esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Characterization Methods  
The microstructures of the samples were characterized using an X-ray diffractometer (XRD, DMAX-3C, Rigaku Corporation, Tokyo, Japan) with Cu K\(\alpha\) radiation, a scanning electron microscope (SEM, Verios 460L, FEI Company, Hillsboro, OR, USA), and a transmission electron microscope (TEM, JEM-4010, JEOL, Ltd., Akishima, Japan) operating at an acceleration voltage of 400 kV. Furthermore, X-ray photoelectron spectroscopy (XPS, PHI 5000 VersaProbe, ULVAC-PHI, Inc., Chigasaki, Japan) was utilized to investigate C1s core level of RGO in the NCTF. The room temperature charge transport properties in the samples were characterized by using a Hall measurement system (ResiTest 8300, Toyo Corporation, Tokyo, Japan) under a magnetic field of 0.57 T.
## 3 System Architecture of the IoT AP
### IoT AP Functionality  
Figure 1 depicts the application scenario of the IoT AP for smart life where three network types are considered. The first one is Ethernet, which allows an AP connecting to Internet; the second one is Wi-Fi, which provides Internet connection for handheld devices via an AP; the third one is ZigBee, which is characterized by low-power and commonly embedded in sensors for environmental monitoring or event detection. However, it is unable to allow various network protocols to communicate with each other by using a traditional access point. Therefore, such heterogeneous network problem must be solved for building smart life.  
To allow each device to communicate with each other within a heterogeneous network, this paper proposes an IoT AP that provides three main functions. First, it provides Internet connection and data conversion services between Wi-Fi and ZigBee networks. Then, it establishes a ZigBee network that allows home devices to communicate with each other by using the ZigBee wireless protocol. Finally, it provides a user interface control panel so that users can connect to IoT AP through the Internet to get the status of each ZigBee device at home and control them remotely.  
ZigBee is a low-power wireless networking protocol with short-range transmissions. ZigBee Alliance develops this specification to allow developers carrying out their products, among which all of them are compatible. In addition, ZigBee provides several benefits once it is applied to IoT applications, including low power, low cost, low complexity, reliability, safety, and the support of a large number of network nodes. Based on the original Wi-Fi AP, this paper proposes the design and implementation of an IoT AP through the firmware update. The existing wireless AP is integrated with ZigBee wireless protocol to  
Figure 1: An Internet of Things (IoT) application: Smart Life scenario.  
develop an IoT AP that provides ZigBee wireless communications, Wi-Fi wireless communications and Internet access capabilities.  
To prevent illegal users manipulating the ZigBee devices at home, this paper further applies the universal plug and play (UPnP) technology, which constrains users to access the devices only through a LAN. To privately and remotely control home appliances, remote users can apply the virtual private network (VPN) technology to establish a private network connection for the IoT AP to access the ZigBee devices. [PERSON] _et al._[35] had proposed the VPN access for UPnP technology. Therefore, this paper does not further discuss VPN technology.
# Low-Cost Digitalization Solution through Scalable IIoT Prototypes
## 4 Case Study
### Determining the Areas for Improvement  
The observation of pressure fluctuations in the plateau corresponding to the distance between clamps led the authors to rethink the clamping mechanism of the tubes. They modified the standard clamping system and were able to reduce the pressure deviation significantly. Figure 9 shows the deviation of the plateau pressure from its average value in percent for two cuts with the 102 mm tool before and after the modification of the clamping system.  
