esa-sceva/satcom-chunk-collection huggingface dataset:
# A Low-Complexity Security Scheme for Drone Communication Based on PUF and LDPC
## 3 Security Architecture
### System Model
#### 3.1.4 Implementation  
This study uses two STM32F407-DISCOVERY (STMMicroelectronics, China, www.st.com/en/evaluation-tools/stm32f4 discovery.html (accessed on 1 May 2024)) boards (operating at a frequency of 168 MHz, with 128 KB of RAM) to simulate drones, with a Raspberry Pi 3 (RP3) simulating the ground station. An Arduino MEGA board and relays are used for automated power control, which can work with the RP3 for automated ID extraction. The RP3 development environment runs on Python 3.10 under Linux, with a text-based database. The development boards utilize the Keil MDK 5.24 integrated development environment, which includes online simulation functionality for easy monitoring of resource usage, memory allocation, and processing speed, particularly when calculating the extraction position of the PUF.
## 6 Security Analysis and Results
### Storage Cost  
Since sensor motes operate under limited memory, we ensure that the persistent storage costs associated with **Discard** are well within the available memory limits of current sensor motes. For example, Mica2 motes have 4 KB RAM, as listed in the data sheet by the manufacturer CrossBow Technologies [43]. In **Discard**, each node maintains two caches, and we showed that the total size of these caches is \(48c+80d\) bits for neighborhood and duplicate caches of sizes \(c\) and \(d\), respectively. For \(c=5\) and \(d=5\), we see that this size is \(640\) bits, well within the Mica2 data storage capability, leaving enough room for temporary working memory space for the \(outsideHomeZone\) cache. Clearly, there is also enough memory available to run the sensor network protocols of primary interest and, if needed, to increase the cache sizes, as well, to improve the detection performance.
# Smart Rings vs. Smartwatches: Utilizing Motion Sensors for Gesture Recognition
## 4 Evaluation and Results
### Evaluation
#### 4.3.1 Activation Mechanism-Independent  
Both data sets of the watch session achieve a relatively high accuracy with 98.8% for the watch data set and 96.6% for the ring data set. Overall, the watch data set produces models with higher accuracies. This suggests that finger movements produce data with more variance, which is reasonable, because finger movements are more flexible than hand movements. The finger can be moved relative to the wrist when performing a gesture, adding varying information to the data. Since the same gestures are used, the varying information of the finger data is distributed inside the gesture class, ultimately leading to more variance inside the class. Additionally, during the observation of the data acquisition, it was evident that the finger position changed slightly over time when participants focused solely on the display of the watch. This changes the reference frame of the gestures, which adds even more variance to the data. On the contrary, the position of the watch never changed, because it was always corrected implicitly by pressing the button on the display.  
Another explanation could be that the data originating from the Apple watch are preprocessed internally, which is likely, because the separation of the gravity and the linear acceleration vectors needed to be reversed in order to mirror the data of the ring. To separate the data in the first place, Apple's algorithm needs to preprocess the data, which could affect the data set. However, since the data are preprocessed again by a coarse smoothing filter and most of the features are windowed, it is unlikely that Apple's preprocessing algorithm has a significant impact on the quality of the data set.  
When comparing the confusion matrices of the RF models for both data sets, the only similarity is the confusion of the _Clap Once_ and _Clap Twice_ gestures. However, labeling errors can be ruled out completely, because the gestures were recorded and labeled simultaneously. Generally, it is evident that the clap gestures are more difficult to distinguish across all models. The explanation for this observation could be that the time between the claps is variable, however, this does not affect the rotation gestures, which are also based on a varying time interval. A more reasonable explanation emerges when examining the preprocessed data manually. The moving average smoothing filter distorts the data because the claps create high frequency signals with high amplitudes, which the primitive filter cannot handle very well.
