esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Detection of Arabic Social Spam Using Data Augmentation and Machine Learning
## 4 Experiments
### Experiment 1: Non-Augmented Dataset with TF-IDF Feature  
Table 3 and Figure 4 show the obtained results for the non-augmented dataset using only the TF-IDF feature. These findings showed that the Macro F1 score for NB, LR, and LinearSVC is about 58%. The difference was small, only about 0.01%. As for accuracy, the LinearSVC surpasses the other tested classifiers with 91.8% of correctly classified instances. LR Accuracy is 89.3%. The lowest classifier is NB, with an accuracy of 89.1%. The accuracy range was between 92% and 89%. In terms of AUC, LR performs 75% better than the other tested classifiers, and SVC has an AUC of 74%, while NB has the lowest AUC of 59%.
# Marine UAV-USV Marsupial Platform: System and Recovery Technic Verification
## 3 Main Method
### Active Modeling of UAV-USV Heterogeneous Platform Motion Model  
#### 3.1.1 Relative Dynamics Modeling of Heterogeneous Platforms  
The establishment of a UAV-USV system mathematical model is the basis and key for subsequent collaborative planning and research. Since both UAV and USV are typical nonlinear systems, it is necessary to analyze the relative motion of the system based on establishing an accurate nonlinear model of a single unmanned system, to achieve dynamic modeling of the entire collaborative system.  
First, the relative motion states between platforms are introduced to characterize the relative dynamics and kinematics of the UAV-USV system in free mode [35; 36].  
As shown in Figure 6, consider the following relative motion states.  
\[\left[\begin{array}{c}p_{e}^{E}\\ \Theta_{e}^{E}\end{array}\right]=\left[\begin{array}{c}p_{a}^{E}\\ \Theta_{a}^{E}\end{array}\right]-\left[\begin{array}{c}p_{s}^{E}\\ \Theta_{s}^{E}\end{array}\right]\text{, }\left[\begin{array}{c}V_{e}\\ \Omega_{e}\end{array}\right]=\left[\begin{array}{c}V_{a}\\ \Omega_{a}\end{array}\right]-R_{s}^{a}\left[\begin{array}{c}V_{s}\\ \Omega_{s}\end{array}\right] \tag{1}\]  
where \(p\), \(\Theta\), \(V\), and \(\Omega\) indicate the global position, attitude, body shafting speed, and attitude angular velocity of the UAV (subscript a) and USV (subscript s), respectively. Moreover, \(p_{e}^{E}\), \(\Theta_{e}^{E}\), \(V_{e}\), and \(\Omega_{e}\) are the relative attitude of the UAV-USV system. \(R_{s}^{a}\) is a transformation matrix, which represents the transformation of the USV body axis to the UAV body axis. The abovementioned relative pose analysis can effectively characterize the relative motion of the UAV-USV system in the free mode.  
Considering that the relative pose model contains the UAV and USV dynamic characteristics and the form is relatively complicated, the above model is simplified as necessary. For the strong nonlinear characteristics of the model, the tensor product-based model transformation can be used to obtain the following linear variable parameter (LPV) model.  
\[\dot{x}(t)=A(\rho)x(t)+B(\rho)u(t) \tag{2}\]  
where \(\rho\) is the measurable variable parameter, \(x\) is the system state, \(u\) is the input, and the system matrix \(A(\rho)\) and the input matrix \(B(\rho)\) are generally linearly dependent on the variable parameter [10].
# Shadow Removal for Enhanced Nighttime Driving Scene Generation
## 5 Conclusions  
In this study, we introduced a novel approach called Partitioned Shadow Removal, which segments driving scenes into partitions for effective shadow removal and seamlessly merges them back. Furthermore, this Partitioned Shadow Removal technique is integrated with I2I translation. By segmenting images and applying shadow removal to each partition, our method addresses the limitations of current models trained on datasets with narrow fields of view, consistent depth, and limited object diversity. This approach enhances the applicability of shadow removal models to complex driving scenarios, where shadows and varying object depths are prevalent.  
