esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Different Droop Functions in Active Power Curtailment Method
#### 4.1.4 Discussion  
From the above results, the efficacy of altering droop function from the fixed value is demonstrated which maintains the voltage profile of the farthest houses at the upper boundary of the operating condition. The power curtailment for overvoltage prevention decreases for each house in such approach as shown in Table 1. The energy output from the PV inverters increases and energy curtailment decreases as the fixed droop is changed to a quadratic or exponential function. The energy curtailed in each house decreases in both quadratic and exponential droops resulting in reduced overall energy loss than in linear droop. This proves that altering the droop coefficient can reduce unnecessary power  
Figure 9: Voltage profile of twelve houses (House 1 to House 12) and curtailed power in APC method with quadratic droop.  
curtailments in PV inverters in APC technique. Further, the numerical results with the proposed control strategy that can achieve the same objective are demonstrated.
## 4 Applications of Graph Attention Networks
### Biomarker-Disease Association  
The identification of the relationship between molecular biomarkers and different types of ailments is of paramount importance in elucidating the underlying molecular mechanisms of the disease. Circular RNA (circRNA) is a distinctive class of non-coding RNA molecules that play a pivotal role in a diverse range of pathological conditions. They represent a critical component of the non-coding RNA repertoire, contributing significantly to the modulation of gene expression by sequestering microRNAs (miRNAs) in a regulatory manner [152]. Long non-coding RNA (IncRNA) is a class of RNA molecules that surpass a threshold of 200 nucleotides in length. They modulate the expression of genes at various stages of gene regulation, including transcriptional, RNA processing, translational, and post-translational levels, through intermolecular interactions with nucleic acids and proteins. These molecules serve as molecular guides that facilitate the recruitment of transcription factors to specific DNA binding sites. Additionally, lncRNAs can serve as decoys, which impede the binding of certain proteins to other proteins or nucleic acids [153]. MicroRNAs (miRNAs) are a class of small, single-stranded, non-coding RNA molecules that typically range from 21 to 23 nucleotides in length. These molecules are ubiquitously present in organisms such as plants, animals, and certain viruses. miRNAs play crucial roles in modulating RNA silencing and post-transcriptional regulation of gene expression. Specifically, they participate in regulating gene expression by interfering with the translation of messenger RNA molecules into functional proteins [154]. PIWI-interacting RNAs (piRNAs) are a category of small silencing RNAs that are exclusive to animals and are differentiated from other classes of small RNAs such as microRNAs (miRNAs) and small interfering RNAs (siRNAs). piRNAs function to suppress transposable elements, govern gene expression, and defend against viral infection. They serve as guides for PIWI proteins to recognize and cleave target RNA, and also play a critical role in the promotion of heterochromatin assembly and DNA methylation [155].  
Graph attention networks (GATs) have emerged as a promising tool for predicting associations between biomarkers and diseases [91]. Specifically, GATs have been utilized to predict disease associations with a diverse set of biomarkers, including circular RNAs (circRNAs) [49; 156], long non-coding RNAs (IncRNAs) [50; 58; 157; 40], microRNAs (miRNAs) [32; 85; 110; 158], PIWI-interacting RNAs (piRNAs) [159], and microbes [126; 160]. By leveraging the unique features and relationships of these biomarkers, GATs can effectively capture the complex patterns underlying biomarker-disease associations. These efforts have shown great promise in advancing our understanding of the complex interplay between biomarkers and disease states, and hold great potential for improving disease diagnosis, treatment, and prevention in the future.
# Full Real-Time Positioning and Attitude System Based on GNSS-RTK Technology
## 3 Material and Methods
### Communication Protocols
#### 3.1.3 RTCM Version 3  
This protocol is an international standard to publish GNSS data in real time. All high-precision GNSS receivers support this protocol.  
It provides a binary message like the UBX protocol, and it is protected by a CRC-24Q checksum (cyclic redundancy verification) with a very low probability of undetected errors [29].  