## 2 Problem Statement of Multi-Robot Robust Motion Planning
### Collision Avoidance  
The workspace of the robot is defined as \(C\subseteq\mathbb{R}^{2}\). Let \(\mathbf{p}_{m,k}=[x_{m,k},y_{m,k}]\in\mathbb{R}^{2}\) denote the position of robot \(m\) and \(\mathbf{q}_{o,k}=[x_{o,k},y_{o,k}]\in\mathbb{R}^{2}\) denote the position of obstacle \(o\). Hence, the collision condition of robot \(m\) with obstacle \(o\) at time \(k\) is given as follows.  
\[C_{m,k}:=\Big{\{}\mathbf{x}_{m,k}\ \big{|}\big{\|}\mathbf{p}_{m,k}-\mathbf{q}_{o,k} \big{\|}\leq d_{m}\Big{\}} \tag{3}\]  
In Equation (3), \(d_{m}\) represents the minimum safe distance of robot \(m\).  
For any two robots \(m\) and \(n\), if their position \(\mathbf{p}_{m,k}\) and \(\mathbf{p}_{n,k}\) satisfy the following equation  
\[C_{int\sigma_{-}mn,k}:=\Big{\{}\mathbf{x}_{m,k}\ \big{|}\big{\|}\mathbf{p}_{m,k}-\mathbf{p }_{n,k}\Big{\|}\leq d_{m}+d_{n}\Big{\}}, \tag{4}\]  
there is a collision between robot \(m\) and \(n\).  
Due to the disturbances, the position of the robot is a Gaussian random variable. Chance safety constraints are given as Equations (5) and (6).  
\[\mathcal{P}(\mathbf{x}_{m,k}\
otin C_{m,k})\geq 1-\delta_{o},\forall m \tag{5}\]\[\mathcal{P}(\mathbf{x}_{m,k}\
otin C_{inter\_mm,k})\geq 1-\delta_{r},\forall m \tag{6}\]  
In Equation (5), \(\delta_{o}\) is the probability threshold for robot-obstacle collision. In Equation (6), \(\delta_{r}\) is the probability threshold for inter-robot collision
## 2 Data and Method
### Data
#### 2.1.2 VIIRS Boat Detection Data  
The VIIRS Boat Detection (VBD) data product captures illuminated pixels of fishing boats in a VIIRS DNB image for a single night. The basic VBD algorithms were described by [PERSON] et al. [10]. The VBD data files provide detailed information, including the geo-location of illuminated pixels, radiance values, satellite overpass times, satellite viewing zenith angles, and various threshold parameters used to differentiate potential light sources from fishing vessels and other sources [28]. In this study, nightly VBD data from the 2020 fishing seasons in the study area were utilized to compare and evaluate the performance of deep learning methods for detecting lit fishing activities ([[https://eogdata.mines.edu/products/vbd/](https://eogdata.mines.edu/products/vbd/)]([https://eogdata.mines.edu/products/vbd/](https://eogdata.mines.edu/products/vbd/))). We extracted only VBD points with quality flags 1 (strong boat detection), 2 (weak boat detection), 3 (blurry boat detection), and 10 (weak and blurry lights), which corresponded to radiance spikes more likely to have originated from marine vessels. Additionally, for VBD points in overlapping satellite images, only a selected portion corresponding to GEE filtered parts were used, and pixels with radiance values below 10 were excluded from the analysis [29].
## 3 Comprehensive Model Development  
### Thevenin Equivalent Circuit Model  
The equivalent circuit model is commonly used in predicting battery function and providing estimation to the battery management system [53; 54].Figure 4a shows the battery's open-circuit voltage (OCV)-SoC characteristics. Figure 4b shows the battery's voltage-current characteristics. The battery  
Figure 3: \(SoH_{N}\) at different discharging C rates and temperatures during 400 cycles: (**a**) 1 cycle, (**b**) 100 cycles, (**c**) 200 cycles, (**d**) 300 cycles, and (**e**) 400 cycles.  
models in Figure 4a,b are quite accurate, since they can clearly describe the battery's nonlinear dynamic behavior, which can be used as a good solution for the SoC estimation of energy storage devices.  