While the pressure deviated by more than 20% with the standard system, the value can be kept under 5% through the undertaken modifications. A more regular cut results in a better surface finish and increases the lifetime of the tool. Modifications like these, which were only made possible through the IIoT system, enable the factory to produce products of higher quality at lower manufacturing costs. In addition to the purchase of a broaching machine itself, the biggest investment is the manufacturing of new broaching tools. As these wear out, it is inevitable. Enabling predictive maintenance through data collection and analysis allows for the determination of the ideal point in time for the resharpening of a tool, and thereby avoids overstressing and increases the tool's lifetime. The tool presented in Figure 2 already outperforms previous tools in terms of efficiency by far. However, it is still manufactured from one solid piece of steel which requires many machining hours on a very expensive five-axis CNC mill. Therefore, the design had been further upgraded by using a modular architecture with a hollow body carrying ring-shaped blades. The body only has to be manufactured once, and the blades can easily be resharpened and cost-effectively reproduced when completely worn out. The tool has already been successfully tested.
# Formation Control Technology of Fixed-Wing UAV Swarm Based on Distributed Ad Hoc Network
## 3 Waypoint-Based Formation-Switching Method
### Consensus-Based Waypoints Planning  
#### 3.2.1 Formation Waypoints  
The UAV speed studied in this paper was constant, and the UAV swarm formation was controlled based on the route. Therefore, the consistency control protocol in Equation (4) needed to be discretized. First, the communication time of the UAV swarm was discretized, then the position status of each UAV was updated in real time according to the difference equation, and the discrete consistency control protocol can be given by:  
\[\mathbf{X}_{i}[k+1]=\mathbf{X}_{i}[k]+\Delta\mathbf{X}_{i}[k]+\mathbf{D} \tag{5}\]\[\Delta\mathbf{X}_{i}[k]=\frac{\sum\limits_{j=1}^{m}\left(a_{ij}[k]((\mathbf{X}_{j}[k]- \mathbf{X}_{i}[k])-\mathbf{X}_{jiref})\right)}{\sum\limits_{j=1}^{m}a_{ij}[k]} \tag{6}\]  
where \(k\) represents a communication event, which is a formation process of the UAV swarm formation waypoint; \(\mathbf{D}\) is the distance required to switch the formation, and is selected according to actual needs; \(\mathbf{X}_{j}[k]\) and \(\mathbf{X}_{i}[k]\) are the position status of the UAV\({}_{j}\) and UAV\({}_{i}\) at time \(k\), also called formation waypoint; \(a_{ij}[k]\in R\times R\) is the adjacency weight matrix of the communication topology, and its elements are defined as:  
\[a_{ij}=\left\{\begin{array}{ll}1&\left(v_{j},v_{i}\right)\in E\\ 0&\left(v_{j},v_{i}\right)\
otin E\end{array}\right. \tag{7}\]  
The undirected graph in this article did not allow self-loops, so \(a_{ij}=0\).  
During the flight, UAV\({}_{i}\) established a communication connection with other UAVs in the ad hoc network, forming a formation waypoint \(\mathbf{X}_{i}[k]\) within each communication event \(k\) according to Equations (5) and (6).  
According to the consistency theory, it can be proved that for any UAV\({}_{i}\) with an initial value \(\mathbf{X}_{i}[0]\in R^{P}\), the time-varying communication topology union is fully connected in a formation transformation [27], namely \(\sum\limits_{k=0}^{Nk}a_{ij}[k]>0\) (\(i\
eq j\)), when \(k\to Nk\), there is \(\left|\Delta\mathbf{X}_{ji}[k]-\Delta\mathbf{X}_{jiref}[k]\right|\to 0\); that is, the relative distance between the UAV swarm reached the desired value, and the formation switch was realized. If the initial state of the communication topology \(a_{ij}[0]\) was full connectivity; namely, the communication was in the normal state, the formation fly could be achieved as the expected formation when \(k=0\).