Our integration of shadow removal with I2I translation allows for the generation of more realistic nighttime images, leading to improved performance in data augmentation-based segmentation models. This combined framework better represents the unique visual challenges posed by nighttime driving conditions, which are often neglected by models trained predominantly on daytime datasets.  
The experimental results demonstrated significant performance improvements, particularly in road segments where shadows are more common. Further study could lead to improvement in entire regions without introducing unwanted artifacts in non-shadowed regions.  
[PERSON] contributed to this work by performing the experiment, by analyzing the data, and by writing the manuscript; [PERSON] and [PERSON] conceived the method; [PERSON] and [PERSON] revised and edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT) (No. 2022R1A5A8026986 and No. 2022R1F1A1073745), a Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government(MOTIE) (P0020536, HRD Program for Industrial Innovation), and the Chungbuk National University BK21 program (2022).  
Informed Consent Statement: Informed consent was obtained from all individual participants included in the study. All participants signed a consent form after the nature and possible consequences of the studies were explained.  
The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.  
The authors declare no conflicts of interest.  
Figure 10: Examples of segmentation results. (Road areas are colored in gray color).
# Towards Bio-Inspired Anomaly Detection Using the Cursory Dendritic Cell Algorithm
## 4 Cursory Dendritic Cell Algorithm
### Proposal Overview  
Both the online adaptation described in Section 4.2 and the data-driven adaption described in Section 4.3 were implemented upon the modular structure described in Section 4.1. The online adaptation was implemented in the _DC Population_ and in the _Antigen Repertoire_ modules. In the former, _DCs_ directly notify _antigens_ of their collection and maturation, and in the latter, _MCAV_ values are updated incrementally. Regarding the  
Figure 9: Heat map of K-Means for different numbers of clusters on the same data.  
data-driven adaptation, the _Signal Extractor_ module is addressed, such that the K-Means approach is used for the extraction of _danger_ and _safe_ signals from data.  
Considering the contributions described previously, the overall CDCA works as such:  
1. Training phase: 1. The _Interface_ receives a predefined number of normal data points, one by one, which will be used as a baseline for normal behavior. 2. The _Signal Extractor_ receives these data points, one by one, and applies a standard K-Means approach for clustering the data points. Note that, at this stage, a classification is not possible since there is not yet a normal baseline. 3. Eventually, when sufficient training points are received, the _Signal Extractor_ computes each cluster's maximum distance from its centroid to each point considered in the cluster, which will serve as radii for each cluster's hypersphere. The centroids and radii are stored, but the training data points are not.
2. Anomaly Detection phase: 1. The _Interface_ receives each data point, i.e., an _antigen_, one at a time. 2. The _Signal Extractor_ receives each point, and outputs its _danger_ and _safe_ signals, depending on whether they are inside a hypersphere or not, and how far they are from the closest hypersphere surface. _PAMP_ and Inflammation signals are not used in CDCA. 3. Upon receiving the _antigen_ and resulting signals, the _Sampler_ samples _DCs_ in a sliding window approach (n-contiguous cells). 4. Each _DC_ receives the signals and computes their costimulatory value (used for migration) and k value (used to decide whether they migrate as mature or semi-mature). Upon receiving an _antigen_, each _DC_ notifies the respective _antigen_ in the _Antigen Repertoire_ of its collection, incrementing its record of the number of times collected. 5. Over time, _DCs_ mature, and notify their _antigens_ of the times they mature as mature _DC_. _DCs_ that mature as semi-mature also notify _antigens_. 6. When _antigens_ in the _Antigen Repertoire_ achieve high _MCAVs_, they notify the _Interface_ of their state change, from \"inconclusive\" to \"dangerous\", i.r., an anomaly has been detected. When _antigens_ in the _Antigen Repertoire_ have the sum of _mDC_ and _smDC_ to be equal to the times they have been collected, their state changes from \"inconclusive\" to \"safe\", i.e., the data point is considered normal. _Antigens_ that have notified the _Interface_ are removed from the _Antigen Repertoire_.
