esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of Edge-IoMT Network Architecture with Weight-Based Scheduling
## 4 Implementation and Performance Evaluation  
### Experimental Settings  
The experimental field is a single ward, which contains four nodes and a relay node. Through the cluster approach mentioned in Section 3.3.1, the nodes are grouped into two clusters, as shown in Figure 17, where inter-cluster 1 contains two nodes with level-1 and level-2, respectively. And there are two nodes with level-2 and level-3 in inter-cluster 2. Each node schedules the physiological signals according to the method mentioned in Section 3.4 and the direction of the transmission flow is from the sensor nodes to the base station via CHs, inter-CHs, and relays. Considering the strengths of the raspberry pi single-board computer (e.g., its small size, powerful expansion capabilities, and multiple general-purpose input/output pins, supporting Bluetooth and Wi-Fi wireless communication), in this work we use the raspberry pi as a CH or an inter-CH for collecting physiological signals and performing data compression. Moreover, for large amounts of data decompression and analysis, we choose Jetson Nano as a relay node for edge-based applications.  
Figure 16: WPQ_Timestamp_Priority.  
Figure 15: WPQ_Timestamp_Priority_Empty.  
During a network initialization phase, each CH with raspberry pi scans the device to estimate how many adjacent CHs it has and then broadcasts a message of its scheduling priority (1-3) with a random waiting time. At the beginning, the number of inter-cluster members is zero. Each CH updates their neighbor information (i.e., a counter specifying how many neighbors it has detected) and decrease the random waiting time based on each new priority message received. Note that the nodes that hear many neighboring CHs are good candidates for initiating a new inter-cluster; those with few neighbors should choose to wait. When the timer of a CH expires, the CH becomes an inter-CH and broadcasts an address to its neighboring CHs.  
In contrast, when receiving a message with the inter-CH address, the CH joins the inter-cluster and becomes an inter-cluster member. Figure 18 shows the conceptual communication cycles and transmission slots in the proposed network architecture. As the clustering scheme mentioned in Section 3.3, a communication cycle assigns transmission slots among the base station and relay nodes. TDMA is used to schedule the data to transmit from CHs to inter-CHs and from inter-CHs to relay. The scheduling method is setting the high-level CH to transmit more frequently, and the inter-CH will fusion the whole data then transmit to relay. In Figure 17, inter-cluster 1 with level-1 and level-2 CHs, node 1 (i.e., a CH) in the inter-cluster will transmit the data to node 2 (i.e., an inter-CH). Then, the inter-CH will transmit the fusion data, which includes its data to the relay, as shown in Figure 18.
## 3 Results and Discussion
### Porous Carbons Characterization
#### 3.1.5 XPS Analysis  
To get further insight into the surface chemistry of the carbons, XPS analyses were performed. Two distinct peaks (C 1s and O 1s) were observed in the XPS survey scan of the ACK and CC carbons; therefore, the C 1s and O 1s XPS detailed regions were acquired (Figure S4 of the Supplementary Materials), and the corresponding data are presented in Table 4.  
The C 1s XPS regions were fitted with six peaks. The most intense peak on both carbons was positioned at 284.8 eV, attributed to a mixture of aromatic and aliphatic carbon. The peaks centered at around 286.2 eV were assigned to C-O of the ether or alcohol groups, this functional group being dominant in both carbons. The peaks appearing between 287 and 289 eV corresponded to carbon in the carbonyl (C=O) and carboxyl (-COOH)  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**CC** & **ACK** & **CC\({}^{\star}\)** & **ACK** & **Assignment [39]** \\ \hline \multicolumn{5}{c}{**Binding Energy (eV)**} & \multicolumn{3}{c}{**Atomic Concentration \%**} \\ \hline
284.8 & 284.8 & 66.5 & 55.0 & C\({}_{\text{acrom}}\) + C\({}_{\text{aliph}}\) \\
286.2 & 286.2 & 11.5 & 15.9 & C-O \\
287.4 & 287.6 & 6.3 & 7.8 & C=O \\
288.9 & 289.0 & 3.8 & 4.4 & O-C=O \\
290.2 & 290.5 & 3.1 & 3.1 & \(\pi\)\(\rightarrow\)\(\pi^{\star}\) \\
291.7 & 292.1 & 2.6 & 1.7 & C=O\({}_{\text{acrom}}\) \\
530.8 & 531.0 & 0.8 & 1.7 & C=O\({}_{\text{acrom}}\) \\
532.1 & 532.4 & 1.8 & 3.7 & C=O\({}_{\text{alif}}\) \\
533.2 & 533.8 & 2.0 & 2.7 & C-O \\
534.6 & 535.4 & 0.9 & 1.2 & Isolated H\({}_{2}\)O \\
536.0 & 537.0 & 0.7 & 0.7 & H\({}_{2}\)O \\ \hline \hline \multicolumn{5}{c}{**Atomic ratio**} & 14.1 & 8.3 & C/O \\ \multicolumn{5}{c}{6.0} & 5.5 & \(\pi\)\(\rightarrow\)\(\pi^{\star}\)/Ctotal \\ \hline \hline \end{tabular} \({}^{\text{a}}\)[29].  
