esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Developing fast and accurate fluorescence detection technology of oil spill is significant for quantitative analysis in unexpected oil spill events. As the oil sample concentration increases, the fluorescence spectrum produces red-shift behavior, which seriously affects the quantitative detection of concentration. In this work, a three-dimensional concentration-emission matrix (CEM) was constructed by using a series of emission spectra with different levels of concentration at the excitation wavelength of 266 nm. The database is the interpolated CEM of six samples using bicubic interpolation in the concentration dimension. With matrix similarity matching, the database was used to achieve quantification of the concentration of oil samples. The recovery rates of prediction for test samples and weathering samples of six oil samples were between 86.8% and 116.11%, with relative errors of predictions ranging from 2.09% to 15.2%. The results show that this method can provide accurate quantitative determination of the concentration of different oil samples.  
concentration-emission matrix (CEM); red-shift behavior; quantitative detection +
Footnote †: journal: Applied Sciences
# Secure Convolution Neural Network Inference Based on Homomorphic Encryption
## 3 System Model, Encryption Scheme and Design Goals
### System Model  
We assume that in a common scenario, the data owner encrypts the data locally and then uploads it to a third party for calculation. The third party has a pre-trained model. The ciphertext is returned to the data owner, and then decrypted to restore the plaintext of the reasoning result. In the case where a third party is involved in the reasoning process, the data may encounter the risk of data theft such as interception and side channel attacks. This risk can be avoided through a multi-party secure computing protocol. However, what is more worrying is that the cloud service provider could exploit the data provided by data owners. This data may be sold, or used to carry out illegal activities. Homomorphic encryption, a cryptographic method, can effectively deal with this problem. The data owner uses the public key to encrypt the data, while the cloud can only obtain information in the form of ciphertext, and use a dedicated evaluation key (evaluation key) to perform limited operations on the ciphertext. This mode can guarantee the privacy of the data provider. Our reasoning system process is illustrated in Figure 2.
# Adapted Diffusion for Energy-Efficient Routing in Wireless Sensor Networks
## 1 Introduction  
### Energy-Efficient WSN Algorithms  
Most of the research on WSNs has concentrated on the design of energy- and computationally efficient algorithms and protocols.  
This paper introduces a new energy-efficient routing protocol which we model using an agent-based model (ABM) in the NetLogo environment where the nodes have changing energy levels. In our previous paper [1], we described a new ABM of the standard directed diffusion (DD) routing protocol [2]. Additionally, in our previous paper [1], we presented two new routing protocols, lazy diffusion (LD) and gradient diffusion (GD). These new protocols have the potential to be more energy efficient. However, there is no guarantee that the source would be found due to the random search being undertaken, and the energy depletion of the random nodes used to forward packets.
## 2 Proposed Methods
### Segmentation  
Watershed transformation is a region-based method [31]. The idea of watershed is based on visualizing an image into its topographical view. Figure 3 illustrates the topographic view of an ultrasound image in which the bright portions cover \"high\" altitudes and dark contain \"low\" altitudes. Every local minima of an image includes a hole through which water is supplied into various catchment basins. The catchment basins are filled up with water starting from the bottom (i.e., minima of lowest intensity), and continue to high until water level attains the top peak in the topography. Dams are built to avoid the joining of water originating from two or more local minima. Consequently, the topography is divided into regions, also called catchment basins parted by dams, known as watershed lines or watersheds. The quantity of objects that are n outcome of segmentation relies upon the quantity of local minima that occurs in the image. Thus, the existence of several local minima in the object creates the problem of over-segmentation.  
Figure 2: B-mode (brightness) image of the lesion, the image was reconstructed based on the set of radio-frequency (RF) signals (510 RF lines, 1824 samples in every line, and penetration depth = 35.76 mm), where white arrows indicate the lesion [26].  
Over-segmentation is the main drawback of watershed transformation. It creates many regions owing to the existence of specious minima. To avoid this dilemma, automatic markers are introduced earlier and they are employed inside the required regions. This process dictates the flow of water only to the basins related to each marker. This method is referred to as marker-controlled watershed transformation and it is a powerful technique for breast lesions segmentation [12; 31].  