This protocol is mentioned because GNSS receivers use it to communicate with each other (see [30]), but it is not programmed for this prototype.
# Challenges, Threats, Security Issues and New Trends of Underwater Wireless Sensor Networks
## 2 Particularities and Constraints
### Transmission Loss  
During spreading, the energy of acoustic signal may be attenuated and absorbed by the medium. The transmission loss includes spreading loss and attenuation loss.  
Spreading loss (SL): this is the power loss during the spreading period from source node to destination node. In the spreading period, the acoustic wave front will occupy a larger and larger surface area, and therefore the wave energy in each unit surface becomes less and less. According to the source and working environment, the spreading power loss can be modeled by two methods, including spherical spreading and cylindrical spreading. As shown in Table 2, the acoustic wave loss model includes spherical wave loss model and cylindrical wave loss model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Long Range** & **Medium Range** & **Short Range** \\ \hline Communication Range & 20–2000 km & 1–10 km & \textless{}1 km \\ Working Environment & Deep Water & Shallow Water & Deep Water \\ Available Bandwidth & 500 Hz–10 kHz & 10–100 kHz & 100–500 kHz \\ Data Rate & \textless{}10 kb/s & \textless{}50 kb/s & \textgreater{}100 kb/s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Communication bandwidth.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Signal Source** & **Working Environment** & **Spreading Loss** \\ \hline Spherical Wave Loss Model & point source & deep water & proportional to the square of the distance \\ Cylindrical Wave Loss Model & long line source & shallow water & proportional to the distance \\ \hline \hline \end{tabular}
\end{table}
Table 2: Acoustic wave loss model.  
Spreading loss can be calculated with the following formula:  
\[SL=d\times 10\log r \tag{12}\]  
where \(d\) is the spreading factor that describes the loss model, and r is the range in meter.  
As shown in Table 3, the factor d is commonly set to 2 for spherical wave loss model, and set to 1 for the spherical wave loss model. But for a practical underwater application, the spreading loss is a hybrid of spherical and cylindrical spreading, where d is set to 1.5 [11].  
Attenuation loss (AL): during the propagation period, the energy of an acoustic wave would be converted to other forms (e.g., heat) and absorbed by the transmission medium. Moreover, the attenuation loss is dependent on frequency. Hence, the absorption coefficient \(a(f)\) can be used to express and calculate the absorption loss, and the f is the frequency of the acoustic wave. The \(a(f)\) can be expressed empirically, using [PERSON]'s formula as [12]:  
\[\left\{\begin{array}{c}10\log a(f)=0.11\frac{f^{2}}{1+f^{2}}+44\frac{f^{2}}{ 4100+f^{2}}+0.000275f^{2}+0.003f\geq 0.4\\ 10\log a(f)=0.002+0.11\frac{f^{2}}{1+f^{2}}++0.11f^{2}f<0.4\end{array}\right. \tag{13}\]  
where \(a(f)\) is in dB/km, and\(f\) is in kHz.  
The transmission loss \(TL\) can be calculated as follows [11]:  
\[TL=SL+AL=d\times 10\log r+r\times 10\log a(f) \tag{14}\]
# Challenges of Network Forensic Investigation in Fog and Edge Computing
## 2 Background
### Networking in Edge and Fog Computing
Networking in fog computing involves establishing and managing communication between various devices, sensors, fog nodes, and cloud resources. The use of local resources reduces the amount of data transferred to the cloud, but requires a continuous connection to the different devices on the edge side. Especially the fog nodes as intermediate points between devices and the cloud are critical for this connection. A fog node can be a single network device, like a router, a computational server or a resource-constrained device such as a set-top-box and access point [12]. Because of this diversity, the network capabilities of fog nodes differ. The correct location of these devices has a huge impact on the performance of the network [13]. The nodes are responsible for collecting data from devices, processing them locally, and forwarding relevant information to the cloud or other fog nodes for further analysis. Inadequate positioning of fog nodes results in high latency, low transfer rates because of long distances and time-consuming connections between the different devices and the nodes [14; 15].