###### Abstract  
Diabetic foot ulcers (DFUs) represent a significant challenge in managing diabetes, leading to higher patient complications and increased healthcare costs. Traditional approaches, such as manual wound assessment and diagnostic tool usage, often require significant resources, including skilled clinicians, specialized equipment, and extensive time. Artificial intelligence (AI) and generative AI offer promising solutions for improving DFU management. This study systematically reviews the role of AI in DFU classification, prediction, segmentation, and detection. Furthermore, it highlights the role of generative AI in overcoming data scarcity and potential of AI-based smartphone applications for remote monitoring and diagnosis. **Methods**: A systematic literature review was conducted following the PRISMA guidelines. Relevant studies published between 2020 and 2025 were identified from databases including PubMed, IEEE Xplore, Scopus, and Web of Science. The review focused on AI and generative AI applications in DFU and excluded non-DFU-related medical imaging articles. **Results**: This study indicates that AI-powered models have significantly improved DFU classification accuracy, early detection, and predictive modeling. Generative AI techniques, such as GANs and diffusion models, have demonstrated potential in addressing dataset limitations by generating synthetic DFU images. Additionally, AI-powered smartphone applications provide cost-effective solutions for DFU monitoring, potentially improving diagnosis. **Conclusions**: AI and generative AI are transforming DFU management by enhancing diagnostic accuracy and predictive capabilities. Future research should prioritize explainable AI frameworks and diverse datasets for AI-driven healthcare solutions to facilitate broader clinical adoption.  
diabetic foot ulcers; artificial intelligence; machine learning; generative AI; mobile applications
## 3 Experimental Scheme
### Measurement Scheme  
In order to measure and calculate the state vector of the control system, a number of displacement, velocity, acceleration and force sensors were arranged on the experimental model. three acceleration sensors were arranged at each floor level to measure the vibration response of each layer. The three sensors (Figure 8c) on each floor were 4381 V piezoelectric acceleration sensors produced by the B&K Company of Denmark. One of them measured the acceleration response directly, and the other two obtained the absolute displacement and velocity response values by integrating the acceleration values. When the absolute reaction values of adjacent layers were subtracted, the relative reaction values between layers can be obtained. The purpose of using three acceleration sensors to work at the same time was to reduce the on-line calculation time and the real-time control error. The quantity of each type sensor is one in every floor. The linear variable differential transformer (LVDT) displacement sensor was made in Shenzhen China, with a range of \(\pm\)64 mm, sensibility of 10%, and sampling frequency of more than 1000 Hz. It was arranged on the isolation layer, the bottom layer and the top layer for measuring the story drift response of the structure. In order to measure the actual performance of the MR damper, a force sensor with a measuring range of two tons was set up, which was directly connected to the damper and was arranged in the isolation layer. The described sensor arrangement has been shown in Figure 8.  
Figure 8: Sensors arrangement. (**a**) linear variable differential transformer (LVDT) displacement sensor in isolation layer; (**b**) LVDT displacement sensor in the bottom layer; (**c**) three acceleration sensors; (**d**) magnetorheological (MR) damper in isolation layer.
## 3 Results and Discussion
### Characterization of Biochar
#### 3.4.1 SEM-EDS Analysis
As the temperature rises, the C element content decreases, while the Si element content experiences a constant increase. The content of the O element remains relatively stable. Biochar produced from MG has more pores due to the uniform release of the volatile matter across the whole particle [  
