with Non-Invasive Devices
## 4 Results
### Human Body Region of Interest  
Figure 7 illustrates the distribution of the parts of the body on which the analysis is carried out. Sometimes studies address various topics, so few papers are classified in more than one class; there are also some papers about technologies exploitable for more than one type of device, so a specific body part could not be identified.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Device** & **Description** \\ \hline \multirow{2}{*}{Light reflection/absorbance based} & Use devices that indirectly measure hemoglobin levels using light \\  & reflection properties to distinguish oxyhemoglobin, \\  & and deoxyhemoglobin (pulse co-oximeters). \\ \hline Camera & Use a digital camera to take pictures or videos, to later extract data from it. \\ \hline Mini Invasive Device & Use devices that require a small drop of blood to evaluate HB. \\ \hline Smartphone & Use a smartphone as a measurement tool and/or a user-friendly service to monitor patients’ conditions. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Device types’ definitions.  
Figure 6: Distribution of papers by device for the assessment.  
Figure 7: Region of the body investigated in published papers.  
The graph in Figure 7 shows how the finger (120 papers) is the most analyzed body part, due to all the papers about devices and technologies that use to extract data from the fingertip capillaries, due to the massive use of co-oximeters which in most cases uses a finger clamp to extract the data; few camera and smartphone devices are also used to take pictures or small videos of the fingertip. Second, with a great gap, the conjunctiva (26 papers in total) comprehends papers that evaluate hemoglobin or anemia using the palpebral or fornical conjunctiva. Other parts of the body are involved in a small percentage of the papers (14 papers); these papers analyze data from fingernails, eye sclera, retina, hand palms, tongue, or lips.
# Analysis of Cryptographic Algorithms to Improve Cybersecurity in the Industrial Electrical Sector
## 1 Introduction
Since GOOSE messages have a 3 ms delivery time as a priority requirement, IEC 62351-1 specifies that encryption algorithms should not be used, as the processing times for encrypting GOOSE messages would be longer due to the limited computing capacity of intelligent electronic devices (IEDs). In addition, the standard does not specify message confidentiality as a fundamental requirement. However, GOOSE messages contain sensitive information related to the electricity market or energy administration, which would create a serious management problem if these messages were obtained and decrypted by attackers. Therefore, given this new perspective, GOOSE messages need to be encrypted to add the confidentiality requirement but still meet the 3 ms timing requirement. Based on the above, the main contribution of this article is to provide a comprehensive investigation of the ChaCha20 and Poly1305 algorithms, both individually and together as components of an AEAD algorithm, to achieve the confidentiality and integrity of GOOSE and R-GOOSE messages, which in turn meet the stringent requirements of the IEC61850 standard, thus establishing them as valid alternatives to the AES algorithm specified in the IEC62351-1 standard, an algorithm that has been thoroughly studied by various cryptanalyses, which is currently the only recommended algorithm in the standard. Through experimental validation, the paper shows its ability to meet the strict timing constraints of the IEC61850 standard for frame transmission, specifically in the context of the GOOSE and R-GOOSE protocols. As a result, this study positions these algorithms as credible alternatives to the existing ones described in the standard. The structure of this article unfolds as follows: Section 2 provides details of the GOOSE and R-GOOSE communication protocols, shedding light on the prevailing cybersecurity challenges within the IEC61850 standard. Section 3 delves into fundamental cybersecurityconcepts, examining notable attacks targeting the aforementioned protocols, and scrutinizing the cryptographic algorithms endorsed in the IEC62351 standard to fortify the sector against diverse threats. Lastly, Section 4 meticulously expounds on the algorithms proposed herein--ChaCha20 and Poly1305--alongside their experimental validation, either in isolation or in tandem as an AEAD algorithm. This section meticulously demonstrates their ability to adhere to the stringent time constraints mandated by the IEC61850 standard for transmitting GOOSE and R-GOOSE frames. Furthermore, it includes a comprehensive comparative analysis between the proposed algorithms and those stipulated in the standard.