# Combined Use of LoRaWAN Medium Access Control Protocols for IoT Applications
###### Abstract  
The low power wide area networks (LPWANs) based on the LoRaWAN standard are suitable for Internet of Things (IoT) applications that involve a large number of low-power devices distributed over large areas. The LoRaWAN standard imposes some limitations on end-device configuration, such as the medium access strategies to be adopted, which depend on the region in which the network operates. In particular, in Europe, according to the ETSI regulations, a LoRaWAN end-device can use either a pure ALOHA medium access control (MAC) protocol or a polite medium access technique based on Listen Before Talk (LBT) Adaptive Frequency Agility (AFA). The aim of this work is to investigate the combined use of the two MAC protocols in the same LoRaWAN network. In particular, the work presents a simulative assessment of a LoRaWAN network that combines the use of Pure ALOHA and LBT AFA in realistic scenarios, under different workloads, when they work in compliance with the ETSI regulations. The work provides quantitative information that can help the network designer choose which protocol is more suitable for achieving the desired performance.  
LPWAN; LoRaWAN; medium access control protocols; ALOHA; Listen Before Talk (LBT); Adaptive Frequency Agility (AFA); Internet of Things (IoT) +
Footnote †: journal: Applied Sciences
# Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data
## 3 Methodology
### Global Overflow for Irrigation Event Detection
Considering that the pre-processing of S1 images and applying the proposed method could be automatically performed with minimum human involvement, the irrigation event could then be detected about one hour after receiving the S1 images. Finally, the post NDVI filter (Section 3.6) is applied for each detected irrigation point in order to eliminate the ambiguity with soil work. When another NDVI image is acquired at \(t_{i+(20~{}to30~{}days)}\), the \(\Delta NDVI\) is calculated. Then, for each detected irrigation event, if \(NDVI_{ti}<0.4\) and \(\Delta NDVI\leq 0.1\), then this irrigation event is eliminated. This NDVI filter is a long-term real-time scenario because it requires obtaining a new NDVI value one month later. Therefore, this optical filter remains as a post processing of the obtained irrigation points. It is important to mention that the methodology is applied separately for the ascending (evening overpass) and descending (morning overpass) SAR acquisitions. In fact, morning and evening acquisitions could not be joined in one temporal series due to the presence of the diurnal variations between the two acquisitions. The diurnal variation is a result of the difference in the vegetation water content (VWC) between the morning and the evening. This difference in VWC causes high difference in the radar backscattering signal over vegetated plots between the morning and the evening acquisitions. Several studies have reported that \(\sigma^{0}\) in the morning overpass registers higher values than \(\sigma^{0}\) in the evening overpass [44; 45; 46]. Therefore, it was suggested to investigate separately each SAR temporal series acquired in the morning and evening.
# Structural Behaviour of Concrete Deep Beams Reinforced with Aluminium Alloy Bars
## 3 Results and Discussion
### Parametric Study
#### 3.2.4 a/d Ratio  
To be able to investigate the effect of the a/d ratio, a total of six AA RC beams were numerically tested. The tested parameters of the a/d ratio varied from 1.14 to 3.43, indicating that both deep and slender beams were examined. All beams had the same identical properties, such as cross-sectional dimensions, material properties, and loading conditions, but had different a/d ratios (see Figure 13). Both tension and compression reinforcement consisted of four 14 mm diameter AA bars, and shear reinforcement consisted of 4 mm diameter AA bars at a space of 100 mm. The concrete strength was 20 MPa for all numerically tested beams. The examined shear spans (a) were 200, 250, 350, 400, 500, and 600 mm, which resulted in varied a/d ratios from 1.14 to 3.43.  
