esa-sceva/satcom-chunk-collection huggingface dataset:
# IoT Based Smart City Bus Stops
## 1 Introduction  
This paper proposes an Internet of Things (IoT) based solution to improve the services of smart bus stop operations and maintenance in the United Arab Emirates (UAE). In recent years, the transport authority of Dubai has built around 100 air-conditioned smart bus-shelters [1]; however, the \"smart\" aspect of these bus stops does not provide a means of remotely monitoring bus stop utilities and malfunctions. Authorized operators periodically check the bus stop's status as well as ask bus stop users to report faulty air conditioners (ACs) by calling a helpline [2]. An ideal solution is to minimize response time by automatically detecting malfunctions and instantly reporting them to the authorities. Additionally, officials recognized the need for energy-efficient bus stops and planned to install solar panels to power them.  
It would be ideal to utilize smart technology to contribute to this initiative of conserving energy by regulating the ACs and the lights' energy consumption based on real-time decisions made by analyzing the readings acquired from status and environmental monitoring sensors.  
In an attempt to enhance existing bus stops, our proposed system's objective was to develop an efficient sensor array unit to collect, process, and distribute data related to the bus stop operational andenvironmental parameters in order to operate the bus stop efficiently. The proposed system uses a mobile app that enables users to view current bus stop conditions on Google Map. An added benefit of the proposed system is that by keeping an estimated track of the number of people at the bus stop, the system can turn off the lights and AC during hours of vacancy, thus leading to significant energy conservation.  
In the past, maintenance used to be considered difficult to conduct, however, with recent technological advances, it is no longer an issue [3]. An example of such an advancement includes the concept of IoT technology where people, processes, and things are connected [4]. Similarly, our proposed system reflects the aspect of 'things' by making use of sensors and actuators. The 'process' aspect of the proposed system is processing the generated and collected data in order to make appropriate decisions to command actuators accordingly. Whereas, the 'people' aspect refers to the maintenance personnel and the general public.  
The rest of the paper is organized as follows. The literature review is presented in Section 2. Section 3 discusses the proposed hardware architecture. Section 4 discusses the proposed software architecture. Section 5 presents the implementation and testing of the prototype. The last section concludes the paper and presents ideas for future work.
## 3 Microfabrication of Suspensions of Fibers by Colloidal Self-Assembly
### Control of Fiber Properties
#### 3.3.2 Fiber Position, Orientation and Concentration  
When applying the magnetic field, the vertical columns formed by the assembled colloids show a strong ordering: the dipolar magnetic interactions between columns is repulsive and the equilibrium distance, \(d\), between columns is only determined by their length, \(L\), and \(d=1.33L^{0.37}\)[20]. One can see the strong ordering of the filaments, showing a constant distance between chains in top left image of Figure 7. This ordering leads to a homogeneous fiber concentration and random orientation once fibers have sedimented to the bottom of the channel (see Figure 7). One can however take advantage  
Figure 7: Successive photographs of the channel from above starting when the magnetic field is removed until the end of the sedimentation of fibers to the bottom of the channel. The channel height (and fiber length) is 28 μm. A video of these fabrication steps is available in Supplementary Materials Video S3.  
of the fact that the formed fibers are magnetic and can use a magnetic field to orient the fibers in the desired direction (see Figure 8).  
The maximum concentration of filaments is set by the above-cited relationship between \(L\) and \(d\). Below this limit, the fiber concentration in the suspension can be adjusted by tuning the initial volume fraction of colloids. We illustrate this in Figure 9 showing pictures of fiber suspensions fabricated at three volume fractions both during the fabrication (top row) and after sedimentation (bottom row). The channel geometry used for the fabrication is the same for all three bottom pictures with a height of 24 \(\upmu\)m.