# Calculation of Nonlimit Active Earth Pressure against Rigid Retaining Wall Rotating about Base
## 4 Derivation of the Analytical Solution for Active Earth Pressure
### Analysis of the Upper and Lower Interfaces of Thin-Layer Units  
It is assumed that the maximum principal stress at point \(D\) of the soil at the wall-soil contact is \(\sigma_{1}^{a}\). The major principal stress at the parting level is considered to vary linearly with depth, as depicted in Figure 3. The maximum principal stress at an arbitrary point \(i\) on the upper interface of a bending thin-layer cell can be given as  
\[\sigma_{1}^{i}=\sigma_{1}^{a}+\gamma R_{DE}(\sin\theta-\sin\theta_{a}) \tag{31}\]  
where \(\gamma\) is the gravity of soil. \(\theta\) is the angle between the line connecting point \(i\) and the center of its circular arc and the horizontal line, and \(\theta_{a}\) is the angle between the line connecting point D and the center of the circle and horizontal direction. The horizontal force \(F_{x}{}^{DE}\) and vertical force \(F_{y}{}^{DE}\) acting on the top interface \(DE\) from the wall-soil contact to the potential slip-cracking surface of the soil may be represented as  
\[F_{x}^{DE}=\int_{\theta_{a}}^{\theta_{b}}\sigma_{1}^{i}R_{DE}\cos\theta d\theta \tag{32}\]  
\[F_{y}^{DE}=\int_{\theta_{a}}^{\theta_{b}}\sigma_{1}^{i}R_{DE}\sin\theta d\theta \tag{33}\]  
Substituting Equation (31) into Equations (32) and (33) yields  
\[F_{x}^{DE}=\sigma_{1}^{a}R_{DE}b_{1}+\frac{1}{2}\gamma R_{DE}^{2}b_{1}^{2} \tag{34}\]  
\[F_{y}^{DE}=\sigma_{1}^{a}R_{DE}b_{2}+\gamma R_{DE}^{2}b_{3} \tag{35}\]  
Figure 3: Force analysis of a curved thin-layer cell.  
where  
\[b_{1}=\sin\theta_{b}-\sin\theta_{a} \tag{36}\]  
\[b_{2}=\cos\theta_{a}-\cos\theta_{b} \tag{37}\]  
\[b_{3}=\frac{\theta_{b}-\theta_{a}}{2}-\frac{\sin 2\theta_{b}-\sin 2\theta_{a}}{4}- \sin\theta_{a}b_{2} \tag{38}\]  
The horizontal force \(F_{x}{}^{D^{\prime}E^{\prime}}\) and vertical force \(F_{y}{}^{D^{\prime}E^{\prime}}\) on the lower interface of the differential unit \(D^{\prime}E^{\prime}\) may be calculated in the same way as the forces acting on DE at the higher interface of the differential unit.  
\[F_{x}^{D^{\prime}E^{\prime}}=(\sigma_{1}^{a}+\Delta\sigma_{1}^{a})R_{D^{\prime }E^{\prime}}b_{1}+\frac{1}{2}\gamma R_{D^{\prime}E}^{2}b_{1}^{2} \tag{39}\]  
\[F_{y}^{D^{\prime}E^{\prime}}=(\sigma_{1}^{a}+\Delta\sigma_{1}^{a})R_{D^{\prime }E^{\prime}}b_{2}+\gamma R_{D^{\prime}E}^{2}b_{3} \tag{40}\]  
where \(\Delta\sigma_{1}^{a}\) is the stress increment relative to point \(D\). Moreover, \(F_{x}{}^{D^{\prime}E^{\prime}}\) and \(F_{y}{}^{D^{\prime}E^{\prime}}\) are the horizontal force and vertical force of the differential unit \(D^{\prime}E^{\prime}\), respectively.