###### Abstract  
Palladium-based carbon catalysts (Pd/C) can be potentially applied as an efficient catalyst for Suzuki-Miyaura and Mizoroki-Heck coupling reactions. Herein, a variety of catalysts of palladium on activated carbon were prepared by varying the content of 'Pd' via an in situ reduction method, using hydrogen as a reducing agent. The as-prepared catalysts (0.5 wt % Pd/C, 1 wt % Pd/C, 2 wt % Pd/C and 3 wt % Pd/C) were characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX) and Brunauer-Emmett-Teller (BET) analyses. The catalysts were tested as a coupling catalyst for Suzuki-Miyaura coupling reactions involving aryl halides and phenyl boronic acid. The optimization of the catalyst by varying the palladium content on the activated carbon yielded Pd/C catalysts with very high catalytic activity for Suzuki reactions of aryl halides and a Mizoroki-Heck cross-coupling reaction of 4-bromanoisol and acrylic acid in an aqueous medium. A high 'Pd' content and uniform 'Pd' impregnation significantly affected the activity of the catalysts. The catalytic activity of 3% Pd/C was found to make it a more efficient catalyst when compared with the other synthesized Pd/C catalysts. Furthermore, the catalyst reusability was also tested for Suzuki reactions by repeatedly performing the same reaction using the recovered catalyst. The 3% Pd/C catalyst displayed better reusability even after several reactions.  
palladium; carbon; catalyst; Suzuki-Miyaura coupling; Mizoroki-Heck
# Review on the Electrical Resistance/Conductivity of Carbon Fiber Reinforced Polymer
## 4 CFRP Conductivity Measurements and Techniques for Non-Destructive Testing Applications
### Methods to Avoid Delamination  
The electrical conductivity of CFRPs was generally increased in order to prevent delamination cracking [161]. In addition, resin-rich layers with elastomer particles were also used to toughen laminated CFRP [90]. However, the highly toughened CFRP showed the lower electrical conductivity than common laminated CFRP [86]. The electrical conductivity of the former is significantly orthotropic and easily lead to structural damages in case of lightning strike [85].
## 3 Multiple-Access Reservation Receiver Initiation MAC (MAR-RiMAC)
### RI-MAC Overview  
Receiver-Initiated MAC (RI-MAC) is an asynchronous duty-cycle MAC protocol for wireless sensor networks that aims to operate efficiently over a wide range of traffic loads. As the name suggests, in RI-MAC, the receiver initiates the data exchange. Accordingly, a node intending to send data stays active, silently waiting for its intended receiver to notify that it is awake and ready to receive data. Each node periodically wakes up based on its own schedule, and once carrier sensing the channel to ensure the medium is idle, it broadcasts a transmission invitation beacon, which is termed a \"Ready to Receive\" (RtR) beacon. This transmission-invitation-beacon frame contains, in addition to ordinary control fields (e.g., preamble, length), a source ID field (Src), which identifies the RtR's transmitter. A device waiting to transmit to the awakening device (receiver), upon receiving the RtR's handshake signal, starts its data transmission, which will be acknowledged by another beacon. The acknowledgment beacon sent by the receiver is another RtR, as, in addition to acknowledging the receipt of the data frame sent, also serves as a new RtR, inviting additional data frame transmissions to the same receiver. When no additional data frames are received, the device (receiver) goes to sleep. A basic overview of the operation of RI-MAC is depicted in Figure 1. Since when two or more devices are waiting to transmit to the same receiver, a collision will occur, accordingly, RI-MAC has devised a collision resolution mechanism. According to this mechanism, whenever a collision has occurred (the receiver has received a frame that it cannot decode), the receiver broadcasts an additional RtR in which it includes a contention window size that should be used by all contending devices such that each device should randomly select a backoff time within this window and attempt transmission only if the channel has remained idle throughout this interval. After each additional collision, the receiver generates additional RtRs in which it increases the contention window size incorporated within the RtRs (similarly to the binary exponential backoff mechanism). Note that when some of the transmitting devices are hidden from one another, the collision mechanism scheme can cause repeated collisions even when the contention window determined by the receiver is relatively large (Figure 1b).  