-pore diameter reaches its maximum at 900 \({}^{\circ}\)C, measuring 4.38 nm. Biomass primarily comprises lignin, cellulose, and hemicellulose [55]. Organic compounds with carboxyl, hydroxyl, and carbonyl groups could react with the gasification agent differently at high temperatures. Cellulose and hemicellulose have higher O/C ratios than lignin, so they are gasified faster than lignin, producing more CO and less H\({}_{2}\)[56]. These reactions may involve the breaking of C=O bonds and C-H bonds and the release of water vapor. Thus, the char generates a portion of structurally undeveloped pores, changing the average pore size [55; 57; 58]. Additionally, during biomass gasification, the carbon on the outer and inner surfaces within the pores interacts with CO\({}_{2}\), forming more pores. When the gasification temperature surpasses a specific limit, the pre-existing micropores break down, resulting in a substantial increase in the pore size or even the rupture of the pore wall. As a result, the number of micropores decreases while the number of mesopores and macropores increase. Hence, when the oak chars are heated above 800 \({}^{\circ}\)C, a decrease occurs in both specific surface area and pore volume, and an increase appears in pore diameter. The surface area and pore volume of char from oak EG follow a pattern of initial increase and subsequent decrease, with lower values than before. The highest specific surface area and total pore volume of the corn stalk char from MG are achieved at 800 \({}^{\circ}\)C, measuring 525.87 m\({}^{2}\) g\({}^{-1}\) and 0.62 cm\({}^{3}\) g\({}^{-1}\), respectively. The maximum average pore diameter of 4.18 nm is obtained at 900 \({}^{\circ}\)C. The highest specific surface area, total pore volume, and average pore diameter from corn stalk EG are achieved at 900 \({}^{\circ}\)C, measuring 502.52 m\({}^{2}\) g\({}^{-1}\), 0.51 cm\({}^{3}\) g\({}^{-1}\), and 3.53 cm\({}^{3}\) g\({}^{-1}\), respectively. Microwave heating makes the internal temperature of biomass higher than the external one, which is more conducive to the formation of a rich microporous structure of biomass. Generally, the biochar from MG exhibits a greater specific surface area and total pore volume than biomass char derived from EG. The results obtained are consistent with the previous articles [59; 60].
# Real-Time, Model-Agnostic and User-Driven Counterfactual Explanations Using Autoencoders
## 4 Experimental Framework
### IM1 and IM2 Metrics  
To assess the interpretability of the counterfactuals generated with each method, two metrics proposed by [PERSON] et al. (2010), IM1 and IM2, have been used. On the one hand, IM1 measures the ratio between the reconstruction error of the counterfactual \(\mathbf{x}_{cf}\) using an AE specifically trained with instances of the counterfactual class (AE\({}_{\mathbf{y}_{cf}}\)) and using another AE trained with instances of the original class (AE\({}_{\mathbf{y}}\)).  
\[\text{IM1}(\text{AE}_{\mathbf{y}_{cf}},\text{AE}_{\mathbf{y}},\mathbf{x}_{cf} ):=\frac{\left\|\mathbf{x}_{cf}-\text{AE}_{\mathbf{y}_{cf}}(\mathbf{x}_{cf}) \right\|_{2}^{2}}{\left\|\mathbf{x}_{cf}-\text{AE}_{\mathbf{y}}(\mathbf{x}_{cf })\right\|_{2}^{2}+\epsilon} \tag{16}\]  
A smaller value of IM1 means that the counterfactual instance is easier to reconstruct with the AE trained only with instances of the class \(\mathbf{y}_{cf}\) than with the AE trained only with instances of the original class \(t_{0}\). This means that the counterfactual instance is closer to the data manifold of the \(\mathbf{y}_{cf}\) class instances than to those belonging to the original class \(t_{0}\). On the other hand, IM2 measures how close the counterfactual instance is to the data manifold. To do so, it compares the reconstructions of the counterfactual instances when using an AE trained with instances of all classes (AE) and the AE trained with instances of the counterfactual class (AE\({}_{\mathbf{y}_{cf}}\)). To make the metric comparable across all classes, IM2 is scaled with the \(L_{1}\) norm of \(\mathbf{x}_{cf}\).  
\[\text{IM2}(\text{AE}_{\mathbf{y}_{cf}},\text{AE},\mathbf{x}_{\text{cf}}):= \frac{\left\|\text{AE}_{\mathbf{y}_{cf}}(\mathbf{x}_{cf})-\text{AE}(\mathbf{ x}_{cf})\right\|_{2}^{2}}{\left\|\mathbf{x}_{cf}\right\|_{1}+\epsilon} \tag{17}\]  
When the reconstruction of \(\mathbf{x}_{cf}\) by AE and by AE\({}_{\mathbf{y}_{cf}}\) is similar, IM2 is low. A lower value of IM2 makes the counterfactual instance more interpretable, because the data distribution of the counterfactual class \(\mathbf{y}_{cf}\) describes \(\mathbf{x}_{cf}\) equally well as the distribution over all classes.