The equivalent circuit model can be represented by the following state equations [36; 37; 53; 54], where \(V_{ps}\) and \(V_{pf}\) are the state variables, \(I_{b}\) is the input, and \(V_{b}\) the output:  
\[\begin{array}{l}\dot{V}_{pf}=\frac{1}{R_{1}C_{1}}V_{pf}-\frac{1}{C_{1}}I_{b}; \\ \dot{V}_{ps}=\frac{1}{R_{2}C_{2}}V_{ps}-\frac{1}{C_{2}}I_{b};\\ V_{b}=V_{oc}+V_{ps}+V_{pf}+R_{d}I_{b},\end{array} \tag{4}\]  
where \(V_{b}\) is the voltage at the battery terminals, \(I_{b}\) is the current flowing through the battery, \(V_{oc}\) is the open circuit voltage, \(R_{b}\) is the internal resistance, \(R_{1}\) and \(C_{1}\) comprise the fast resistor-capacitor (RC) network, \(R_{2}\) and \(C_{2}\) comprise the slow RC network, \(V_{pf}\) is the voltage across the fast RC network, and \(V_{ps}\) is the voltage across the slow RC network.  
Overall, the equivalent-circuit model is described by the two RC networks shown in Figure 4b. The slow RC network dominates when the battery is nearly full, in which case the discharging behavior is fairly stable. The fast RC network dominates when the battery is running low--during the unstable discharging behavior.  
The proposed model is used to estimate the battery's SoC and the remaining capacity by considering the battery model's parameters, and the available states are \(V_{b}\), \(I_{b}\), and temperature. Therefore, the simplifications of the electrochemistry equations make it possible to integrate it using equivalent circuit models.
## 2 Literature Review  
Algorithms used on IoT devices are attacked using linear cryptanalysis attacks. This attack reveals the cryptographic key to the intruder. Intruders use the key to obtain access to private data and information on the IoT system. [PERSON] et al. [24] used the linear cryptanalysis attack on AES. [PERSON] et al. [25] used the linear cryptanalysis attack on AES. The results of obtaining a key were successful on two rounds due to the way that the AES S-Box was constructed [25]. Blowfish is also one of the algorithms that suffers from linear cryptanalysis attacks. The small portions of keys were discovered after the linear differential cryptanalysis attack was applied to BLOWFISH because of the generated weak components (like P-arrays) [26]. P-array is the function defined on BLOWFISH; it has all the properties like S-Box [27]. The attack can only be detected if the weak key is used [26] (p. 20). CAMELLIA revealed its cryptographic key in round nine after being modified for the application of the linear cryptanalysis attack [27]. [PERSON] [28] attacked CAMELLIA using the linear cryptanalysis attack. [PERSON] et al. [29] applied linear cryptanalysis to CAST; the cryptographic key was revealed on rounds 6 and 18 [29], and the experiment was conducted using the simplified S-Box. CLEFIA revealed its cryptographic key on 11, 12, 14, and 15, depending on the size of the inputs [31]. DES revealed its cryptographic key on rounds 3, 5, 8, 12, and 16 during the linear cryptanalysis attack [32]. MMB revealed its cryptographic key on all rounds during the linear cryptanalysis attack [33]. [PERSON] and [PERSON] [34] revealed the full cryptographic key of RC5 using the linear cryptanalysis attack [34]. SERPENT revealed its cryptographic key on round 11 during the linear cryptanalysis attack [35]. SKIPJACK reveals a certain bit of its cryptographic key from S-Box during the linear cryptanalysis attack [36] (p. 25). S-Boxes are the main building blocks of algorithms to give more chances to allow linear cryptanalysis attacks to be conducted [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39].  
The more S-Box is poorly constructed, the easier it is to conduct the linear cryptanalysis attack [1; 2; 3; 40; 41; 42; 43; 44; 45; 46]. Little has been achieved to block linear cryptanalysis attacks on most cryptographic algorithms used on IoT devices [1; 2; 3; 40; 41; 42; 43; 44; 45; 46; 41; 42; 43; 44; 45; 46]. In this study, we block linear cryptanalysis attacks by applying a mathematically novel approach of using the Galois Field of order \(\mathbf{2^{32}}\), denoted by GF (\(\mathbf{2^{32}}\)), with the combination of generated irreducible polynomials to generate S-Boxes (or any function like S-Box) that will give 32-bit outputs. With the S-Box of 32-bit output, it is difficult to attack the algorithm using the linear cryptanalysis attack [29] (p. 429). A summary of the literature review is given in Table 1.