## 3 Results and Discussion
### Carotenoids Composition
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Rosehip** \\ **Species** \\ **Carotenoid** \\ \end{tabular} } & \multicolumn{6}{c}{**Carotenoid Composition, mg 100 g\({}^{-1}\) DW**} \\ \cline{2-9}  & \multicolumn{2}{c}{**Total**} & \multicolumn{2}{c}{**\(\beta\)-Caroten**} & \multicolumn{2}{c}{**\(\alpha\)-Carotene**} & \multicolumn{2}{c}{**Lutein**} & \multicolumn{2}{c}{**Zeaxanthin**} & \multicolumn{2}{c}{_Cis-Lycopene_} & \multicolumn{2}{c}{_Trans-Lycopene_} \\ \hline \multicolumn{9}{c}{In rosehips flesh} \\ \hline RRA & 25.29 \(\pm\) 0.54 b & 16.48 \(\pm\) 0.42 b & 2.32 \(\pm\) 0.11 b & 3.87 \(\pm\) 0.13 b & 0.27 \(\pm\) 0.02 b & 0.59 \(\pm\) 0.02 b & 1.76 \(\pm\) 0.02 b \\ RRR & 16.58 \(\pm\) 0.58 c & 9.40 \(\pm\) 0.43 d & 2.24 \(\pm\) 0.06 b & 2.68 \(\pm\) 0.17 c & 0.25 \(\pm\) 0.01 c & 0.49 \(\pm\) 0.01 d & 1.52 \(\pm\) 0.05 c \\ RR & 18.07 \(\pm\) 0.93 c & 12.71 \(\pm\) 1.07 c & 1.26 \(\pm\) 0.11 c & 2.68 \(\pm\) 0.15 c & 0.24 \(\pm\) 0.005 cd & 0.25 \(\pm\) 0.01 e & 0.93 \(\pm\) 0.02 d \\ RC & 8.67 \(\pm\) 0.93 d & 3.95 \(\pm\) 0.80 e & 0.80 \(\pm\) 0.12 d & 1.55 \(\pm\) 0.08 d & 0.25 \(\pm\) 0.003 d & 0.55 \(\pm\) 0.03 c & 1.59 \(\pm\) 0.09 c \\ RV & 49.51 \(\pm\) 1.04 a & 31.40 \(\pm\) 0.22 a & 6.11 \(\pm\) 0.35 a & 6.06 \(\pm\) 0.56 a & 0.32 \(\pm\) 0.02 a & 1.44 \(\pm\) 0.03 a & 4.18 \(\pm\) 0.04 a \\ \hline \multicolumn{9}{c}{In rosehips seeds} \\ \hline RRA & 0.62 \(\pm\) 0.004 c & - & - & - & 0.21 \(\pm\) 0.004 c & 0.13 \(\pm\) 0.001 a & 0.28 \(\pm\) 0.001 b \\ RRR & 1.19 \(\pm\) 0.09 a & - & 0.20 \(\pm\) 0.07 a & 0.31 \(\pm\) 0.01 b & 0.22 \(\pm\) 0.006 b & 0.14 \(\pm\) 0.01 a & 0.32 \(\pm\) 0.005 a \\ RR & 0.55 \(\pm\) 0.07 c & - & - & - & 0.23 \(\pm\) 0.004 a & 0.06 \(\pm\) 0.07 b & 0.26 \(\pm\) 0.004 c \\ RC & 0.58 \(\pm\) 0.005 c & - & - & - & 0.21 \(\pm\) 0.004 c & 0.13 \(\pm\) 0.001 a & 0.24 \(\pm\) 0.001 d \\ RV & 1.07 \(\pm\) 0.03 b & - & 0.04 \(\pm\) 0.01 b & 0.40 \(\pm\) 0.02 a & 0.22 \(\pm\) 0.007 b & 0.13 \(\pm\) 0.001 a & 0.28 \(\pm\) 0.007 b \\ \hline \multicolumn{9}{c}{In the same column, different small letters represent significant differences between flesh samples and seed samples} \\ \multicolumn{9}{c}{In the same column, different small letters represent significant differences between flesh samples and seed samples} \\ \multicolumn{9}{c}{species, respectively (_p_! 0.05). RRA: _Rosa rugosa_ ‘Alba’; RC: _Rosa carinin_; RR: _Rosa rugos_; RRR: _Rosa rugos_ ‘Rubra’; RV: _Rosa villosa_.  
