esa-sceva/satcom-chunk-collection huggingface dataset:
# On Beamforming with the Single-Sideband Transform
## 2 Frequency and Time-Frequency Transforms  
Hereafter, we assume that all time-domain signals are real-valued, which allows for shortcuts to some transforms and enables the use of others.  
For continuous time and frequency domains, the Fourier Transform (FT) of a time signal \(x(t)\) is defined as  
\[\begin{split} X_{\mathcal{F}}(f)&\equiv\mathcal{F} \{x(t)\}(f)\\ &=\int\limits_{-\infty}^{\infty}x(t)e^{-|2\pi ft}\mathrm{d}t\end{split} \tag{1}\]  
where \(X_{\mathcal{F}}(f)=X_{\mathcal{F}}^{*}(-f)\) if \(x(t)\) is real-valued, with \((\cdot)^{*}\) being the complex-conjugate operation.  
We define the Real Fourier Transform (RFT) similarly to the FT, constructed such that its frequency spectrum is real-valued without loss of information. The RFT is given by  
\[\begin{split} X_{\mathcal{R}}(f)&\equiv\mathcal{R} \{x(t)\}(f)\\ &=\sqrt{2}\mathrm{R}\left(\int\limits_{-\infty}^{\infty}x(t)e^{- |2\pi ft+\frac{3\pi}{4}}\mathrm{d}t\right)\\ &=\int\limits_{-\infty}^{\infty}x(t)\left[-\cos(2\pi ft)+\sin(2 \pi ft)\right]\mathrm{d}t\end{split} \tag{2}\]and the Inverse Real Fourier Transform (IRFT) as below (see Property A2 in Appendix A):  
\[\begin{split} x(t)&\equiv\mathcal{R}^{-1}\{X_{\mathcal{R }}(f)\}(t)\\ &=\sqrt{2}\text{R}\{\int\limits_{\ll\infty}^{\infty}X_{R}(f)e^{ \text{j}2\pi ft-\text{j}\frac{3\pi}{4}}\text{d}f\}.\end{split} \tag{3}\]  
The RFT can also be defined via the FT through a simple substitution of Equation (1) in Equation (2) (see Property A1 in Appendix A), resulting in  
\[X_{R}(f)=-X_{\mathcal{F}}^{\text{R}}(f)-X_{\mathcal{F}}^{\text{I}}(f). \tag{4}\]  
Using the fact that \(x(t)\) is a real signal, it is possible to achieve the following (Property A1, Appendix A):  
\[X_{R}(f) =\frac{1}{\sqrt{2}}\left(e^{\text{j}\frac{3\pi}{4}}X_{\mathcal{F} }(f)+e^{-\text{j}\frac{3\pi}{4}}X_{\mathcal{F}}(-f)\right) \tag{5a}\] \[X_{\mathcal{F}}(f) =\frac{1}{\sqrt{2}}\left(e^{-\text{j}\frac{3\pi}{4}}X_{\mathcal{ R}}(f)+e^{\text{j}\frac{3\pi}{4}}X_{\mathcal{R}}(-f)\right). \tag{5b}\]  
This means that the RFT can be defined in terms of the FT and vice-versa, forming a bijective relationship between the two transforms. This is only true if the original time-domain signal is real-valued, which is a requirement for the RFT to be invertible in the first place. A similar result was obtained previously (Equations (4) and (8) in [21]), where it was shown that the SSB representation depends on the complex-conjugate of the single-sideband modulated signal. Here, this complex-conjugate is represented by the negative frequency component, these being the same under the assumption of \(x(t)\) being real-valued.
# Traffic Congestion Detection System through Connected Vehicles and Big Data
## 3 Proposal Architecture
### Design of the Packet Structure  
The design of the packet structure used in both the OBU and the RSU to insure the proper flow of LORA-CBF, as well as the message capsule used by the big data Cluster to detect traffic, are described in the following tables.  
Table 1 shows the header and structure of protocol packets, which are encapsulated in a UDP datagram. This type of packet forms the basic structure for all the packets handled by the protocol.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Begin** & **Length** & **Type Send** & **RSSI** & **Reserved** & **Source Address** & **Destination Address** & **Payload** & **Checksum** & **Forceer** \\ \hline
7E & 00 & 08 & 01 & 1 Byte & 1 Byte & 4 Bytes & 4 Bytes & 0–1488 Bytes & 9D & 7D \\ \hline \hline \end{tabular}  
* **Begin**: a special character to indicate the beginning of the packet.