## 2 Materials and Methods
### Modeling
#### 2.2.3 The \(k\)-\(C^{*}\) Model  
The \(k\)-\(C^{*}\) model describes the overall performance of an individual layer in terms of the pseudo-first-order kinetics under steady-state conditions, as in Equation (7) [11; 43]:  
\[\frac{C-C^{*}}{C_{0}-C^{*}}\ =\ e^{-k_{C}H/u} \tag{7}\]  
where \(C\) is the effluent SS concentration (mg/L) at time \(t\), \(C_{0}\) is the influent SS concentration (mg/L), \(C^{*}\) is the background SS concentration (mg/L), \(u\) is the hydraulic loading (m/h), \(H\) is the depth of an individual layer (m), and \(k_{C}\) is the rate constant (h\({}^{-1}\)). Equation (7) can be expressed as Equations (8) and (9) by substituting \(C^{*}\) for \(A_{C}\):  
\[\frac{C}{C_{0}}\ =\ A_{C}e^{-k_{C}H/u} \tag{8}\]  
\[ln\bigg{(}\frac{C}{C_{0}}\bigg{)}\ =\ ln(A_{C})-k_{C}\frac{H}{u} \tag{9}\]  
where \(A_{C}\) is a constant. The effects of various SS removal mechanisms (i.e., diffusion, straining, adhesion, interception, and sedimentation) are collectively represented by \(k_{C}\) and \(A_{C}\), whose values are influenced by the pollutant concentration and hydraulic conditions [44]. Previously, the time-courses of SS removal have been predicted using the \(k\)-\(C^{*}\) model for wastewater filtration and stormwater treatment systems such as infiltration systems, swales, constructed wetlands, and gravel filters [11; 44; 45]. In the present study, however, Equation (9) is modified by considering the SS load and the specific deposit (i.e., \(\sigma\), as presented in Equations (10) and (11) below) because it is thought that the SS concentration might not represent the performance of the system due to the irregular inlet and outlet SS concentrations induced by the sedimentation chamber:  
\[ln\bigg{(}\frac{L}{L_{0}}\bigg{)}\ =\ ln(A_{L})-k_{L}\frac{H}{u} \tag{10}\]  
\[ln(\sigma)\ =\ ln(A_{\sigma})-k_{\sigma}\frac{H}{u} \tag{11}\]  
where \(L\), \(L_{0}\), and \(\sigma\) (kg/m\({}^{3}\)) are the outlet SS load, inlet SS load, and specific deposit at time \(t\), respectively; \(A_{L}\) and \(A_{\sigma}\) are the constants corresponding to the SS loads and the specific deposit, respectively; and \(k_{L}\) and \(k_{\sigma}\), are the rate constants corresponding to the SS loads and specific deposit, respectively.
## 3 Results  
There were no significant differences between the PG and CG with regard to age, gender, underlying pathology, type of aHST, and aHST conditioning regimen.  
The mean duration of the PBM performed in the PG was 21 days. The average number of PBM sessions performed was 16. In the PG, the incidence of OM was 60% (12 cases) with a maximum OM grade of II. Four of them (20%) suffered from OM grade I (general erythema) with a mean duration of 2.15 days. The rest (40%) suffered from grade II (ulcerative stage) with a mean duration of 2.2 days. The mean starting time of OM was D+7 (7 th day post-transplantation). The mean duration of OM was 4.7 days. No cases experienced OM grades III and/or IV. Eight cases (40%) experienced a maximum OM grade of 0. The maximum registered NRS pain value was 8 (with a mean of \(\sim\)2).  
The incidence of OM was 100% (20 patients) in the CG. The mean duration of OM was 15 days. The maximum OM grade was grade IV in 2 cases (10%) with a mean duration of 0.6 days. Eleven patients (55%) showed grade II as a maximum grade of OM. Only 3 patients (15%), experienced a maximum OM grade of I, with a mean duration of 6.3 days. The development of OM was observed at D+6 (6 th day post-transplantation) (Figures 1 and 2).  