The marker image is a binary image also named as marker function, comprising of either single or big marker regions along with true logical variables. Each marker specifies a particular position inside the segmentation function [12] that impose a region to be global minima of the topography through the minima imposition method [33]. Therefore, this technique eliminates all inappropriate minima that are not associated to the marked areas. The minima imposition is a morphological operator [33] and more detail about it can be found in the paper [12]. At last, the potential lesion margins are acquired by calculating the watershed transformation of the minima- imposed image, shown in Figure 4.  
Figure 4: (**a**) Radiologist outlining; (**b**) computerized segmentation.  
Figure 3: Sight of watershed transformation [32].
## 3 Methodology
### Synthesis of the Results  
From the analysis of the previous tables, the following main points can be emphasized:  
* Prevalence of predictive maintenance: 59% of the articles focus on predictive maintenance, making it the most explored technology.
* Environmental focus: the majority of articles highlight the environmental pillar of sustainability, with an emphasis on reducing environmental impacts.
* Economic sustainability: the economic pillar is also widely addressed, particularly in studies on automation and process optimization.
* Less focus on social sustainability: social sustainability is mentioned less frequently, indicating limited focus on this pillar.
* Predominance of case studies and reviews: 33% of the articles are case studies and 45% are reviews, reflecting the consolidation of practical and theoretical knowledge.
* Models and practical applications: 16% of the article's present models, reflecting the practical application of technologies for sustainability.
* Emerging technologies: automation and machine learning are linked to sustainability, focusing on efficiency and cost reduction.
## 3 Results and Discussion
### Questionnaire for Users without Disabilities  
During the first pilot testing session at the school, we dispensed two different questionnaires: one for the involved students and the other for teachers. The aim was to collect the impressions of users without disabilities on aspects regarding their perceptions of the ElGo system and its ability to include people with disabilities in sports and or recreational activities.  
All students considered ElGo suitable for entertainment and 20 out of 24 responded positively to the question about the suitability of the system for team play. ElGo was mainly considered a mean to give every student the same opportunities to play and its use for facilitating the participation of students with disabilities was primarily considered entertaining. Almost all students (23) deemed the involvement of peers with disabilities by means of ElGo important. Table 1 reports in detail the collected responses to multiple-choice questions. In the open-ended questions, the participating students (7) expressed a general satisfaction and belief that playing together with disabled peers is very important. In addition, we received some critics about the dummy visibility: we resolved this problem by putting a colored rubber stripe on the dummy border.  
Participating teachers agreed on the support provided by ElGo for team games and the possibility to use the system in activities of physical education at school. They pointed out that entertainment is the most important value to facilitate the participation of all students in educational activities. The usefulness of ElGo for socialization through sport and providing equal opportunities for all students were also reported. In open-ended questions, they underlined the importance of socialization and integration of people with  
Figure 8: Pictures of the second pilot testing session: (**a**) user and friends with ElGo system and (**b**) ElGo system setup.  
disabilities and one of them reported the issue related to the visibility of the dummy. The detailed results of closed-ended questions are shown in Table 14.  
Ultimately, the collected responses suggest a general appreciation of the ElGo system and show how it can be an effective instrument for the social inclusion of people with motor-skill disabilities in sports and recreational activities in real-life situations, for example at school, in a public park, or football ground.
# Integrated Optical Phased Arrays for Beam Forming and Steering
## 5 Material Platforms
### Si Photonics Platforms
#### 5.1.1 OPAs Based on the Si Platform
A 1D 512-element OPA with significantly low power consumption of \(<\)1 mW enabled a wide FOV of 56\({}^{\circ}\)\(\times\) 15\({}^{\circ}\) and \(<\)30 \(\upmu\)s high-speed 2D beam steering. A coherent 2D solid-state LiDAR based on this OPA could extract the velocity on diffusive targets at a range of near 200 m [11]. Based on p-i-n EO phase modulators, a fast OPA presented 2D beam steering at a frequency of 20 MHz. After calibration of the randomly distributed initial phases by interference, the OPA enabled an FOV of 8.9\({}^{\circ}\)\(\times\) 2.2\({}^{\circ}\) and a beamwidth of 0.92\({}^{\circ}\)\(\times\) 0.32\({}^{\circ}\)[44]. Si photonics provides a promising platform to realize high-yield the near-infrared (NIR) OPAs, and offers high-performance with competitive low-price for applications such as free-space communications, LiDAR, imaging, and biological sensing. Some issues remain to be further resolved, such as the strong nonlinear absorption of Si at high input powers.