* **Length**: the number of bytes contained between the size data and the verification sum.
* **Transmission method**: the manner in which the packet is sent (Multicast, Broadcast, Unicast).
* **Source Address**: the address of the sending node (changes with each jump).
* **Destination Address**: the address of the receiving code (changes with each jump).
* **RSSI**: the received signal intensity (dBm).
* **Payload**: the content of the packet with a maximum size of 1488 bytes.
* **Checksum**: a number for checking the packet’s integrity.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Begin** & **Length** & **Type Send** & **RSSI** & **Reserved** & **Source Address** & **Destination Address** & **Payload** & **Checksum** & **Forceer** \\ \hline
7E & 00 & 08 & 01 & 1 Byte & 1 Byte & 4 Bytes & 4 Bytes & 0–1488 Bytes & 9D & 7D \\ \hline \hline \end{tabular}  
* **Begin**: a special character to indicate the beginning of the packet.
* **Length**: the number of bytes contained between the size data and the verification sum.
* **Transmission method**: the manner in which the packet is sent (Multicast, Broadcast, Unicast).
* **Source Address**: the address of the sending node (changes with each jump).
* **Destination Address**: the address of the receiving code (changes with each jump).
* **RSSI**: the received signal intensity (dBm).
* **Payload**: the content of the packet with a maximum size of 1488 bytes.
* **Checksum**: a number for checking the packet’s integrity.  
\end{table}
Table 1: LORA-CBF Packet Structure.  
Figure 10: Construction of the syntactically aggregated data.  
* **Forcer**: a byte to force the 802.11 transceptor to send the datagram and it doesn't wait to fill its maximum capacity.
# Optimization of Sensor Positions and Orientations for Multiple Load Case Scenarios
## 2 Materials and Methods  
Different load cases produce critical values of measured physical quantities (e.g., strain, temperature, acceleration, displacement, etc.) at different locations of the structure. This situation makes it difficult to accurately capture the exact maximal values for each load case using only a limited number of sensors. Therefore, optimization becomes necessary, as it enables a sensor placement strategy to achieve close approximations of realistic detection results. In sensor placement optimization, the sensor must be placed in some critical area of the structure domain [14; 25; 26]. As shown in Figure 1, sensors should be placed in key areas of the structure in order to detect the highest strain or temperature and to minimize errors between the detected and actual values. \(\mathbf{\Omega}\) is structure domain, \(\mathbf{\Omega_{s}}\) is the sensor placement area, and \(\mathbf{L}_{i}\) is the area of applied loads (thermal or structural).  
The presented optimization method can be applied to any type of sensor-measured physical quantity. In this paper, two cases are considered: temperature and strain measure  
Figure 1: Optimized sensor placement within the structural domain.  
ments. For each case, the approach for optimization of the sensor positions and orientations is presented. Both approaches utilize a directional interpolation and objective-function minimization approach, where sensor positions and orientations (azimuth and elevation angles) are iteratively optimized to minimize the error between the detected and maximum values (strain or temperature) under various load cases. But, unlike scalar temperature measurements, strain detection is direction sensitive. To account for this, the strain-based model incorporates azimuth and elevation angles, as well as sensitivity coefficients for orthogonal components. This enables more realistic simulation of FBG sensor or strain gage behavior. As a result, sensor orientation can be more accurately optimized, which is critical for capturing the directional characteristics of mechanical strain in 3D structures.  
In this study, strain and temperature optimizations were considered separately, but they can be also used together; however, sensors should allow for the separation of both values in computations (for example, the FBG sensor with areas with and without structural loads should be present, or a mix of temperature and strain sensors). The presented approach with separated fields is for clearer validation of sensor placement strategies in each field, where strain is orientation-sensitive and temperature is just scalar. In cases where both mechanical and thermal loads act simultaneously, coupled thermo-mechanical analysis could better capture real-world interactions. The presented decoupled approach provides a controlled way to validate the optimization framework.