## 3 Results and Discussion
### Hydrogen Production via Dark Fermentation  
Figure 6 shows the effect of HTP on different biomasses and its effect on hydrogen production following dark fermentation. A significant variation in yields of hydrogen was detected across the substrates, exhibiting a direct relation to the duration of pretreatment.  
Figure 6: Hydrogen production for SBP, BSG, OP, and RH, expressed as mL H\({}_{2}\) (n.c.)/g (d.m.), under DF, dark fermentation. Note: IN = inoculum.  
The maximum hydrogen yield was obtained for SBP treated for 20 min (T20), yielding 97.5 mL H\({}_{2}\) (n.c.)/g (d.m.). Following 3 days of fermentation, hydrogen production becomes more stable. In the case of this substrate, the hydrogen production increased over time across all pretreatment durations (DF10, DF20, and DF30). Furthermore, these results demonstrate the efficacy of the inoculum utilized in the fermentation process, with SBP producing a higher yield of hydrogen. Significantly, SBP was used as the original substrate to feed the acidogenic reactor from whom the inoculum was obtained. In the study that was conducted by [PERSON] et al., hydrothermal treatment of food waste for 20 min at 140 \({}^{\circ}\)C resulted in a high hydrogen yield of 43 mL H\({}_{2}\) (n.c.)/g vs. [45]; this yield is relatively low compared to the results obtained in the present study, where hydrogen production reached (129.9 mL H\({}_{2}\) (n.c.)/g VS). Similarly, in the study by [PERSON] et al., a hydrogen yield of 104.9 mL H\({}_{2}\) (n.c.)/g vs. was reported [46]. In the case of BSG and RH, the maximum hydrogen yields were obtained with DF30, yielding 34.1 mL H\({}_{2}\) (n.c.)/g (d.m.) after two days and 14.1 mL H\({}_{2}\) (n.c.)/g (d.m.) after five days, respectively.  
These results demonstrate the significant adaptability of the inoculum to BSG and its rapid conversion of solubilized compounds into hydrogen. Conversely, the low yield of hydrogen produced by RH is due to its limited solubilization and high lignin content, which limits microbial degradation.  
In the case of OP, the hydrogen yield reached a maximum of 18.6 mL H\({}_{2}\) (n.c.)/g (d.m.) after three days of fermentation, with the biomass pretreated for 10 min. For this biomass, there is no direct and proportional correlation between the solubilization yields achieved and hydrogen production, indicating that the highest solubilization yields were not necessarily followed by the highest hydrogen production.  
Moreover, the significant protein yield extracted from OP after the HTP process was hydrolyzed during DF into peptides and amino acids. These compounds, as alternative carbon sources in metabolic pathways, effectively influenced microbial activity, thereby reducing hydrogen production. Furthermore, under favorable conditions, the inoculum can convert the solubilized compounds to alternative metabolic pathways, such as alcoholic fermentation, which is unfavorable for the hydrogen production [45, 47].
# Short-Range Transportation Using Unmanned Aerial Vehicles (UAVs) during Disasters in Japan
## 2 Materials and Methods
### Methods  
The selected UAV (M1000) was used to conduct a goods transportation trial. The chosen CardioLife@automatic external defibrillator (AED) equipment (Nihon Kohden Corp., Tokyo, Japan) (weight: 3.7 kg; size: \(44\times 26\times 10\) cm) was transported using a rope suspension method under a pilot's manual supervision and control. The DMAT equipment rucksack (weight: 17 kg), foodstuffs (100 servings of alpha rice) (weight: 13.8 kg; size: \(34\times 68\times 22\) cm), and 60 hardtack meals (weight: 6.8 kg; size: \(26\times 36\times 27\) cm) were also transported using this method.  