\end{table}
Table 4: Binding energy values (eV) for C 1s and O 1s fitted peaks, atomic concentrations %, and atomic ratios.  
Figure 2: FTIR spectra of char and the ACK and CC porous carbons.  
functional groups. The region between 290 and 292 eV corresponded to energy losses due to \(\pi\)-\(\pi^{\bullet}\) electronic excitations typical of aromatic systems. Meanwhile, the O 1s region was peak-fitted with five peaks with binding energies from 530 to 536 eV, confirming the presence of the oxidized carbonaceous species and some water.  
From Table 4, through the C/O ratio, it is possible to observe that ACK carbon presented more oxygenated surface functional groups than commercial carbon.
###### Abstract  
This study presents the formulation and verification of a novel online adaptive reconfigurable learning control algorithm (RLCA) for improved longitudinal motion control and disturbance compensation in Unmanned Aerial Vehicles (UAVs). The proposed algorithm is formulated to track the optimal trajectory yielded by the baseline Linear Quadratic Integral (LQI) controller. However, it also leverages reconfigurable dissipative and anti-dissipative actions to enhance adaptability under varying system dynamics. The anti-dissipative actor delivers an aggressive control effort to compensate for large errors, while the dissipative actor minimizes control energy expenditure under low error conditions to improve the control economy. The dissipative and anti-dissipative actors are augmented with state-error-driven hyperbolic scaling functions, which autonomously reconfigure the associated learning gains to mitigate disturbances and uncertainties, ensuring superior performance metrics such as tracking precision and disturbance rejection. By integrating the reconfigurable dissipative and anti-dissipative actions in its formulation, the proposed RLCA adaptively steers the control trajectory as the state conditions vary. The enhanced performance of the proposed RLCA in controlling the longitudinal motion of a small UAV model is validated via customized MATLAB simulations. The simulation results demonstrate the proposed control algorithm's efficacy in achieving rapid error convergence, disturbance rejection, and seamless adaptation to dynamic variations, as compared to the baseline LQI controller.  
unmanned aerial vehicle; adaptive control; reconfigurable learning algorithm; longitudinal motion; disturbance rejection 20251818  
disturbances is a crucial issue among the many difficulties encountered when working with UAVs [7]. When altitude, velocity, and pitch angle change, the UAV moves along the vertical axis, which is known as longitudinal motion [6]. An effective longitudinal motion is necessary to guarantee a steady flight, precise maneuvering, and performing critical tasks like takeoff, landing, and following a desired flight path, making it a fundamental aspect of UAV operation and control [8]. Stabilizing the system is crucial to ensuring a smooth operation because UAVs operate in a variety of environments that are subject to parametric disruptions [9]. Robust and agile control principles are necessary during the longitudinal motion of the UAV to preserve flight stability under the influence of external disturbances caused by wind gusts, imprecise sensor measurements, and modeling inaccuracies that further hinder their flight control [10].