\end{table} 
Table 1: Carotenoid composition and content (mg 100 g\({}^{-1}\) dry weight (DW)) of different organic rosehip species flesh and seeds. contents ranged between 4.00 \(\upmu\)g g\({}^{-1}\) (_R. webbiana_) and 26.80 \(\upmu\)g g\({}^{-1}\) FW (_R. damascena_) and between 1.02 \(\upmu\)g g\({}^{-1}\) (_R. moschata_) and 8.70 \(\upmu\)g g\({}^{-1}\) FW (_R. canina_), respectively. According the authors of this study, the variations of carotenoids level among the species of Rosa fruits could be attributed to species, climatic conditions, degree of maturation, and harvest time [27].
# On Modeling Eavesdropping Attacks in Underwater Acoustic Sensor Networks +
###### Abstract  
The security and privacy of underwater acoustic sensor networks has received extensive attention recently due to the proliferation of underwater activities. This paper proposes an analytical model to investigate the eavesdropping attacks in underwater acoustic sensor networks. Our analytical framework considers the impacts of various underwater acoustic channel conditions (such as the acoustic signal frequency, spreading factor and wind speed) and different hydrophones (isotropic hydrophones and array hydrophones) in terms of network nodes and eavesdroppers. We also conduct extensive simulations to evaluate the effectiveness and the accuracy of our proposed model. Empirical results show that our proposed model is quite accurate. In addition, our results also imply that the eavesdropping probability heavily depends on both the underwater acoustic channel conditions and the features of hydrophones.  
security; eavesdropping; underwater acoustic sensor networks; isotropic hydrophones; array hydrophones +
Footnote †: _Sensors_  
Footnote †: _Sensors_**2016**, 16, 721; doi:10.3390/s16050721
# Satellite IoT Edge Intelligent Computing: A Research on Architecture
## 5 Conclusions  
Satellites have become an important part of the Internet of Things. This paper analyzes the current status and future development trends of satellite IoT networks. Aiming at the problem of satellite IoT data processing, this paper proposes the satellite IoT edge intelligent computing architecture based on the latest development of edge computing and deep learning. Among them, the satellite IoT edge computing and the distributed satellite IoT intelligent computing architecture are described in detail. Finally, this paper simulates and analyzes the connectivity and coverage performance of the satellite IoT and the performance of the satellite IoT edge intelligent computing architecture. By showing the performance of different neural network models in the satellite edge intelligent computing architecture, we can find that the lightweight neural network model can be more suitable for satellite IoT scenarios. The lightweight model of neural networks can promote the development of satellite IoT edge intelligent computing architecture.  
Conceptualization, [PERSON] and S.C.; methodology, [PERSON], [PERSON] and S.C.; validation, [PERSON]; investigation, [PERSON] and [PERSON]; writing--original draft preparation, [PERSON] and [PERSON]; writing--review and editing, [PERSON] and [PERSON]; visualization, [PERSON] and [PERSON]; supervision, S.C.; project administration, S.C.  
The project was supported by the National Natural Science Foundation of China (No. 617011484), the Research Fund of the manned space engineering (No. 18022010301), the Open Fund of IPOC (BUPT), and the National Defense Science and Technology Innovation Zone Project of China.  
The authors declare no conflict of interest.