The scatter plot of Figure 14 effectively illustrates the relationship between shear force (kN) and the a/d ratio. The inverse correlation, which was obtained from a parametric study conducted by FE analyses, was aligned with the fundamental shear mechanism in RC beams [32], where increasing the a/d ratio typically instigated a reduction in shear force capacity because of the transition from a shear to a flexure failure mode. The AA beam with an a/d ratio of 1.14 failed at a shear force of 104.4 kN. This shear strength capacity afterwards decreased with an increasing a/d ratio of 3.43. As can be seen in Figure 14, the  
Figure 12: Shear strength versus effective depth.  
shear force capacity decreased by up to 43.9 kN. The findings support and are similar to the general shear mechanism in RC beams reinforced with steel or FRP bars [12; 32; 52].  
Figure 15 compares AA RC beams presented in this study with steel-RC beams tested by [PERSON] [46] and [PERSON] [53] in terms of the effect of the a/d ratio. As can be seen in Figure 15, the variation of shear stress with an a/d ratio for AA beams is identical to those of RC beams reinforced with steel bars. The shear stress decreased from 4.97 MPa to 2.09 MPa with the a/d ratio increasing from 1.14 to 3.43. A similar trend was also observed in steel-RC beams. For example, the shear stress decreased from 7.68 MPa to 1.37 MPa with an increase in the a/d ratio from 1 to 4 for the beams tested by [PERSON] [46].  
Figure 14: Effect of a/d ratio.  
Figure 13: Investigation of a/d ratio: (**a**) cross-section; (**b**) elevation.  
The identical response in variation of shear stress with an a/d ratio for AA and steel-RC beams was consequently observed. AA RC beams can provide better performance in corrosive conditions.
# Numerical and Experimental Analysis of Torsion Springs Using NURBS Curves
## 5 Conclusions  
This study applied nonuniform-rational-basis-spline (NURBS) curves for the design of torsion springs, analyzed the displacements of these springs using finite-element analysis, and verified the design of these springs through experimentation.  
(1) A method was proposed for deriving the coordinates of a control point for shifted elements by applying the inverse method on the basis of data derived through finite-element analysis. In addition, the relationship between the movement of the control point and stiffness matrix was identified and formulated by varying the torsion-spring parameters.  
(2) A method was proposed for deriving the torsion-spring shape by converting the torsion-spring displacement measured through experiments into coordinates that could be expressed using NURBS curves.  
(3) Comparative analyses between the results of the proposed analytical method and the experiment measurements demonstrated that the proposed method is valid within a satisfactory range of error.  
Y.S.K., Y.J.S., and E.S.J. conceived and designed the experiments; Y.S.K., Y.J.S., and E.S.J. performed the experiments; Y.S.K., Y.J.S., and E.S.J. analyzed the data; Y.S.K., Y.J.S., and E.S.J. contributed reagents/materials/analysis tools; and Y.S.K., Y.J.S., and E.S.J. wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest
# Location-Based Deferred Broadcast for Ad-Hoc Named Data Networking
## 2 Related Work
### Existing Work to Mitigate Broadcast Storm in Ad-Hoc NDN
A rapid traffic information dissemination scheme is proposed for VANET applications to disseminate traffic information rapidly [5; 26]. It inherits the ideas about broadcasting and overhearing from LFBL adding adjustments to the vehicular environments. Similar to LFBL, it has a collision-avoidance timer to avoid packet collision on MAC layer. It also uses a push timer to push traffic information rapidly. The timer is set based on the distance from the previous transmitter [27]. In particular, receivers farther from the previous transmitter will wait for a shorter time to forward the packet than closer receivers. During the waiting time, if a node receives an interest or data packet with the same name, it will check if the overhead packet is from the neighbor farther away from the origin. If so, it will cancel its own scheduled forwarding. Another work by [PERSON] et al. proposed a provider-blind forwarding strategy and a provider-aware strategy through a defer timer and overhearing to deal with flooding [28]. [PERSON] et al. proposed a forwarding strategy using the angle information to decide forwarding eligibility in addition to the distance when setting up the deferred timer [29].