## 2 Materials and Methods
### EO Composition Analysis
#### 2.3.2 Gas Chromatography-Mass Spectrometry (GC-MS)  
GC-MS analyses were carried out with a Perkin-Elmer Clarus 600 gas chromatograph hyphenated with a Perkin-Elmer Turbomass Clarus 600T mass spectrometer (software version 5.4, PerkinElmer, Shelton, CT, USA), using a DB-1 column as described above. The GC-MS settings were as follows: injector and oven temperatures were as above; transfer line and ion source temperatures, 280 \({}^{\circ}\)C and 220 \({}^{\circ}\)C, respectively; carrier gas (helium) at a linear velocity of 30 cm/s; split ratio, 1:40; electron impact (EI) mode at 70 eV; ionization current of 60 \(\upmu\)A; mass scan range of 40-300 amu; and scan time of 1 s. The identity of the EO components was assigned by comparison of their retention indices, calculated in accordance with ISO 7609 [42], relative to C\({}_{9}\)-C\({}_{21}\)_n_-alkane indices, and GC-MS spectra with corresponding data of (1) components of reference EOs--_Thymus caespititius_[43], _L. azorica_[44], _Coriandrum sativum_, _Satureja montana_, _Santolina chamanacyparissus_, _Thymus vulgaris_[45], and _Monizia edulis_[46]; (2) laboratory-synthesized components following the methods described by [PERSON] et al. [45]; and (3) commercially available standards from a home-made library.
## 2 Half-Vehicle Seat System Integrated with NESIs
\begin{array}{cccccccc}c_{11}&c_{12}&-c_{f}&-c_{r}&-c_{a}&0&-c _{bf}&-c_{br}\\ c_{21}&c_{22}&c_{f}l_{f}&-c_{r}l_{r}&c_{a}l_{a}&0&c_{bf}l_{f}&-c_{br}l_{r}\\ -c_{f}&c_{f}l_{f}&c_{f}&0&0&0&0&0\\ -c_{r}&-c_{r}l_{r}&0&c_{r}&0&0&0&0\\ -c_{a}&c_{a}l_{a}&0&0&c_{a}+c_{ba}&-c_{ba}&0&0\\ 0&0&0&0&-c_{ba}&c_{ba}&0&0\\ -c_{bf}&c_{bf}l_{f}&0&0&0&0&c_{bf}&0\\ -c_{br}&-c_{br}l_{r}&0&0&0&0&0&0&c_{br}\end{array} \right)\]\[\mathbf{K}=\left( \begin{array}{cccccccc}k_{11}&k_{12}&-k_{f}&-k_{r}&-k_{a}&0&0&0 \\ k_{21}&k_{22}&k_{f}l_{f}&-k_{r}l_{r}&k_{al}a&0&0&0\\ -k_{f}&k_{f}l_{f}&k_{f}+k_{tf}&0&0&0&0&0\\ -k_{r}&-k_{r}l_{r}&0&k_{r}+k_{tr}&0&0&0&0\\ -k_{a}&k_{al}a&0&0&k_{a}&0&0&0\\ 0&0&0&0&0&0&0\\ 0&0&0&0&0&0&0\\ \end{array} \right)\]  
\[\mathbf{H}(\mathbf{Z})=\left( \begin{array}{cccccccc}k_{bf}(z_{s}-l_{f}\varphi -z_{bf})^{3}+k_{br}(z_{s}+l_{r}\varphi-z_{br})^{3}\\ k_{br}l_{r}(z_{s}+l_{r}\varphi-z_{br})^{3}-k_{bf}l_{f}(z_{s}-l_{f}\varphi-z_{ bf})^{3}\\ 0\\ k_{ba}(z_{a}-z_{ba})^{3}\\ -k_{ba}(z_{a}-z_{ba})^{3}\\ k_{bf}(z_{bf}-z_{s}+l_{f}\varphi)^{3}\\ k_{br}(z_{br}-l_{r}\varphi-z_{s})^{3}\end{array} \right)\mathbf{F}=\left( \begin{array}{c}0\\ 0\\ k_{tf}z_{rf}\\ k_{tr}z_{rr}\\ 0\\ 0\\ 0\end{array} \right)\mathbf{Z}=\left( \begin{array}{c}z_{s}\\ \varphi\\ z_{uf}\\ z_{uf}\\ z_{zu}\\ z_{ba}\\ z_{bf}\\ z_{bf}\\ z_{bf}\\ z_{bf}\\ z_{bf}\\ z_{bf}\\ z_{bf}\\ \end{array} \right)\]  
\[c_{11}=c_{f}+c_{r}+c_{a}+c_{bf}+c_{bf}+c_{br} \qquad c_{12}=c_{r}l_{r}-c_{f}l_{f}-c_{a}l_{a}-c_{bf}l_{f}+c_{br}l_{r}\] \[c_{21}=c_{r}l_{r}-c_{f}l_{f}-c_{a}l_{a}-c_{bf}l_{f}+c_{br}l_{r} \qquad c_{22}=c_{r}l_{r}^{2}+c_{f}l_{f}^{2}+c_{a}l_{a}^{2}+c_{bf}l_{f}^{2}+c_ {br}l_{r}^{2}\] \[k_{11}=k_{f}+k_{r}+k_{a} \qquad k_{12}=k_{r}l_{r}-k_{f}l_{f}-k_{a}l_{a}\]  