A simple blood glucose meter (Glutest-ai(r), Sanwa Kagaku Kenkyusho Co., Ltd., Nagoya, Japan) and a drug-integrated insulin injector (Insulin glargine Kit \"FFP\" formulation(Sanwa Kagaku Kenkyusho) were also transported. Next, a drug-integrated insulin injector in a simple refrigerator (Hexashi Portable Refrigerator(r), Taihe Technology Ltd., Shenzhen, China) was transported at low controlled temperatures. Due to the light payload, these experiments involved automatic round-trip flights and automatic landing at the destination. Table 1 shows the schedule for the transport experiment, the location, and the transported goods.  
The UAV-assisted transportation was carried out 12 times. All of the goods except insulin were transported under visual flight conditions across straight-line distances of 30 to 100 m. The flight times included detours to avoid obstacles, ascending or descending from lowlands to plateaus, and detours along the flight route under the pilot's supervision until the goods were held static above the ground and carefully unloaded at the destination. The UAV was landed at the take-off point or indicated location. The auxiliary unit also performed visual UAV surveillance during the UAV flight, assisting the pilot with the positioning of the UAV during the UAV flight, loading and unloading, and landing at the final destination (Table 2).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Trial** & **Date** & **Place of Flight** & **Transported goods** \\ \hline
1 & 29 June 2019 & [PERSON] & Rucksack (DMAT Equipment) \\
2 & 6 July 2019 & [PERSON] & Rucksack (DMAT Equipment) \\
3 & 26 July 2019 & [PERSON] & Rucksack (DMAT Equipment) \\
4 & 26 July 2019 & [PERSON] & Rucksack (DMAT Equipment) \\
5 & 22 September 2019 & [PERSON] & Self-injection set. (insulin+glucometer) \\
6 & 5 October 2019 & [PERSON] & Insulin in the refrigerator \\
7 & 20 October 2019 & [PERSON] & Insulin in the refrigerator \\
8 & 28 December 2019 & [PERSON] & Hardtack (disaster relief food) \\
9 & 1 January 2020 & [PERSON] & Pregelatinized rice (disaster relief food) \\
10 & 15 February 2020 & [PERSON] & Hardtack (disaster relief food) \\
11, 12 & 4 April 2020 & [PERSON] & Automatic external defibrillator (AED) \\ \hline \hline \end{tabular} Abbreviations: Tomi: Nishiri-ku, Tomi City, Nagano, Japan; Fujisawa: Endo, Fujisawa City, Kanagawa, Japan; DMAT: Disaster Medical Assistance Team.  
\end{table}
Table 1: Flight schedule and study design.
## 2 Experiment Setup and Simulation Model
### Simulation Models
#### 2.2.1 Intake and Exhaust System  
As shown in Figure 2, the intake system consists of a filter, fire barriers, compressors, intercoolers, throttle, intake runners, intake ports, intake valves, and those pipes connecting the objects in the system. Likewise, the exhaust system includes exhaust valves, exhaust ports, turbines, exhaust runners, mufflers, and the pipes necessary to connect these objects. The specific geometry of the pipes, runners, ports, fire barriers, and intercoolers in the intake and exhaust systems was set according to the actual engine specification. The discharge coefficients of the intake and exhaust valve versus the lift profile were provided by the engine manufacturer. The flow resistance, discharge coefficients, and heat transfer coefficients of other parts were calibrated carefully according to the experimental data, including the pressure and temperature in the manifold, and the mass flow rate of air and natural gas.  
The turbine converts part of the thermal energy of the exhaust gas into air compression energy, which results in the rising of air pressure and temperature. There is a special sub-model to simulate the operation of the turbocharger and compressor. The compressor objective modeled by a compressor MAP consisting of the pressure ratio, mass flow rate,  
Figure 1: Schematic of the experimental setup.  
and efficiency calculated the air mass flow rate, speed, outlet temperature, and pressure and consumed power at a given compressor speed and input power. Likewise, the turbine objective modeled by a turbine MAP consisting of the pressure ratio, mass flow rate, and efficiency calculated the output power according to the mass flow rate of exhaust gas, speed, temperature, and pressure of the inlet and outlet.