# Mutual Authentication Protocol for D2D Communications in a Cloud-Based E-Health System
## 4 Proposed Protocol
### Health Center Upload Phase (HUP)  
It is considered an insecure channel for this phase. Its aim is the mutual authentication among entities to allow secure transmission of the patient's collected data, from the health center to the cloud server. The complete procedure is shown in Figure 2. The HUP phase starts when the user goes to the health center for a health inspection and receives a login and a password to access the patient's system in its mobile device. The patient can access his/her health information whenever wanted by inserting the login/password pair on his/her device.  
Step 1. The health center selects a \(TID_{H}\) and generates a random number \(R_{H}\). Then, it calculates \(MAC_{HC}=h_{2}(ID_{H}\parallel R_{H})\) and sends _Message 1_ = (\(TID_{H}\), \(R_{H}\), \(MAC_{HC}\)) to the cloud server with a timestamp \(T_{H}\).  
Step 2. After receiving _Message 1_ and \(T_{H}\) from the health center, the cloud server verifies if \(T_{H}\) is valid. If the verification fails, the procedure is terminated. Otherwise, the cloud server calculates \(MAC_{HC}\)' = \(h_{2}(ID_{H}\parallel R_{H})\) using the real identity of the health center received in the registration phase andthe random number received in _Message 1_. It then verifies if \(MAC_{HC}\)\({}^{\prime}=MAC_{HC}\). If the verification fails, the procedure ends because an intruder has been detected. Otherwise, the cloud server authenticates the health center, selects a random number \(R_{CH}\), calculates \(MAC_{CH}=h_{2}(ID_{H}\parallel R_{CH})\) and sends _Message 2_ = (\(MAC_{CH}\), \(R_{CH}\)) with a timestamp \(T_{C}\) to the health center.  
Step 3. The health center receives _Message 2_ and \(T_{C}\) from the cloud server and checks if timestamp \(T_{C}\) is valid. If the validation fails, the procedure ends. Otherwise, the health center calculates \(MAC_{CH}\)\({}^{\prime}=h_{2}(ID_{H}\parallel R_{CH})\) and verifies if \(MAC_{CH}\)\({}^{\prime}=MAC_{CH}\). If the verification fails, the procedure is terminated because an intruder has been detected. Otherwise, the health center authenticates the cloud server and generates the session key, \(K_{HC}=h_{3}(ID_{H}\parallel R_{H}\parallel R_{CH})\) and the session key validator, \(C_{HC}=h_{4}(K_{HC})\). It then uses the session key to encrypt the patient's report, \(M_{RP}=E_{KHC}\) (_Patient Report_, \(TID_{P}\), \(C_{HC}\)) and finally sends _Message 3_ = \(M_{RP}\) and a new timestamp \(T_{H}\) to the cloud server.  
Step 4. The cloud server receives {Message 3, \(T_{H}\)} and verifies \(T_{H}\). If the verification fails, it terminates the procedure. Otherwise, it calculates the session key \(K_{HC}=h_{3}(ID_{H}\parallel R_{H}\parallel R_{CH})\) and decrypts the patient's report, (_Patient Report_, \(TID_{P}\), \(C_{HC}\)) = \(D_{KHC}(M_{RP})\). It then calculates \(C_{HC}=h_{4}(K_{HC})\) and verifies if \(C_{HC}\)\({}^{\prime}=C_{HC}\). If the verification fails, it ends the procedure. Finally, the cloud server stores the patient's report with the respective identities.