\[k_{21}=k_{r}l_{r}-k_{f}l_{f}-k_{a}l_{a} \qquad k_{22}=k_{r}l_{r}^{2}+k_{f}l_{f}^{2}+k_{a}l_{a}^{2} \tag{12}\]  
The dynamic equations of the linear system corresponding to the half-vehicle seat system integrated with NESIs can be obtained by setting the nonlinear term matrix, \(\mathbf{H}(\mathbf{Z})\), to zero, which is provided as  
\[\mathbf{M}\mathbf{Z}^{\prime\prime}+\mathbf{C}\mathbf{Z}^{\prime}+\mathbf{K }\mathbf{Z}=\mathbf{F} \tag{13}\]  
The natural frequencies of the linear system can be determined based on its characteristic equation; the characteristic equation is provided by  
\[\left|\mathbf{M}^{-1}\mathbf{K}-\omega_{n}^{2}\mathbf{I}\right|=0 \tag{14}\]  
where \(\omega_{n}\) denotes the natural frequency, and \(\mathbf{I}\) is the identity matrix. Substituting the structural parameters shown in Table 1 into Equation (14), the natural frequencies of the linear system are equal to 5.835 \(\mathrm{rads}^{-1}\), 8.059 \(\mathrm{rads}^{-1}\), 11.716 \(\mathrm{rads}^{-1}\), 70.289 \(\mathrm{rads}^{-1}\), and 73.853 \(\mathrm{rads}^{-1}\).
## 2 Founding Principles around Secure Data Storage  
Protecting data in Cloud-based storage systems is a key challenge [1], and existing architectures often fail to properly control access rights to such data. The insider threat, especially from the likes of a System Administrator, and from unforeseen implementation issues, causes Cloud-based systems to be flawed from many viewpoints. There are thus some key driving forces to ensure that data is protected in future Cloud-based infrastructures, including:  
* **Keyless encryption method**. While the methods employed in modern encryption are often theoretically watertight, it is the actual implementation in real-life systems which are flawed. Most systems still rely on digital certificates holding a key-pair, and these certificates are all too easily stolen or compromised through brute force attacks. The Superfish compromise highlights a weakness introduced by the software developers, where a digital certificate with both the public and private key was distributed with the software, and which also had a default password based on the name of the company who developed the software [2].
* **Self-destruct**. Some techniques support the concept of a self-destructing data system. With this, all the stored information and their copies, as well as decryption keys, will become unusable after a user-specified time period and without any user involvement. SeDas [3] causes sensitive information, such as account numbers, passwords and notes to irreversibly self-destruct, without any action on the user's part. This technique is applicable in a multiple server-based system, admittedly incurring an operational overhead.
* **Break-glass data recovery.** Many systems have strict access control policies for data, but in some circumstances it is important that access to data can be granted in emergency situations. This is typical in life-threatening scenarios, such as in health and social care. The loss of an encryption key can also cause major problems in data recovery, which would hinder a break-glass requirement.  