# Emerging Industrial Internet of Things Open-Source Platforms and Applications in Diverse Sectors
## 2 Background
### Business Strategy and IT Alignment
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Attribute** & **LoRa [23]** & **SigFox [24]** & **Z-Wave [25]** & **Cellular Networks [26]** \\ \hline
**Governing standards** & LoRa Alliance Specifications & SigFox Protocol & Z-Wave Standard &
\begin{tabular}{c} Various (GSM/GPRS/EDCE, \\ UMTS/HSPA, LTE) \\ \end{tabular} \\ \hline
**Frequency bands utilized** & \begin{tabular}{c} Multiple (915 MHz in \\ Americas, 923 MHz in Asia) \\ \end{tabular} & \
\begin{tabular}{c} Varied (868 MHz in EU, \\ 902 MHz in USA) \\ \end{tabular} &
\begin{tabular}{c} 868-908 MHz \\ \end{tabular} & Standard cellular frequencies \\ \hline
**Type of network** & \begin{tabular}{c} Long-Range Wide-Area \\ Network (LoRaWAN) \\ \end{tabular} & \
\begin{tabular}{c} Low-Power Wide-Area \\ Network (LPWAN) \\ \end{tabular} &
\begin{tabular}{c} Wireless Personal Area \\ Network (WPAN) \\ \end{tabular} & Wide Area Network (WAN) \\ \hline
**Network topology** & Star & Star & Mesh & Not applicable \\ \hline
**Energy consumption** & Low & 10–100 mW & 2.5 mA, low consumption & Relatively high \\ \hline
**Data transmission speed** & 27 kbps & Up to 100 bps (Uplink), 600 bps (downlink) & 40 kbps & Not specified \\ \hline
**Operational range** & Up to 5 km & Urban: Up to 10 km; rural: up to 50 km & Indoors: 30 m; outdoors: 100 m & Extensive, several kilometers \\ \hline
**Security Features** & AES encryption & Basic security measures & AES-128 encryption & RC4 encryption \\ \hline
**Signal spreading method** & Not specified &
\begin{tabular}{c} Direct-Sequence Spread \\ Spectrum (DSSS) \\ \end{tabular} & Not applicable & DSSS \\ \hline
**Distinctive characteristics** & Long range, energy-efficient &
\begin{tabular}{c} Extended battery life, \\ cost-effective \\ \end{tabular} & Simplified protocol & Extended range capabilities \\ \hline
**Primary use cases** & Smart city infrastructure & Street lighting, energy & Home monitoring and control &
\begin{tabular}{c} Machine-to- \\ machine communication \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overview of standard protocols in IoT-edge communication. created, including Business Systems Planning (BSP) [28] and Information Systems Study and Information Engineering [29]. Alignment can be broadly described as the degree to which IT applications, infrastructure, and organizational bodies enable and shape business processes and strategies, as well as the techniques or approaches used to attain a certain level of alignment [30]. Alignment is a key factor in the decision to implement one solution over another.