## 4 Mathematical Description of the Principles of ED Using SLC in MIMO-OFDM Systems
### Mathematical Formulation of the Role of Noise Uncertainty in the ED Process
However, this scenario is the most challenging for simulation or practical implementation since it demands the continuous estimation and adaptation of DDT according to the NU level. In this case, NU and DDT parameters were used to model detection and false alarm probabilities. To model noise variance, bounds in the interval \(\sigma_{w\!NUD\!T}^{2}\varepsilon\left[\sigma_{w\!NUD\!T}^{2}/\!\!\!\rho^{ \prime}\ \rho_{w}^{2}\right]\) were used to model NU fluctuation, while to model the DDT adaptation boundaries, the interval \(\lambda^{\prime N\!NUD\!T}\varepsilon\left[\lambda_{d}^{N\!NUD\!T}/\!\!\rho^{ \prime},\ \rho^{\prime}\lambda_{d}^{N\!NUD\!T}\right]\) was used. The detection \(\left(P_{d}^{N\!NUD\!T}\right)\) and false alarm \(\left(P_{fa}^{N\!NUD\!T}\right)\) probability was given as follows:  
\[P_{fa}^{N\!NUD\!T}=\min_{\lambda^{\prime N\!NUD\!T}\varepsilon \  
\[\lambda_{d}^{NUDDT}=\left[Q^{-1}\Big{(}P_{d}^{NUDDT}\Big{)}\sqrt{\left(1+\frac{P \rho}{R\sigma_{\overline{w}}^{2}}\right)}+\sqrt{RN^{NUDDT}}\bigg{(}1+\frac{P\rho }{2R\sigma_{\overline{w}}^{2}}\bigg{)}\right]\sqrt{RN^{NUDDT}}\left(\frac{2 \sigma_{w}^{2}\rho^{\prime}}{\rho}\right) \tag{20}\]  
Furthermore, according to Equation (19), the false alarm threshold can be defined with respect of false probability as follows:  
\[\lambda_{fa}^{NUDDT}=\left[ \begin{array}{c}Q^{-1}\Big{(}P_{fa}^{NUDDT}\Big{)} +\sqrt{RN^{NUDDT}}\end{array} \right]\frac{2\rho\sigma_{w}^{2}}{\rho^{\prime}} \sqrt{RNN^{NUDDT}} \tag{21}\]  
According to Equations (20) and (21), the minimum number of sample points required for precise signal recognition can be determined according to Equation (22):  
\[N^{DDT}=\frac{\left[Q^{-1}\Big{(}P_{fa}^{PDDT}\Big{)}-\sqrt{\rho^{\prime 2}(1+2 \overline{\gamma_{SLC}})}Q^{-1}\big{(}P_{d}^{DDT}\big{)}\right]^{2}}{R[\rho^{ \prime 2}\overline{\gamma_{SLC}}+(\rho^{\prime 2}-1)]^{2}} \tag{22}\]  
The probability of detection can be defined with respect of the false alarm probability as  
\[P_{d}^{NUDDT}=Q\left(\frac{\frac{\rho}{\rho^{\prime}}Q^{-1}\Big{(}P_{fa}^{NUDDT }\Big{)}-\sqrt{RN^{NUDDT}}\Big{(}\rho^{\prime}\ \overline{\gamma_{SLC}}+\frac{\rho^{\prime}}{\rho}-\frac{\rho}{\rho}\Big{)}}{ \sqrt{\rho^{\prime}\Big{(}\frac{1}{\rho}+2\overline{\gamma_{SLC}}\Big{)}}}\right)\]  
\[=\left(\frac{\frac{\rho}{\rho^{\prime}}Q^{-1}\Big{(}P_{fa}^{NUDDT}\Big{)}- \sqrt{RN^{NUDDT}}\Big{(}\rho^{\prime}\ \overline{\frac{p}{2R\sigma_{w}}^{2}}+\frac{\rho^{\prime}}{\rho}-\frac{\rho}{ \rho}\Big{)}}{\sqrt{\rho^{\prime}\Big{(}\frac{1}{\rho}+\frac{p}{R\sigma_{w} ^{2}}\Big{)}}}\right) \tag{23}\]  
According to Equation (23), the NU and DDT parameters affect the sensing efficiency of the PU signals. The purpose of this work was to examine the influence of varying degrees of NU fluctuations and DDT adaptations on the precision of the ED process in the MIMO-OFDM system.
## 3 The Proposed Scheme
### Key Recovery Phase  
Access parties (including the client, security guard, and staff) need to register with the \(SCC\), and the public key \(Puk_{X}\) and private key \(Prk_{X}\) will be issued to the parties. If the parties lose their private key, they can send a key recovery request to the \(SCC\); the scenario is as follows:  
1. The access party sends a key recovery request to the \(SCC\), and the request message includes \(ID\) and details information.
2. The \(SCC\) receives the request, then checks the consistency of the information from the request message and smart contract automatically.
3. If the information from the accessing party is equal to the smart contract, then they re-register a new key pair from the \(BCC\) and update the new key pairs to the corresponding party's information structure.