* **In-built failover protection.** For instance, data which needs redundancy can be replicated and encoded across multiple cloud-based storage systems using an _any k-from-n_ sharing policy. So for example, with a storage system striped across three Cloud storage platforms, a policy of 2-from-3 can support a single Cloud storage systems failure, while still allowing the original data to be recovered.  
This paper looks at a range of techniques for tackling these issues using a _secret sharing_ technique, where the information to be made secret is encoded and distributed across multiple data shares, where each share of the information is stored on a different cloud provider.
## 4 Experimental Setup
### System Overview  
Figure 10 illustrates the complete laboratory setup, highlighting the spatial arrangement and interconnection of all key components. The wind turbine is placed directly in front of the industrial fan, which is responsible for generating the artificial wind. The generator's output is routed through the rectifier, followed by the power conditioning stages, and finally to the storage interface, completing the power conversion process. All system variables--including wind speed, DC voltage, DC current, and PWM duty cycle--are continuously monitored and logged using the sensors and the digital acquisition system described in Section 3.1.1. This ensures real-time tracking of the turbine's performance under various conditions.  
Additional instrumentation includes:  
* Oscilloscope: to visualize PWM signal, coil current, and switching transients on the IGBT gate;
* Watt meters and multimeters: to monitor generated power and verify measurement accuracy;  
Figure 10: Complete experimental set-up of the platform presented.  
Figure 9: Bidirectional power supply that emulates a Li-ion Battery.  
* Tachometer and frequency meter: to evaluate generator shaft speed and electrical frequency, enabling rotor characterization.
## 4 Results
### Mineralogy and Lithofacies  
The X-ray diffraction (XRD) results suggest that the WF-LMX shales primarily comprise quartz and clay, followed by calcite, dolomite, feldspar, and pyrite (Figure 3). For well DA, quartz contents range from 20.4 to 76.2% (average = 43.3%), and clay contents range from 14.6 to 64.8% (average = 39.3%). For well DB, quartz contents range from 19.7 to 73.5% (average = 37.7%), and clay contents range from 15.5 to 61.7% (average = 43.8%). Based on the lithofacies classification ternary diagram of clay-carbonate-quartz + feldspar [32; 33; 34; 35], the WF-LMX shale lithofacies in this study are mainly siliceous shale, siliceous-argillaceous mixed shale, and argillaceous shale (Figure 4).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Well Name**} & \multirow{2}{*}{**Depth (m)**} & \multicolumn{2}{c}{**TOC (wt.\%)**} & \multicolumn{2}{c}{**TOC (wt.\%)**} \\ \cline{2-9}  & & & **Pre-Combustion** & \multicolumn{2}{c}{**Post-Combustion**} \\ \hline DA & 2247.09 & 1.93 & 0.07 \\ \hline DA & 2262.2 & 2.98 & 0.08 \\ \hline DA & 2267.7 & 5.53 & 0.08 \\ \hline DA & 2269.74 & 5.38 & 0.09 \\ \hline DB & 3743.3 & 0.74 & 0.08 \\ \hline \end{tabular}
\end{table}
Table 2: TOC content of the Wufeng–Longmaxi shale samples after combustion.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Well Name**} & \multirow{2}{*}{**Depth (m)**} & \multicolumn{2}{c}{**TOC (wt.\%)**} & \multicolumn{2}{c}{**TOC (wt.\%)**} \\ \cline{2-9}  & & & **Pre-Combustion** & \multicolumn{2}{c}{**Post-Combustion**} \\ \hline DA & 2247.09 & 1.93 & 0.07 \\ \hline DA & 2262.2 & 2.98 & 0.08 \\ \hline DA & 2267.7 & 5.53 & 0.08 \\ \hline DA & 2269.74 & 5.38 & 0.09 \\ \hline DB & 3743.3 & 0.74 & 0.08 \\ \hline \end{tabular}
\end{table}
Table 1: TOC content of the Wufeng–Longmaxi phase samples from the Wufeng–Longmaxi formations, Dingshan area, southeast Sichuan Basin.