###### Abstract  
The fifth-generation (5G) cellular infrastructure is expected to bring about the widespread use of connected vehicles. This technological progress marks the beginning of a new era in vehicular networks, which includes a range of different types and services of self-driving cars and the smooth sharing of information between vehicles. Connected vehicles have also been announced as a main use case of the sixth-generation (6G) cellular, with ultimate requirements beyond the 5G (B5G) and 6G eras. These networks require full coverage, extremely high reliability and availability, very low latency, and significant system adaptability. The significant specifications set for vehicular networks pose considerable design and development challenges. The goals of establishing a latency of 1 millisecond, effectively handling large amounts of data traffic, and facilitating high-speed mobility are of utmost importance. To address these difficulties and meet the demands of upcoming networks, e.g., 6G, it is necessary to improve the performance of vehicle networks by incorporating innovative technology into existing network structures. This work presents significant enhancements to vehicular networks to fulfill the demanding specifications by utilizing state-of-the-art technologies, including distributed edge computing, e.g., mobile edge computing (MEC) and fog computing, software-defined networking (SDN), and microservice. The work provides a novel vehicular network structure based on micro-services architecture that meets the requirements of 6G networks. The required offloading scheme is introduced, and a handover algorithm is presented to provide seamless communication over the network. Moreover, a migration scheme for migrating data between edge servers was developed. The work was evaluated in terms of latency, availability, and reliability. The results outperformed existing traditional approaches, demonstrating the potential of our approach to meet the demanding requirements of next-generation vehicular networks.  
distributed edge computing; software-defined networks; VANETs; 5G; fog computing; microservices +
Footnote †: journal: Future internet  
02024/16/257
## 3 Main Results
### Guidance
#### 3.1.1 Estimations of Ocean Currents  
Before designing the kinematic controller, the unknown ocean currents need be identified precisely. The finite-time currents observer for \(V_{x}\) and \(V_{y}\) are designed as follows:  
Figure 2: The structure diagram of the path following controller.  
The virtual control \(\alpha_{y}\) is designed to cancel \(\theta_{y}\) asymptotically [10]  
\[\frac{U_{r}\alpha_{y}}{\sqrt{\left(y_{e}+\alpha_{y}\right)^{2}+\Delta^{2}}}=\hat{ \theta}_{y} \tag{18}\]  
Solving for \(\alpha_{y}\) provides a credible solution (the negative root) as follows  
\[\alpha_{y}=\frac{y_{e}\big{(}\hat{\theta}_{y}/U_{r}\big{)}^{2}-\hat{\theta}_{y }/U_{r}\sqrt{\Delta^{2}\left(1-\big{(}\hat{\theta}_{y}/U_{r}\big{)}^{2}\right) +y_{e}^{2}}}{1-\big{(}\hat{\theta}_{y}/U_{r}\big{)}^{2}} \tag{19}\]  
In this case, the boundedness of \(\hat{\theta}_{y}/U_{r}\) must be ensured such that \(\big{|}\hat{\theta}_{y}/U_{r}\big{|}<1\). Thus, the boundedness of the parametric ocean currents is defined such that \(\theta_{y}<M_{\theta}<U_{r}\).  
The desire target velocity \(U_{pp}\) is chosen to stabilize \(x_{e}\) as follows  
\[U_{pp}=U_{r}\cos\big{(}\psi-\gamma_{p}+\beta_{r}\big{)}+k_{1}x_{e}+\hat{ \theta}_{x} \tag{20}\]  
where \(k_{1}\) is a positive design parameter. Thus, the path variable update law can be obtained according to Equation (8):  
\[\dot{\theta}=\frac{U_{pp}}{\sqrt{\dot{x}_{d}^{2}+\dot{y}_{d}^{2}}} \tag{21}\]  
Therefore, substituting Equations (18) and (19) into Equation (17), we have  
\[\begin{cases}\dot{x}_{e}=-k_{1}x_{e}+\tilde{\theta}_{x}+y_{e}\dot{\gamma}_{p }\\ \dot{y}_{e}=-\frac{U_{r}y_{e}}{\sqrt{\left(y_{e}+\alpha_{y}\right)^{2}+\Delta^{ 2}}}+U_{r}\phi\left(y_{e},\tilde{\psi}\right)\tilde{\psi}+\tilde{\theta}_{y}- x_{e}\dot{\gamma}_{p}\end{cases} \tag{22}\]
## 2 Background
### Green Public Procurement in Japan  
Public procurement refers to governmental actors that purchase goods, services, or works from companies [2]. When a government develops purchasing policies in response to social, environmental, and economic concerns, this is referred to as sustainable public procurement (SPP) [7]. This generic term covers, for instance, ethical trade (i.e., social criteria), green procurement (i.e., environmental criteria), and local SME procurement (i.e., economic criteria). This study focuses on green public procurement (GPP), which is defined as \"a process whereby public authorities seek to procure goods, services and works with a reduced environmental impact throughout their life cycle when compared to goods, services and works with the same primary function that would otherwise be procured\" [8].  