4. The \(SCC\) responds with a new key pair to the accessing party.
5. The access party receives the new key pair and the key recovery is done.
## 2 Materials and Methods
### Bioassay for Campylobacter or Listeria Bactericidal Activity  
The dialysate and desalted batch adsorption fractions (as well as all subsequent column chromatography and isoelectric focusing fractions) were tested for their ability to interfere with the normal growth of _Campylobacter jejuni_ or _Listeria monocytogenes_ in a microplate assay, based on the method of [PERSON] et al. [51]. Briefly, a 0.1 mL aliquot of each fraction or dialysate was added to an equal volume of Bolton broth (_Campylobacter_) or TSB (_Listeria_) in the first row (Row A) of a sterile microtiter plate (Greiner, Frickenhausen, Germany). The wells in the remaining rows received 0.1 mL sterile Bolton broth or TSB. The Row A mixture was mixed by pipetting up and down 6 times, and 0.1 mL was subsequently transferred to Row B. The process was repeated to obtain a serial 1:2 dilution of the dialysate or fraction down the plate. After serial dilution, each well received 20 \(\upmu\)L of a washed C. _jejuni_ NCTC 11168 or _L. monocytogenes_ V7 culture. Positive control wells contained _C. jejuni_ or _L. monocytogenes_, 0.1 mL medium, and 0.1 mL 50 mM Tris pH 7.5 buffer, but no dialysate or fraction. Negative control wells contained 0.1 mL medium and 0.1 mL 50 mM Tris pH 7.5 buffer, but no _C. jejuni_ or _L. monocytogenes_. The microtiter plate was placed in a zip-lock bag and the bag was inflated with a microaerobic atmosphere (gas mix containing 10% CO\({}_{2}\), 5% O\({}_{2}\), and 85% N\({}_{2}\)). The bag with the plate was placed inside a glass tray and the tray was placed on a rotating incubator (New Brunswick Scientific, Edison, NJ, USA) at 42 \({}^{\circ}\)C with 120 RPM shaking for 18 h. The plate was subsequently removed from the bag, and the 600 nm optical absorbance of each well was recorded with a microplate reader (Tecan Infinite-200, Tecan, Salzburg, Austria). The levels of optical absorbance of the negative and positive control wells were compared to experimental wells to determine the end point dilutions for dialysate and the fractions. Identical experiments were performed with _L. monocytogenes_, except that the plate was incubated at 37 \({}^{\circ}\)C and an aquarium air pump (Topfin-4000, Topfin, Vietnam), pumping ambient air, was used in place of the microaerobic gas mix to inflate the bag.
## 3 Network Model and Problem Formulation
### Node Coverage Rate and Localization Optimization  
The received energy is estimated based on the node energy dissipated and the energy utilized in the estimation. Hence, the utilized energy is obtained with the 'z' number of bits having the received signal strength in terms of 'z' bits forming a reception of node positioning in the WSN given in Equation (7):  
\[RX_{E}=E_{\textit{utilised}}X&Z=\frac{E_{\textit{disisp}}X&Z}{E_{RSSI}} \tag{7}\]  
The sensor nodes and the corresponding nodes utilize the maximum amount of reception in the node strength. Equation (7) satisfies the allocated energy for utilization in the node optimization using cluster analysis.  
\[E_{\textit{Final}}=\sum_{i}E_{\textit{IN}}&+SN_{a} \tag{8}\]  
Figure 2: CH formation for node optimization with energy conservation.  
Mean Localization Error (LE) is calculated by the normalized Average LE and the range of sensor nodes transmission (Rt).  