## 4 The Effect of Fog on Frequency-Modulated Continuous-Wave (FMCW) Radar Accuracy  
Previous theoretical work indicated that dispersion in the atmospheric medium affects the accuracy of radars [19]. In the setup described in the followings, a wide band FMCW is utilized for an experimental study of the effect of fog on the accuracy of distance measurement to a target. The transmitted signal is a frequency modulated (FM) signal with an instantaneous frequency varying linearly in time (chirp) [19]. The time dependent frequency in the chirp is given as:  
\[f_{i}(t)=f_{0}+\frac{\text{BW}}{T_{s}}\cdot t \tag{18}\]  
where \(f_{0}\) is the start frequency of the chirp, BW is frequency span, and \(T_{s}\) is sweep time. Passing through the atmosphere to the target located at a distance \(d\), scattered and reflected back to the radar receiver, the 'chirped' signal is delayed and its intensity is reduced. The intermediate frequency (IF) of the detected signal obtained at the receiver output is expected to be:  
\[f_{IF}=2\cdot\frac{\text{BW}}{T_{s}}\cdot\tau_{d}=2\cdot\frac{\text{BW}}{T_{s} }\left(\frac{d}{c}+\Delta\tau_{d}\right) \tag{19}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Frequency** & **Reference** & **Water Droplet** & **Fog Attenuation** & **Fog Attenuation** \\
**(GHz)** & **Concentration** \(W_{0}\) **(g/m\({}^{3}\))** & **Experiment (dB/km)** & **Simulation (dB/km)** \\ \hline
72.56 & [24] & 0.2 & 0.6 & 0.7 \\
210 & [25] & 0.03 & 0.4 & 0.3 \\
240 & [26] & 3 & 37 & 38.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of measured fog attenuation in millimeter wavelengths with previous researches.  
Figure 3: Visibility (m) as a function of liquid water content \(W_{0}\) (g/m\({}^{3}\)) on assumed droplet number concentration \(n_{d}\) (cm\({}^{-3}\)).  
Here \(\tau_{d}\) is the time delay in propagation a range \(d\) to the target. For propagation in a vacuum, the incremental group delay \(\Delta\tau_{d}=0\), and the resulted IF signal would have been a single frequency tone at:  
\[f_{m}=2\cdot\frac{\text{BW}}{T_{s}}\cdot\frac{d}{c} \tag{20}\]  
which is proportional to the target distance \(d\). However, when a dispersive medium (as in foggy conditions) is involved, there is a shift in the intermediate frequency due to the incremental group delay \(\Delta\tau_{d}\):  
\[\Delta f_{IF}=f_{IF}-f_{m}=2\cdot\frac{\text{BW}}{T_{s}}\cdot\Delta\tau_{d} \tag{21}\]  
Using the Expression (9) we derived for group delay \(\Delta\tau_{d}\), one can express the error \(\Delta d\) in the radar range measurement expected due to the dielectric properties of the medium in different weather conditions:  
\[\frac{\Delta d}{d}=c\cdot\frac{\Delta\tau_{d}(f_{0})}{d}=2\pi\bigg{(}\frac{f_ {0}^{2}}{c}\bigg{)}\frac{1}{\alpha^{2}+\beta^{2}}\bigg{[}\alpha\bigg{(}\frac{ \varepsilon^{\prime\prime}}{f_{0}}+\frac{1}{2}\frac{d\varepsilon^{\prime \prime}}{df}\bigg{|}_{f_{0}}\bigg{)}+\beta\bigg{(}\frac{\varepsilon^{\prime}} {f_{0}}+\frac{1}{2}\frac{d\varepsilon^{\prime}}{df}\bigg{|}_{f_{0}}\bigg{)} \bigg{]}-1 \tag{22}\]
# Parallelization of the Bison Algorithm Applied to Data Classification
## 4 Experimental Setup
### Infrastructure for Running Experiments  
The nodes used for the experiments were the Pittsburgh Supercomputing Center's Bridges-2 system, which is equipped with two AMD EPYC 7742 CPUs, each featuring 64 cores (128 threads total per node) with a clock speed between 2.25 GHz and 3.40 GHz. These nodes have either 512 GB of RAM and 3.84 TB of local NVMe SSD storage. The nodes are connected via Mellanox ConnectX-6 HDR InfiniBand with 200 Gbps bandwidth, providing efficient communication across the system, which is key for high-performance computing tasks such as used in this study.