## 3 Results and Discussion
### Estimation of Polyphenols from Culinary Herbs and Spices
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variables** & **TPC (mg GAE/g)** & **TFC (mg QE/g)** & **TCT (mg CE/g)** \\ \hline Basil leaves & 26.99 \(\pm\) 1.28 b & 9.35 \(\pm\) 0.16 b & 3.97 \(\pm\) 0.10 b \\ Fennel seeds & 9.90 \(\pm\) 0.27 d & 2.48 \(\pm\) 0.21 d & 2.88 \(\pm\) 0.10 b \\ Coriander seeds & 23.02 \(\pm\) 1.53 b & 3.35 \(\pm\) 0.26 d & 11.51 \(\pm\) 1.68 a \\ Mint leaves & 125.42 \(\pm\) 10.89 a & 31.72 \(\pm\) 1.22 a & 3.31 \(\pm\) 0.08 c \\ Fenugreek leaves & 17.21 \(\pm\) 1.08 c & 4.41 \(\pm\) 0.08 c & 1.36 \(\pm\) 0.08 d \\ \hline \hline \end{tabular}  
* According to this table given values mean \(\pm\) SD per dried weight where number of replicates per sample was 3 (n = 3). Values in each column having different superscript letters (“e”) are profoundly distinct from one another at \(p<\) 0.05. GAE (gallic acid equivalents); CE (cateltein equivalents); QE (quercetin equivalents).  
\end{table} 
Table 2: TPC, TFC and TCT of selected culinary herbs and spices. 2.48 \(\pm\) 0.21 mg QE/g, respectively. When compared to previously reported TFC values of nutmeg (2.22) and black cumin (1.73), the TFC values of fenugreek leaves, fennel seeds, and coriander seeds were found to resemble [1]. Herbs possess phenolic acids as well as flavonoids in higher proportions in comparison with other phenolic compounds. In contrast, the TCT value of coriander seeds (11.51 \(\pm\) 1.68 mg QE/g) was noticeably greater than other mentioned culinary spices and herbs; for instance, TCT value for basil leaves (3.97 \(\pm\) 0.10 mg QE/g), mint leaves (3.31 \(\pm\) 0.08 mg QE/g), fennel seeds (2.88 \(\pm\) 0.10 mg QE/g) and the lowest TPC value were observed to be of fenugreek leaves (1.36 \(\pm\) 0.08 mg QE/g). The two most significant subgroups of phenolic compounds are flavonoids and tannins. Among phenolic compounds, flavonoids have earned more consideration due to their potential as antioxidants and function as a nutritional indication for many food items. Therefore, herbs' characterization with modern analytical techniques, for instance, LC-ESI-QTOF-MS/MS, can offer more reliable, accurate, and useable information on bioactive compounds, including polyphenols for applications in animal feed and human food and cosmetic industries that belong to pharmaceutical.
# Privacy-Preserving Solutions in Blockchain-Enabled Internet of Vehicles
## 6 Privacy-Preserving Blockchain-Based Solutions
### Data Exchange
A Trust Authority (TA) is responsible to register Vehicles (users) with a unique VIN code. This code is the vehicle's one-time multiple pseudonym to protect its real identity, allowing it to participate in the network and enjoy its benefits. Moreover, to boost privacy, nearby vehicles will form a cluster and a cluster leader will be selected dynamically by smart contract and identity-based aggregate signature. The cluster leader will take the role of proxy between the cluster and the RSUs scattered across the road. Similar, to vehicle clusters, RSUs will form their cluster named Edge Services Cluster (ESC) where the permissioned blockchains are deployed; these clusters are providing computing power and temporary storage capabilities to vehicles. The ESCs are manageable by TA via a cloud server in the cloud layer. In the vehicular layer, vehicles will exchange information between them and the RSUs; all transmissions will be stored in RSU blockchain in the edge layer. Finally, TA will keep a reputation system of the vehicles and has the ability to track the real identity of malicious vehicles. Based on simulation experiments, the authors simulate their proposal on OMNet++ and prove that it meets security requirements and is a feasible solution in IoV.