In both groups, the most frequent sites of OM were the tongue with 21 patients (52.5%) and the floor of the mouth with 10 patients (25%).  
In the PG, a significant difference was observed between the type of transplantation and the total duration of OM (\(p=0.036\)), and the duration of OM grade I (\(p=0.034\)). Patients who received aHST from an MRD, showed a longer total duration of OM (MRD = 8.50 \(\pm\) 4.85 days; MUD = 3.07 \(\pm\) 4.32 days) and demonstrated a longer duration of OM grade I (MRD = 4.17 \(\pm\) 2.93 days; MUD = 1.29 \(\pm\) 2.37 days). In the CG, no significant correlation was observed between the set of variables and age, gender, and type of transplantation.  
Several variables were used to compare the groups (PG vs. CG), and statistical significance was observed for the maximum grade of OM, the total duration of OM, duration of grade 0, grade I, grade II and grade III, and OM scores at evaluation time points E1, E2, E4, and E5 (\(p<0.05\)) (Table 3) (Figure 3).  
Figure 1: Distribution of cases according to the maximum oral mucositis grade.  
Figure 3: Mean oral mucositis score at the evaluation time points (E).  
Figure 2: Mean duration of each oral mucositis grade in both the preventive group and the control group.  
The severity of OM was observed to be significantly different between the 2 groups (PG vs. CG) (\(\chi^{2}=8424;p=0.003\)): 12 patients (60%) in the PG showed a non-ulcerative or only erythema (grade \(\leq\) 1). The incidence of OM showed a significant reduction in patients treated by PBM (\(\chi^{2}=\) 10,000; \(p=0.002\)): 8 patients (40%) in the PG did not show OM (grade 0) during the total duration of therapy. In contrast, all patients in the CG showed OM (grade \(>\) 0) (Table 4).
# Heat Transfer Enhancement of Nanofluids with Non-Spherical Nanoparticles: A Review
## 2 Thermal Conductivity of NSN Nanofluids
### Numerical Predictions and Mechanism Discussion  
Effective heat conductivity (EHC) \(k\) is often used to describe the bulk ability of thermal conductivity. For suspensions with spherical particles, [PERSON] [52] first presented the classical EHC model:  
\[k_{nf}=k_{f}\left[\frac{(k_{p}+2k_{f})+2\phi\Big{(}k_{f}-k_{p}\Big{)}}{(k_{p}+2k _{f})-\phi\Big{(}k_{f}-k_{p}\Big{)}}\right] \tag{1}\]  
The formula is derived from spherical particles, but recent studies have found that it is also effective for diluting Newtonian nanofluids [17]. Based on the Maxwell model, [PERSON] and [PERSON] [53] presented the model for suspensions with non-spherical particles:  
\[k_{nf}=k_{f}\left[\frac{k_{p}+(n-1)k_{f}-(n-1)(k_{p}-k_{f})\phi}{k_{p}+(n-1)k _{f}+\Big{(}k_{p}-k_{f}\Big{)}\phi}\right] \tag{2}\]  
where \(n\) is the shape factor, and \(n=3/s_{i}\). \(s_{i}\) is the sphericity, which equals 3 for spherical particles. The data listed in Table 1 indicate that the EHC for nanofluids is influenced by particle shape and materials, particle loading, and pH. [PERSON] et al. [54] established a model for nanoparticle aggregates by combining the effective medium approximation and the fractal theory. Nonetheless, the fractal dimension or shape factor index could not provide particle shape characteristics and the interaction with base fluids. Therefore, there is no perfect model to predict the thermal conductivity of various nanofluids accurately. Table 2 lists several popular models and numerical studies for EHC of NSN nanofluids. Nanofluids with tube/wire/cylinder-shaped nanoparticles showed superior TCE. Thus, the models in the literature are primarily established to focus on this type of particle. Most of these models are based on the classical H-C model and modified based on the experimental data or by combining the characteristics of nanofluids.