# Efficient Implementation of PRESENT and GIFT on Quantum Computers
## 2 Related Work
### GIFT Block Cipher
#### 2.2.3 AddRoundkey of GIFT Block Cipher  
In the GIFT-64/128 block cipher, \(k_{0}\) and \(k_{1}\) (32-bit total) are selected from the key \((K=k_{7},...,k_{0})\). \(k_{0}\) and \(k_{1}\) are used as \(U\) and \(V\) of the round key as follows, \(RK=U||V=u_{15}...u_{0}||v_{15}...v_{0}\) (\(U=k_{1}\), \(V=k_{0}\)). The round key is exclusive-ored with the block \(B\), where \(U\) is XORed to \(b_{4i+1}\) and \(V\) is XORed to \(b_{4i}\).  
\[b_{4i+1}\gets b_{4i+1}\oplus u_{i},\ b_{4i}\gets b_{4i}\oplus v_{i},\ i=0,...,15 \tag{6}\]  
In the GIFT-128/128 block cipher, \(k_{0},k_{1},k_{4}\), and \(k_{5}\) (64-bit in a total) are selected from the key \(K\). \(k_{0}\), \(k_{1}\), \(k_{4}\) and \(k_{5}\) are used as \(U\) and \(V\) of the round key as follows,  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \(i\) & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 & 14 & 15 \\ \(P_{64}(i)\) & 0 & 17 & 34 & 51 & 48 & 1 & 18 & 35 & 23 & 49 & 2 & 19 & 16 & 33 & 50 & 3 \\ \hline \(i\) & 16 & 17 & 18 & 19 & 20 & 21 & 22 & 23 & 24 & 25 & 26 & 27 & 28 & 29 & 30 & 31 \\ \(P_{64}(i)\) & 4 & 21 & 38 & 55 & 52 & 5 & 22 & 39 & 36 & 53 & 6 & 23 & 20 & 37 & 54 & 7 \\ \hline \(i\) & 32 & 33 & 34 & 35 & 36 & 37 & 38 & 39 & 40 & 41 & 42 & 43 & 44 & 45 & 46 & 47 \\ \(P_{64}(i)\) & 8 & 25 & 42 & 59 & 56 & 9 & 26 & 43 & 40 & 57 & 10 & 27 & 24 & 41 & 58 & 11 \\ \hline \(i\) & 48 & 49 & 50 & 51 & 52 & 53 & 54 & 55 & 56 & 57 & 58 & 59 & 60 & 61 & 62 & 63 \\ \(P_{64}(i)\) & 12 & 29 & 46 & 63 & 60 & 13 & 30 & 47 & 44 & 61 & 14 & 31 & 28 & 45 & 62 & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Permutation of GIFT-64 bit.  
Figure 2: Encryption process of GIFT block cipher.  
\(RK=U||V=u_{31}...u_{0}||v_{31}...v_{0}\) (\(U=k_{5}||k_{4},V=k_{1}||k_{0}\)). The round key is XORed to the block \(B\), where \(U\) is XORed to \(b_{4i+2}\) and \(V\) is XORed to \(b_{4i+1}\).  
\[b_{4i+2}\gets b_{4i+2}\oplus u_{i},\ b_{4i+1}\gets b_{4i+1}\oplus v_{ i},\ i=0,...,31 \tag{7}\]
## 4 Design of Key Parts
### Design and Manufacture of the Transmission System  
The transmission system serves the purpose of transmitting engine power to the transplanting mechanism, enabling it to operate at the same speed as the transplanter's walking speed. Figure 14 shows the transmission system of the picking-type pot-seedling transplanting mechanism, which comprises the transmission box, transmission shaft, safety clutch, and level-gear pair. The gearbox provides support for both the transmission system and the seedling box. In the event that the transplanting mechanism becomes overloaded or comes into contact with stones in the field, the safety clutch is designed to automatically disengage power transmission, thus preventing any potential damage to the transplanting mechanism. Considering that the rice transplanter operates in a paddy field environment, the transmission box undergoes a rigorous process of hard aluminum-alloy casting and sealing, as depicted in Figure 15.