# Deep Learning Applied to Scenario Classification for Lane-Keep-Assist Systems
## 3 Time Series Classification Using Convolutional Neural Networks (CNN)
### Dilated Convolutions  
One drawback of ordinary CNNs is that, in order to achieve a wide receptive field, the networks need to be very deep. For example, if we have a 1D time series input and network with two convolutional layers, each with a \(1\times 3\) kernel and stride \((1,1)\) (the kernel moves by 1 feature each step), the receptive field of the second layer will be 5. In other words, a perceptron in the second layer will be able to \"perceive\" 5 values from the input. In general, the receptive field grows linearly with the depth of the network and is given as:  
\[R_{i}=R_{i-1}+\prod_{j=1}^{i}s_{j}\cdot(k-1) \tag{2}\]  
where \(R_{i-1}\) and \(R_{i}\) are the receptive fields of the previous and current layer, \(s_{j}\) is the product over all previous strides and \(k\) is the kernel size.  
However, when working with time series data it is often necessary to consider a very wide view of the input to make adequate predictions. What if we could increase the receptive field of the network without adding more layers and increasing the size of the network? The solution is to use dilated convolutions [31], as shown in Figure 2. The main idea is that during the convolution the kernel is not considering every adjacent input from the previous layer but instead there is some fixed dilation step \(d_{i}\) between them. With this approach, it is possible to achieve an exponentially increasing receptive field formulated as:  
\[R_{i}=R_{i-1}+d_{i}\cdot\prod_{j=1}^{i}s_{j}\cdot(k-1) \tag{3}\]  
It can be seen that the receptive field of the ordinary CNN (2) is actually a special case of the dilated convolutional network (3) where the dilation factor \(d_{i}\) equals 1. In practice, it is common to increase the dilation factor \(d_{i}\) exponentially for each layer \(i\), i.e., (\(d_{i}=2^{i}\)).
## Materials and Methods  
### Materials and Chemicals  
PSf (Solvay Korea, Seoul, Korea) and PVDF (Solvay Korea, Seoul, Korea) were used as the basis polymer materials of the membrane, and \(N\)-methyl-2-pyrrolidone (NMP) (Duksan Chemical, Yongin, Korea) was employed as the solvent for preparing the cast solutions. Graphenes, GO and rGO, were used as the blending materials with PSf and PVDF. For the graphenes, natural graphite powder (50 \(\upmu\)m) was obtained from Bay Carbon, and potassium permanganate (KMnO\({}_{4}\)), sulfuric acid (H\({}_{2}\)SO\({}_{4}\), 98%), hydrogen peroxide (H\({}_{2}\)O\({}_{2}\), 30%), and hydrazine monohydrate (N\({}_{2}\)H\({}_{4}\)-H\({}_{2}\)O, 80%) were purchased from Sigma Aldrich (St. Louis, MO, USA). To conduct the membrane fouling and separation performance test, kaolin (powder), sodium alginate, the sodium hypochlorite solution (NaOCI), and sodium hydroxide (NaOH, anhydrous, 97%) were purchased from Sigma Aldrich. Distilled-deionized (DDI) water was used in all experiments.