# AR Long-Term Tracking Combining Multi-Attention and Template Updating
## 4 Experimental Analysis
### Analysis of Results
#### 4.2.2 Visualization of the Tracking Effect  
We selected some challenging video sequences on the OTB100 dataset to test our method in complex environments, and the tracking effect is shown in Figure 9.  
We selected the Diving, David, Biker, Jump, and Lemming sequences in the OTB100 dataset to test our method, respectively. During the Diving video sequence, the diver's target constantly underwent scale changes, rotation, and deformation. In the David video sequence, as the target kept moving, we could find that the illumination of the environment was constantly changing, while the target also underwent scale changes, deformation, and rotation. In the Biker sequence, with the rapid rotation of the bicycle, the target goes through continuous scale change, occlusion, motion blur, fast movement, out-of-vision, and low resolution. In the Girl video sequence, the target is persistently affected by scale change, occlusion, and rotation, while in the Jump video sequence, the target has constantly undergone scale change, occlusion, deformation, motion blur, and rotation. Lemming video sequence targets constantly undergo scale change, occlusion, rapid movement, and rotation. The information displayed in Figure 9 shows that the robustness of our method remains good even when confronted with many challenges.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Trackers** & **F-Score** & **Pr** & **Re** \\ \hline ADiMPL & 0.501 & 0.489 & 0.514 \\ SuperDiMP & 0.503 & 0.510 & 0.496 \\ CoCoLoT & 0.584 & 0.591 & 0.577 \\ SaimDW\_LT & 0.601 & 0.618 & 0.585 \\ SpLT [37] & 0.635 & 0.587 & 0.544 \\ CLGS & 0.640 & 0.689 & 0.598 \\ LTMU\_B [38] & 0.650 & 0.665 & 0.635 \\ STARK-ST50 & 0.702 & 0.710 & 0.695 \\ ours & 0.695 & 0.678 & 0.713 \\ \hline \end{tabular}
\end{table}
Table 6: Comparisons on VOT-LT2020 benchmark. The best three results are marked in red, green, and blue bold fonts, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Trackers** & **MaxGM** & **TPR** & **TNR** \\ \hline Staple & 0.261 & 0.273 & - \\ BACF [39] & 0.281 & 0.316 & - \\ EBT [40] & 0.283 & 0.321 & - \\ SiamFC & 0.313 & 0.391 & - \\ ECO-HC & 0.314 & 0.395 & - \\ LCT [41] & 0.396 & 0.292 & 0.537 \\ DaSiam\_LT & 0.415 & 0.689 & - \\ TLD [42] & 0.431 & 0.208 & 0.895 \\ MBMD [43] & 0.544 & 0.609 & 0.485 \\ GlobalTrack [44] & 0.603 & 0.574 & 0.633 \\ SPLT [37] & 0.622 & 0.498 & 0.776 \\ STARK & 0.782 & 0.841 & 0.727 \\ ours & 0.733 & 0.723 & 0.743 \\ \hline \end{tabular}
\end{table}
Table 7: Comparisons on the Ox-UvA [33] long-term dataset. The best three results are marked in red, green, and blue bold fonts, respectively.
# BDS/IMU Integrated Auto-Navigation System of Orchard Spraying Robot
## 2 Materials and Methods
### Control Model of the System  
#### 2.3.1 Navigation Control Principle of the Spraying Robot  
The automatic navigation control system of the spraying robot was composed of the path tracking system. The structure of the path tracking system is shown in Figure 6. The path to be tracked is selected through the user interface and the automatic navigation start command is sent. The sprayer obtains the real-time information of position and heading, the path tracking algorithm searches the target point on the path and calculates the control parameters, sends the control parameters to the single chip microcomputer through the RS232 communication interface. The single chip microcomputer verifies the integrity of the control parameters, converts them into PWM signals to send to the servo motor driver, which controls the motor to drive the crawler moment. The path tracking algorithm keeps cycling until the distance between the actual position of the spraying robot and the last path point is less than 0.1 m or the automatic navigation termination command is sent out.  
Figure 5: The flow chart of the integrated navigation program.