In the following subsection, we devise an enhancement for RI-MAC that permits multiple devices to notify their intended receiver of their intention to transmit to it simultaneously, which, in turn, can pool the backlogged devices, one by one.
# Domain Discrimination Expert Weighted Network for Multi-Source Carotid Artery Plaque Classification
## 4 Results
### Experimental Results
#### 4.2.1 Model Comparison
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Standards**} & \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**AC-B**} & \multicolumn{3}{c}{**AB-C**} & \multicolumn{3}{c}{**BC-A**} \\ \cline{3-11}  & & **Acc** & **Pre** & **Rec** & **Acc** & **Pre** & **Rec** & **Acc** & **Pre** & **Rec** \\ \hline Source-only \({}^{*}\) & Resnet-50 & 65.47 & 66.41 & 65.71 & 68.82 & 69.47 & 68.82 & 60.27 & 59.35 & 62.41 \\ \hline \multirow{3}{*}{Source-combine} & DANN [18] & 70.31 & 65.82 & 68.73 & 71.75 & 70.19 & 69.08 & 72.26 & 68.57 & 66.36 \\  & MCD [19] & 72.88 & 70.35 & 72.26 & 74.86 & 73.41 & 76.38 & 75.65 & 72.13 & 75.25 \\ \hline \multirow{6}{*}{Multi-source} & MFSAN [32] & 75.70 & 78.21 & 77.73 & 78.30 & 85.21 & 83.97 & 70.83 & 69.00 & 70.65 \\  & MDAN [29] & 72.72 & 72.16 & 74.90 & 79.33 & 83.11 & 80.37 & 67.30 & 70.98 & 70.93 \\ \cline{1-1}  & M3 SDA [22] & 75.32 & 76.46 & 77.98 & 81.62 & 82.67 & 82.98 & 70.26 & 69.82 & 70.45 \\ \cline{1-1}  & AMTLDC [30] & 74.37 & 79.33 & 78.54 & 80.20 & 84.33 & 81.27 & 70.19 & 70.20 & 71.78 \\ \cline{1-1}  & WUCF & 76.77 & 80.58 & 79.29 & 82.26 & 86.00 & 84.15 & 71.28 & 73.70 & 73.32 \\ \hline \hline \end{tabular} \({}^{*}\) Single-center fully supervised, direct testing in target center without domain adaptation, choosing optimal results.  
\end{table} 
Table 2: Comparison of the model with other methods. comparatively more suitable for addressing the classification issues of multi-source center carotid ultrasound images.
# Human Pose Detection for Robotic-Assisted and Rehabilitation Environments
## 3 Materials and Methods  
The architecture of the vision system is composed of two RGBD cameras (Microsoft Kinect Xbox One, also known as Kinect v2, Microsoft, Albuquerque, NM, USA) and a webcam connected to a computer network. Each Kinect is connected to a client computer, which estimates the user skeleton joint tracking through the Microsoft Kinect Software Development Kit (SDK). Microsoft released the Kinect sensor V2 in 2013, which incorporates a RGB camera with a resolution of 1920 \(\times\) 1080 pixels and a depth sensor with a resolution of 512 \(\times\) 424 pixels and a working range of 5-450 cm, a 70\({}^{\circ}\)\(\times\) 60\({}^{\circ}\) field of view, and a frame rate of 15-30 fps. Data from the sensor can be accessed using the Kinect for Windows SDK 2.0, which allows tracking up to 6 users simultaneously, each with 25 joints. For each joint, the three-dimensional position is provided, as well as the orientation as quaternions. The center of the IR camera lens represents the origin of the 3D coordinate system [32,33].  
The Microsoft SDK was designed for Windows platforms; therefore, rosserial is used to communicate between Windows platforms and Linux [34]. Three PCs are used for the system architecture. One of them works both as a client and server. The detailed hardware description is shown in Table 1. The RGB webcam is connected to a client PC equipped with a graphics card (GPU). Both the webcam and GPU are used by the OpenPose and Detectron2 methods for human pose estimation. The overall system topology is shown in Figure 1.