GPP has an important role within environmental policies, for three key reasons. First, because of their considerable size, requests from governmental bodies for greener solutions cannot be neglected by market suppliers [9]. Second, these environmental demands from public actors may also set an example for the private sector and create markets for ecological goods, services, and works [10]. Finally, GPP might develop the general awareness of environmental issues [11].  
Japan has been acknowledged as an early adopter and leading country in GPP [12]. In 1995, the Japanese national government introduced the Action Plan for the Greening of Government Organizations, requiring all public bodies to develop their own environmental procurement policy [13]. In 2001, the Japanese Green Purchasing Law obligated all national institutions to buy green products, as specified by the law. The list of designated products included 101 items in 14 categories in 2001 and evolved into 287 items in 22 categories in 2023. Besides this green purchasing obligation, the law further requires the institutions to publicly report on their green purchasing activities and to adopt a plan-do-check-act cycle for their GPP. This means national institutions must follow an approach where they develop a GPP policy (i.e., plan), implement the policy (i.e., do), analyze the policy achievements (i.e., check), and improve the policy (i.e., act) [14]. As a result, in 2017, it was estimated that green purchasing by national organizations has reduced greenhouse gas emissions by approximately 34,570 tons of CO\({}_{2}\) emissions [15].
###### Abstract  
This research introduces Fortified-Edge 2.0, a novel authentication framework that addresses critical security and privacy challenges in Physically Unclonable Function (PUF)-based systems for collaborative edge computing (CEC). Unlike conventional methods that transmit full binary Challenge-Response Pairs (CRPs) and risk exposing sensitive data, Fortified-Edge 2.0 employs a machine-learning-driven feature-abstraction technique to extract and utilize only essential characteristics of CRPs, obfuscating the raw binary sequences. These feature vectors are then processed using lightweight cryptographic primitives, including ECDSA, to enable secure authentication without exposing the original CRP. This eliminates the need to transmit sensitive binary data, reducing the attack surface and bandwidth usage. The proposed method demonstrates strong resilience against modeling attacks, replay attacks, and side-channel threats while maintaining the inherent efficiency and low power requirements of PUFs. By integrating PUF unpredictability with ML adaptability, this research delivers a scalable, secure, and resource-efficient solution for next-generation authentication in edge environments.  
physical unclonable function; security-by-design; hardware-assisted security; edge computing; secure authentication; cybersecurity; machine learning; cryptography; authentication protocol; error detection; error correction
## 2 Antenna Design Methodology
### Antenna Element Miniaturization  
Multiple antenna elements are necessary to achieve the requirement of the high gain wireless transmission of CubeSats. Nonetheless, the nanosatellite structure is very tiny. Therefore, it is mandatory to reduce the size of the designed element to satisfy this need. The slot miniaturization technique was applied to the patch antenna. The fundament of this technique consists of slotting the patch. In this way, it is possible to have a longer perimeter of the patch, which allows it to resonate at a low frequency without increasing the element size. That is, by adding more slots, the perimeter of the patch increases, but the structure is reduced. Then, the optimization of the antenna dimensions with the CST Studio Suite(r) software was run. The details of the optimization strategy were as follows: the Particle Swarm Optimization algorithm was used with a swarm size of 30, a maximum number of iterations of 15, and a maximum number of solver evaluations of 451. The optimization used the following goals: a reflection coefficient of \(\Gamma\leq-10\) dB (40% of weight) at a 3.5 GHz frequency (60% of weight).  
Figure 2 shows the evolution process of the miniaturization technique with slots in the antenna element. It is possible to observe the modification of the size and shape of the element. Table 1 presents the gain of each case with the operating wavelength. Meanwhile, Figure 3 exhibits its operation as slots were added to the patch in terms of its reflection coefficient. The slots allowed the antenna element to be reduced in size and set to operate at the desired center frequency of 3.5 GHz rather than the originally designed antenna. The correct operation of the antenna can be observed below \(-10\) dB of the reflection coefficient as the number of slots is increased and the size is parameterized.  