## 3 Methods
### Model Establishment
The mesh solution results, as shown in Figure 5, indicate that when the number of mesh elements increases to 32,958, further increases in mesh elements do not significantly affect the solution results. The time step solution results, as shown in Figure 6, demonstrate that when the time step is 0.1 s, further reduction in the time step does not result in significant changes in the simulation outcomes. Therefore, considering both factors, the final selection for the number of nodes and time step is 32,958 and 0.1 s, respectively. Figure 4: Mesh configuration: (**a**) tank grid; (**b**) orifice plate obstacle grid; (**c**) porous obstacle grids. Figure 5: Verification of time step irrelevance. * Reynolds Number: \[\text{Re}=\frac{q}{\epsilon}\] (1)
* Froude Number: \[Fr=\frac{q}{\sqrt{Gh^{3}(\rho_{in}-\rho_{a})}}\] (2)  
Jet disturbances are the primary cause of mixing between water layers inside the storage tank. During both charging and discharging, the degree of disturbance at the outlet is mainly determined by the Reynolds number. Once the working fluid enters the storage tank, whether temperature stratification exists in the internal mixing region of the tank is primarily dependent on the Froude number [28]. The Froude number is a dimensionless number that expresses the ratio of the inertial force of a fluid to the force of gravity. When \(Fr\leq 1\), the buoyancy force of the working fluid is greater than the gravitational force, resulting in less mixing between the hot and cold working fluids and more pronounced stratification. When \(Fr>1\), the gravitational force of the working fluid is greater than the buoyancy force, leading to significant mixing between the hot and cold working fluids, which is detrimental to stratification [29].
# Ensemble Classifiers for Network Intrusion Detection Using a Novel Network Attack Dataset +
## 4 GTCS Dataset Collection
### Data Collection and Feature Extraction
\begin{table}
\begin{tabular}{c l} \hline \hline
**Feature Name** & \multicolumn{1}{c}{**Description**} \\ \hline \multirow{2}{*}{**Flow ID**} & ID which presents the unique ID calculated from the 5-tuple, i.e., Src IP, Dst IP, Src \\  & Port, Dst Port, and protocol number \\ \hline \multirow{2}{*}{**Src IP**} & IP address of the machine from which the traffic started \\ \hline \multirow{2}{*}{**Src Port**} & IP address of destination machine \\ \hline \multirow{2}{*}{**Dst IP**} & IP address of destination machine \\ \hline \multirow{2}{*}{**Dest Port**} & Destination port number \\ \hline \multirow{2}{*}{**Protocol number**} & Number of the transaction protocol \\ \hline \multirow{2}{*}{**Timestamp**} & Date and time of day when the flow occurred \\ \hline \multirow{2}{*}{**Flow Duration**} & Total duration of flow \\ \hline \multirow{2}{*}{**Tot Fwd/Bwd Pkts**} & Total packets in forward/backward direction \\ \hline \multirow{2}{*}{**TotLen Fwd/Bwd Pkts**} & Total size of packet in forward/backward direction \\ \hline \multirow{2}{*}{**Fwd/Bwd Pkt Len**} & Size of packet in forward/backward direction \\ \hline \multirow{2}{*}{**Flow Byts/Packets**} & Rate of flow of bytes/packets (i.e., number of bytes/packets transferred each second) \\ \hline \multirow{2}{*}{**Flow IAT**} & Time between two packets sent in forward/backward direction \\ \hline \multirow{2}{*}{**Fwd/Bwd IAT**} & Time between two packets sent in forward/backward direction \\ \hline \multirow{2}{*}{**Fwd/Bwd PSH**} & Number of times that PSH flag was set to packets moving in either forward/backward direction \\ \hline \multirow{2}{*}{**Fwd/Bwd URG**} & Number of times that URG flag was set to packets moving in either forward/backward direction \\ \hline \multirow{2}{*}{**Active**} & How long a flow was active before becoming idle \\ \hline \multirow{2}{*}{**Idle**} & How long a flow was idle before becoming active \\ \hline \multirow{2}{*}{**Fwd/Bwd Pkts/s**} & Number of transmitted packets per second in forward/backward direction \\ \hline \multirow{2}{*}{**Fwd/Bwd Byts/s**} & Number of transmitted bytes per second in forward/backward direction \\ \hline \multirow{2}{*}{**Label**} & Indicates whether traffic is malicious \\ \hline \hline \end{tabular}
\end{table} 
Table 1: GTCS Dataset extracted features.