## 2 System Model of DBT-MC-FTN
### Dbt-Mc-FTN Receiver  
Assume that, in a DBT-MC-FTN transmission block, the time domain impulse response of a multipath fading channel is approximately constant. Then, at the receiver, the channel impulse response (CIR) vector can be denoted as \(\mathbf{h}=[h_{1},h_{2}...,h_{Q}]^{T}\). After CP removing, the received signal can be expressed as  
\[\mathbf{y}=\mathbf{g}\otimes\mathbf{h}+\mathbf{z}, \tag{6}\]  
where \(\mathbf{y}=[y_{1},y_{2},\cdots,y_{Q}]^{T}\) is the receiver signal vector, \(\mathbf{z}=[z_{1},z_{2},\cdots,z_{Q}]^{T}\) denotes the additive white Gaussian noise (AWGN) with zero mean and the variance of \(\sigma^{2}\), \(\otimes\) stands for the circular convolution.  
Because of the periodicity of the transmitted symbol, Equation (6) can be rewritten in the form of a circulant matrix as follows:  
\[\mathbf{y}=\mathbf{H}_{\mathbf{c}}\mathbf{\Phi}\mathbf{X}+\mathbf{z}=\mathbf{ \Theta}\mathbf{h}+\mathbf{z}, \tag{7}\]  
where \(\mathbf{H}_{\mathbf{c}}\) is a circulant matrix constructed by \(\mathbf{h}=[h_{1},h_{2}...,h_{Q}]^{T}\). \(\mathbf{\Psi}=\left[\begin{array}{cccc}\Phi_{1}\ \Phi_{Q}\ \cdots\ \Phi_{2}\\ \Phi_{2}\ \Phi_{1}\ \cdots\ \vdots\\ \vdots\ \vdots\ \ddots\ \Phi_{Q}\\ \Phi_{Q}\cdots\ \Phi_{2}\ \Phi_{1}\end{array}\right]\), \(\Phi_{k}\) is the \(k\)th row of \(\mathbf{\Phi}\). \(\mathbf{\Theta}\) is a \(Q\times Q\) circulant matrix whose first column vector is \(\mathbf{v}\mathbf{=}\mathbf{\Phi}\mathbf{x}\) and hence can be written by \[\mathbf{\Theta}=\left[\begin{array}{cccc}\Phi_{1}&\Phi_{Q}&\cdots&\Phi_{2}\\ \Phi_{2}&\Phi_{1}&\cdots&\vdots\\ \vdots&\vdots&\ddots&\Phi_{Q}\\ \Phi_{Q}&\cdots&\Phi_{2}&\Phi_{1}\end{array}\right]\left[\begin{array}{cccc} x&0&\cdots&0\\ 0&x&\cdots&0\\ \vdots&\vdots&\ddots&\vdots\\ 0&0&\cdots&x\end{array}\right]. \tag{8}\]  
After demodulation, the demodulated signal matrix \(\mathbf{R}=[\mathbf{r}_{0}\ \mathbf{r}_{1}\cdots\mathbf{r}_{M-1}]^{T}\) can be expressed as  
\[\mathbf{R}=\mathbf{\Phi}^{H}\mathbf{H}_{c}\mathbf{\Phi}\mathbf{X}+\mathbf{Z}_{\mathbf{ \Phi}^{H}}=\mathbf{H}_{eq}\mathbf{X}+\mathbf{Z}_{\mathbf{\Phi}^{H}}, \tag{9}\]  
where \(\mathbf{\Phi}^{H}\) is the conjugate transpose matrix of \(\mathbf{\Phi}\), and \(\mathbf{C}=\mathbf{\Phi}^{H}\mathbf{\Phi}\) is the correlation matrix regardless of the effect of the channel. \(\mathbf{Z}_{\mathbf{\Phi}^{H}}\) is the noise matrix contaminated by the demodulation matrix \(\mathbf{\Phi}^{H}\). \(\mathbf{H}_{eq}\) is the equivalent channel matrix containing the transceiver and the channel. In summary, as the inverse of the DBT-MC-FTN transmitter, the structure of the DBT-MC-FTN receiver can be depicted in Figure 1b.