\[\text{Mean. LE}=\frac{\frac{1}{\text{Tr}}\sum_{i=1}^{Tn}\sqrt{\left(Si-si\right)^ {2}+\left(Ni-ni\right)^{2}}}{Rt} \tag{17}\]
# Performability Evaluation and Sensitivity Analysis of a Video Streaming on Demand Architecture
## 4 SPN Models
### Base Model
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Element** & **Description** & **Server Semantic** \\ \hline \multirow{10}{*}{Places} & P\_Arrival & Wait for new requests & - \\  & P\_InputQueue & Wait for queue availability & - \\  & P\_Process1 & Process Front-End tokens & - \\  & P\_ProcessCap1 & Front-End token capacity & - \\  & Front\_ON & Front-End is on & - \\  & Front\_OFF & Front-End is off & - \\  & Back\_ON & Back-End is on & - \\  & Back\_OFF & Back-End is off & - \\  & P\_Transmit1 & Receives tokens from Front-End & - \\  & P\_Receiver & Transmit tokens to Back-End & - \\  & P\_Process2 & Process tokens of Back-End & - \\  & P\_ProcessCap2 & Back-End capacity & - \\  & P\_Transmit2 & Receives tokens from Back-End & - \\ \hline \multirow{10}{*}{Timed Transitions} & AD & Delay between arrivals & Single Server \\  & T\_Process1 & Transferring time & Infinite Server \\  & T\_Transmit1 & Transferring time & Single Server \\  & T\_Process2 & Transferring time to End\_Process & Infinite Server \\  & Front\_MTTR & Front-End repair time & Single Server \\  & Front\_MTTF & Front-End fail time & Single Server \\  & Back\_MTTR & Back-End repair time & Single Server \\  & Back\_MTTF & Back-End fail time & Single Server \\  & T\_Process2 & Transferring time to End\_Process & Infinite Server \\  & T\_Process2 & Transferring time to End\_Process & Infinite Server \\  & T\_Transmit2 & Transferring time to End\_Process & Single Server \\ \hline \multirow{2}{*}{Place Markings} & CF & Front-End cores capacity & - \\  & CB & Back-End cores capacity & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of Elements for the Petri Net Model. \begin{table}
\begin{tabular}{c c} \hline \hline
**Transition** & **Values** \\ \hline Front\_MTTR & 3 \\ Front\_MTTF & 22,869 \\ Back\_MTTR & 3 \\ Back\_MTTF & 22,869 \\ T\_Process1 & 100 \\ T\_Process2 & 100 \\ T\_Transmit1 & 100 \\ T\_Transmit2 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameter values used in the Base SPN model. taken from [12], with 3.0 h for the MTTR and 22,869.0 h for the MTTF. When the MTTF is reached, the token changes from Front_ON to Front_OFF, making it impossible for the machine to activate until its MTTR is reached. Performance Metrics  
The metrics computed by the suggested models are presented in this section: the mean reaction time of the entire system and availability. The MRT (see expression (1)) obeys [PERSON]'s law [22] and is given by the sum of the number of tokens being executed in each place multiplied by AD (Arrival delay). \[ \begin{split}\textbf{MRT}=(((E\{\#P\_Process1\})+(E\{\#P\_Proce\\ ss2\}))*AD)/1-(P\{(\#P\_InputQueue>0)\\ AND(\#P\_ProcessCap1=0)\})\end{split}  \tag{1}\]  
We can also calculate the availability metric through the probability of the Fron_end and Back_end machines being connected. The expression (2) shows the calculation obtained to calculate the model's availability. \[\textbf{A}=P\{(Front\_ON>0)AND(Back\_ON>0)\} \tag{2}\]
# EGCY-Net: An ELAN and GhostConv-Based YOLO Network for Stacked Packages in Logistic Systems
###### Abstract  
Dispatching, receiving, and transporting goods involve a large amount of manual effort. Within a logistics supply chain, a wide variety of transported goods need to be handled, recognized, and checked at many different points. Effective planning of automated guided vehicle (AGV) transportation can reduce equipment energy consumption and shorten task completion time. As the need for efficient warehouse logistics has increased in manufacturing systems, the use of AGVs has also increased to reduce working time. These processes hold automation potential, which we can exploit by using computer vision techniques. We propose a method for the complete automation of box recognition, covering both the types and quantities of boxes. To do this, an ELAN and GhostConv-based YOLO network (EGCY-Net) is proposed with a Conv-GhostConv Stack (CGStack) module and an ELAN-GhostConv Network (EGCNet). To enhance inter-channel relationships, the CGStack module captures complex patterns and information in the image by using ghost convolution to increase the model inference speed while retaining the ability to capture spatial features. EGCNet is designed and constructed based on ELAN and the CGStack module to capture and utilize hierarchical features efficiently in layer aggregation. Additionally, the proposed methodology involves the creation of a dataset comprising images of boxes taken in warehouse settings. The proposed system is realized on the NVIDIA Jetson Nano platform, using an Arducam IMX477 camera. To evaluate the proposed model, we conducted experiments with our own dataset and compared the results with some state-of-the-art (SOTA) models. The proposed network achieved the highest detection accuracy with the fewest parameters compared to other SOTA models.  
object detection; AGV; package recognition +
Footnote †: journal: Applied Sciences