# Design of Autonomous Mobile Robot for Cleaning in the Environment with Obstacles
## 5 Experiment Results
### Experiment 1  
In this experiment, one obstacle was placed at (2, 1) coordinates, the virtual robot starts from (0, 0) coordinates, and the real robot (cleaning robot) was placed at (1, 0) coordinates. Other settings included the following: \(c_{1}\) was 2, and \(c_{2}\) was 0.5, obstacle radius r was 0.23 m, tu was 0.5 s, and R was 0.7 m. The total experiment was performed for 140 s. The linear velocity for the virtual robot was 0.15 m/s, whereas the angular velocity was 0 rad/s. Persistent excitation was \(h(t,E_{y})=\phi(t)tanh(E_{y})\) where \(\phi(t)\) was a non-smooth pulse function of amplitude 0.5, 4 s period with the pulse width of 80 percent. Figure 5 shows the experimental results of this experiment. The scale of Figure 5a is 2 m for 1 unit of the \(x\)-axis and 0.5 m for 1 unit of the \(y\)-axis. Due to the scale, the circular obstacle looks elliptical. Figure 5a shows the paths of both the robots in the \(XY\) plot. The green color indicates the path of the virtual robot, which started from (0, 0) coordinates and traveled into a straight path. The robot starts from (0, 1), avoids the obstacle in its way, and converges to the desired trajectory in 45 s. Figure 5b shows the error in the \(x\)-coordinates as a function of time. As time passes, the controller helps the robot to achieve near-zero error in the \(x\)-axis. Figure 5c shows the error in the \(y\)-coordinates as a function of time, which also shows that error in the \(y\)-axis converges near zero, due to the controller. The error graph starts with 1 m in the negative direction, due to the initial setup of the robot concerning the virtual robot. The robot orientation error also tries to converge to zero, as shown in Figure 5d. The real-time control signal generated by the controller (linear velocity and angular velocity) for the robot can be shown in Figure 5e,f. However, in these plots, some high linear velocities and angular velocities are generated. In that case, the robot only  
Figure 4: Working diagram of real-time data exchange between PC and robot.  
Figure 3: The graphical form of control algorithm.  
moves to its top speeds. Freeze signals for the robot can be seen in Figure 5g, where zero means no freeze signal is generated and one unit shows that the robot is frozen. These are generated by the controller when the robot is in the potential field near the obstacle.
###### Abstract
Many scholars have conducted traffic conflict analysis using TTC [18; 19]. A method that combines TTC with a multi-level random parameter logistic regression model to calculate the collision risk between merging vehicles and surrounding vehicles was proposed by [PERSON] et al. [20]. However, traditional TTC can only be calculated when the speed of the following vehicle exceeds the leading vehicle. To address the limitation, some studies made relevant improvements based on the conventional TTC. [PERSON] et al. proposed the modified TTC (MTTC) to estimate the collision risk in situations when the following vehicle's speed is lower than the leading vehicle's speed [21]. [PERSON] et al. proposed the work zone time-to-collision (WTTC) based on TTC. This measure factored in the influence of speed limits in freeway work zones and constructed a rear-end conflict identification model based on WTTC and deceleration rate thresholds [22]. Other improved measures based on TTC include time-exposed TTC (TET), time-integrated TTC (TIT), and time-to-lane crossing (TLC) [23; 24]. These aforementioned measures accurately assess conflict risk from specific perspectives in the proximity of collisions. The proximity-based measures assume that proximity serves as a surrogate indicator for conflict severity [25]. However, this assumption is not applicable to reflect the severity of potential collisions. Some studies have adopted the consequences of the risk resulting from conflicts to define the severity of potential collisions. [PERSON] and [PERSON] utilized potential collision energy (PCE) to reflect the impact of a conflict via the potential collision energy [26]. This type of SSM can better reflect conflict severity. Moreover, some studies have proved that proximity-based measures are not suitable in congested road conditions and less organized traffic environments [27; 28]. Therefore, an effective SSM should reflect the correlation between conflicts and collisions, accurately assess the severity of potential collisions, and be applicable to complex road environments.
# HARE: Supporting Efficient Uplink Multi-Hop Communications in Self-Organizing LPWANs
## 2 State-of-the-Art  
There are increasingly more LPWAN competing technologies, each employing varied techniques to achieve long range and low power operation [11]. This section reviews the most representative examples of the two main types: _licensed_ and _unlicensed spectrum LPWANs_, and surveys their currently existing multi-hop approaches.