Figure 1: Initial antenna element design.  
Figure 4 depicts the final optimized element, which resonates at 3.5 GHz. This element uses a Rogers TMM10 substrate with a permittivity of 9.2 and a thickness of 1.270 mm. This substrate was chosen due to its characteristics that allow for great miniaturization. The final dimensions of the proposed miniaturized antenna element are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No. Slots** & **0** & **2** & **4** & **5** & **Final Element** \\ \hline Gain (dBi) & 5.344 & \(-\)7.706 & 2.595 & 1.993 & 1.493 \\ Size (\(\lambda\)) & 0.221 & 0.221 & 0.14 & 0.14 & 0.1352 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Gain values obtained via the electromagnetic simulation of each analyzed design case of slots in the antenna element.  
Figure 3: Slot miniaturization technique performance.  
Figure 2: Evolution process of the slot miniaturization technique: (**a**) initial element with 0 slots on the patch, (**b**) 2 slots on the patch, (**c**) 4 slots on the patch, (**d**) 5 slots on the patch, and (**e**) final miniaturized antenna element.
# Mental Workload Assessment Using Machine Learning Techniques Based on EEG and Eye Tracking Data
## 3 Materials and Methods  
### N-Back Task  
Considering that cognitive load theory was formulated by taking into account the constraints of working memory capacity, it makes sense to choose task categories that specifically focus on working memory for conducting research on manipulating mental workload. N-back tasks are commonly utilized in research literature due to their reliability in assessing working memory capacity and their validity, which has been demonstrated in numerous studies [12; 13; 31; 32; 33; 34; 35; 36]. These memory tests were also employed in this particular study due to their established validity in previous research. N-back tasks inherently involve both the storage and manipulation of information, surpassing traditional working memory tasks. The level of \"n\" in the n-back task is directly related to the \"working memory load,\" representing the mental demand required for the storage and/or manipulation processes involved [37]. The n-back paradigm is well-established, with strong correlations between difficulty level and cortical activation associated with working memory. Monod and Kapitaniak [38] also suggested that task difficulty, an internal cognitive load factor, directly influences cognitive load. Previous research has shown that task difficulty, determined by the number of items to be remembered, hampers performance in memory tasks and affects psychophysiological measurements, particularly components regulated by the autonomic nervous system [39]. As task difficulty is a crucial determinant of mental workload, it was selected as the target variable in the classification model developed for this study.
## 4 Simulation and Analysis
### Simulation for Method for Avoiding Communication Interruption Based on Location Prediction  
Communication interruptions can significantly increase the transmission delay. In this section, we present simulation results to demonstrate the effectiveness of the ACI-LP protocol in reducing transmission delay.  
Our simulation scenario involves a pair of continuously moving UAVs that communicate with each other. The relevant parameters are shown in Table 3. Before transmitting each packet, a carrier sense mechanism is employed to assess the availability of channels, taking into account the presence of multiple channels. Without the ACI-LP protocol, the pair of UAVs would need to rediscover each other due to communication interruptions.  
The angle of the main lobe, speed of UAVs, and distance between the pair of UAVs influence the time of the main lobe rendezvous. Therefore, we investigate the influence of the time of main lobe rendezvous on transmission time.  
The simulation results are shown in Figure 31. In the figure, T-mlr represents the time of main lobe rendezvous, while the time of all information indicates the ratio of transmitted information to the communication rate. The actual time of all information is the communication time considering communication interruptions.  
Figure 30: The number of hops for the omnidirectional antenna and switched beam antenna.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(T_{CS}\) & 1 ms \\ \(T_{TS2-RTS}\) & 1 ms \\ \(T_{TS2-CTS}\) & 1 ms \\ \(T_{\text{Inf}}\) & 5 ms \\ \hline \(T_{\text{ACK}}\) & 1 ms \\ \(T_{TS1-RTS}\) & 1 ms \\ \(T_{TS1-CTS}\) & 1 ms \\ The number of sectors & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The parameters for simulation of the ACI-LP protocol.  
Figure 31 clearly demonstrates that the ACI-LP protocol decreases the actual communication delay caused by interruptions.