# Modeling and Analysis of a Generic Internal Cargo Airdrop System for a Tandem Helicopter
## 2 Internal Cargo Airdrop System
### Rotational Dynamics Descriptions  
Under the action of the linear dynamics of the cargo, the cargo will move to the point of tipoff, and it is going to rotate around the point of tipoff once the up-pitching moment of the cargo around the point of tipoff is generated. Thus, the basic equation of the rotational dynamics of the cargo can be written as follows:  
\[\ddot{\theta}_{\text{L}}=M_{\text{P}}/f_{\text{P}} \tag{5}\]  
where \(\theta_{\text{L}}\) is the rotational angle of the cargo around the point of tipoff, \(f_{\text{P}}\) the moment of inertia of the cargo around the point of tipoff, and \(M_{\text{P}}\) the up-pitching moment of the cargo around the point of tipoff in the cargo body axis system (\(\alpha x_{\rm I}y_{\rm L}z_{\rm L}\)), which can be deduced as follows:  
\[M_{\rm P}=-\sum_{\rm i=1}^{\rm N}\frac{1}{2}H\cdot\Delta F_{\rm L,x}^{\rm i}- \sum_{\rm i=1}^{\rm N}D_{\rm i}(t)\cdot\Delta F_{\rm L,z}^{\rm i} \tag{6}\]  
where \(H\) is the height of the cargo, and \(D_{\rm i}(t)\) the longitudinal distance between the center gravity of the ith discrete section of the cargo and the point of tipoff in the cargo body axis system (\(\alpha x_{\rm I}y_{\rm L}z_{\rm L}\)). It is determined by the cargo kinematics. \(M_{\rm P}<0\) indicates that the cargo has not yet moved to the point of tipoff, which is \(\theta_{\rm L}=0\). Thus, we can let \(M_{\rm P}=0\) in this circumstance.  
According to the parallel axis theorem of the moment inertia, \(J_{\rm P}\) is deduced as follows:  
\[J_{\rm P}=\sum_{\rm i=1}^{\rm N}\frac{1}{12}\rho_{\rm L}^{\rm i}\cdot\Delta I \cdot\left[\Delta I^{2}+\frac{5}{4}H^{2}+D_{\rm i}(t)\cdot D_{\rm i}(t)\right] \tag{7}\]
## 2 Materials and Methods
### Different Number of Directors  
For this study, four different cases were considered: namely, structures with 5 directors, 4 directors, 3 close directors and 3 spaced directors (Figure 9). This study was made in order to determine if the dimensions of the antenna could be reduced. Analyzing the S parameters (Figure 10), it can be concluded that the number of directors does not significantly influence the bandwidth of the antenna.  
As it was expected, the gain value is highest for the structures with the higher number of directors. In addition, the front to back ratio is lower when the number of directors is higher; thus, the study will continue with the improvement of the structure with 5 directors.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & & **Dielectric** & \\ \hline
**Dimension** & **FR4** & **RO3003** & **RO4003C** & **RO4350B** \\ \hline
0.51 mm & 4.0542 & 4.5328 & 4.46 & 4.37 \\
1.51 mm & 3.6917 & 4.2514 & 4.2152 & 4.1446 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The directivity peak values for the studied structures.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & & **Dielectric** & \\ \hline
**Dimension** & **FR4** & **RO3003** & **RO4003C** & **RO4350B** \\ \hline
0.51 mm & 13.75\% & 16.5\% & 14.9\% & 14.7\% \\
1.51 mm & 15.98\% & 15.32\% & 14.9\% & 14.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The bandwidth for the studied structures.  
Figure 9: The four structures analyzed with: (**a**) 5 directors; (**b**) 4 directors; (**c**) 3 close directors; (**d**) 3 spaced directors.  
From the bandwidth point of view, the structure with 3 spaced directors is better, but the difference is negligible; thus, the antenna which will be further optimized is the one with 5 directors.
# Sign Language Sentence Recognition Using Hybrid Graph Embedding and Adaptive Convolutional Networks
### Advanced Feature Extraction and Representation  
Build a sophisticated computer system that can interpret sign language using multi-dimensional, intricate features [6]. The primary objective is to create a hybrid method to understand sign language words with all their semantic nuances, temporal dynamics, and geographical links extracted. By combining graph-based embeddings with Adaptive Convolutional Networks, the study hopes to solve the problem of standard recognition restrictions and handle variations in signing velocity, individual styles, and environmental circumstances [7].