esa-sceva/satcom-chunk-collection huggingface dataset:
# An Experimental Study on D2D Route Selection Mechanism in 5G Scenarios
## 3 Related Work
### Communication Delay between Nodes  
The delay performance between nodes has been investigated in the literature. The traditional testbed that connects USRP/GNU radio nodes to a single processing unit has been investigated in [44]. There are two nodes in [43; 45], four nodes comprised of a SU node pair and a PU node pair in [44], and ten SU nodes in [30]. In [45], the delay incurred in USRP and communication is lower than that in the SDR processes (e.g., the modulation process) of GNU radio. In [43], the shortest delay is incurred in transmission (i.e., packets are transferred to FPGA to be interpolated and sent), and the longest delay is the initialization process i.e., sampling, modulation, encoding, and the transfer of packet from GNU radio to the operating system (or kernel). In [44], the end-to-end delay of SUs includes: (a) the waiting time of a packet in a queue; and (b) the channel sensing time, which contributes up to 30% of the end-to-end delay. Experimental results show that a balanced tradeoff between throughput and delay performance can be achieved. Higher channel sensing time reduces interference at the expense of lower throughput and higher delay, and vice-versa; hence, a balanced tradeoff must be achieved. In [30], the end-to-end delay is reduced by selecting routes prior to data transmission in a proactive manner.  
An enhanced testbed that consists of USRP/GNU nodes equipped with separate RP3 processing unit, which serves as the decision-making engine, has been investigated in [46] for two nodes. In [46], the end-to-end delay includes the processing delay of a USRP (e.g., decimation in FPGA), the operating system processes in the kernel, and the GNU radio processes (e.g., the modulation process). Measurement is conducted when a node pings another node and the processes in GNU radio are time stamped. Experimental resultsshow that the processes running in the kernel incurs the highest processing delay; therefore, USRP/GNU radio has a lower efficiency in network communication.
# Autonomous Mobile Robot Navigation in Sparse LiDAR Feature Environments
## 5 Experimental Results
### Verifying the Deep Learning-Based Localization Switching Method to Solve Corridor Effect  
The location of the experiment is a corridor at the National Taiwan University of Science and Technology, as shown in Figure 16. The red line is the ground truth of the experiment. Marks are spaced every 5 m, and the total length is 88 m.
## 3 Dynamics Simulation and Experimental Verification
### Method Comparison
Furthermore, in order to further verify this generalization method, as shown in Figure 16, we verified it using simulation data, and the accuracy rate reached more than 95%.
## 3 Results and Discussion
### Microfluidic-Sensing Studies for Amyloid-Beta Analysis
#### 3.2.1 Fluorescence Baseline Efficiency of CRANAD-2 Without Amyloid-Beta  
To establish a baseline for subsequent binding studies, CRANAD-2 at 25 uM in MeOH was evaluated in both macroscale and microfluidic configurations for A\(\beta\) (0 nM A\(\beta\)) in PBS, which represents a ratio of 1:1 (\(v/v\)) between MeOH and PBS. Both setups exhibited the same emission peak wavelength, confirming consistency in CRANAD-2's fluorescence response. However, the microfluidic platform yielded a lower absolute fluorescence intensity than the macroscale environment, likely due to optical path miniaturization and system constraints. A direct comparison of these fluorescence signals is presented in the Supplementary Materials (Figure S4). This baseline characterization provides the necessary reference point for analyzing fluorescence trends as A\(\beta\) concentrations increase, allowing for a more accurate interpretation of CRANAD-2's binding interactions in the microfluidic system.  
To account for differences in sample volumes and facilitate a fair comparison of fluorescence efficiency, a relative analysis was performed by normalizing fluorescence intensity per milliliter. Therefore, a sample from the microfluidic system yielded 3333.3 c.p.s./mL, which is approximately 4.93 times greater than the normalized intensity of 675 c.p.s./mL observed in the 2 mL macroscale sample. This substantial difference underscores the microscale system's capacity to preserve a fluorescence signal despite the drastic reduction in sample volume (about 75%). Although the smaller sample size and lower signal-to-noise ratio (SNR) reduced raw fluorescence intensity, the phenomenon remains detectable, allowing for robust quantitative analysis. These results highlight that the microscale environment offers a higher fluorescence intensity per unit volume, potentially due to a higher effec  
\begin{table}
\begin{tabular}{c c c c} \hline
**Protein Structure** & **Hydrodynamic Diameter (nm)** & **SD (nm)** & **Zeta Potential (mV)** \\ \hline Oligomers & 483 & 68 & \(-\)19.2 \\ Fibrils & 2179 & 343 & \(-\)8.8 \\ \hline \end{tabular}
\end{table}
Table 1: Hydrodynamic diameter, standard deviation (SD), and zeta potential for amyloid-beta.  
tive concentration within the confined microfluidic setup, with a detectable fluorescence response despite the significant reduction in sample volume.
# Probing Dust and Water in Martian Atmosphere with Far-Infrared Frequency Spacecraft Occultation
## 4 Discussion and Caveats
In this preliminary study our intent is to demonstrate that the optical properties of Mars atmosphere at terahertz frequency range (1\(\sim\)10 THz) can be exploited to characterize certain key properties of the planet's atmosphere, without delving into the intricate details of its realistic vertical temperature structure or its spatial and temporal variability. An extensive discussion of the number of frequency bands required or the necessary bandwidth to effectively distinguish between atmospheric dust and water is beyond the scope of this preliminary work. However, our approach considers a broad representation, a rough estimate of water ice and aerosol concentrations, which would serve as a first step for an engineering design. The iterative process between scientific study and engineering advancement is key to making progress. As engineering capabilities expand, so will the depth of our scientific investigation. A thorough examination of spectral band selection and spectral inversion techniques--essential to discerning various confounding factors-- is the necessary step in the future publication of a comprehensive science study and engineering design. This would entail considerations of the instrument such as the signal-to-noise ratio (SNR), bandwidth, and its stability as well as establishing a realistic model of Mars atmosphere, rooted in spatially and temporally varying observations combined with results from global circulation modeling.
# On the Delayed Scaled Consensus Problems
## 3 Undelayed Scaled Consensus Process
Then, the zero eigenvalue is no longer a simple eigenvalue of \(|\alpha|\mathcal{L}\)[17]. We consider the following two cases: (i) \(\lambda_{i}=\lambda_{1}=0\) for some \(i\
eq 1\), and the geometric multiplicity of zero is equal to its algebraic multiplicity; and (ii) the geometric multiplicity of zero is less than the algebraic multiplicity. In Case (i), say, \(\lambda_{2}=\lambda_{1}=0\), we have \(\dot{x}(t)=0\) for any \(x(0)\in\text{span}\{v_{2}\}\) by (3). However, for such initial values, scaled consensus to \((\alpha_{1},\cdots,\alpha_{n})\) is impossible because \(v_{2}\
ot\in\text{span}\{v_{1}\}\). Next, we consider Case (ii). In this situation, the state matrix \(-\text{sgn}(\alpha)\mathcal{L}\alpha\) is similar to a Jordan matrix \(J\in\mathbb{R}^{n\times n}\). Hence, \(\exp(-\text{sgn}(\alpha)\mathcal{L}\alpha t)=P^{-1}e^{lt}P\) for some invertible matrix \(P\). For a \(k\times k\) Jordan block \((k\geq 2)\), we have:  
\[\exp\left(\left( \begin{array}{cccc}0&1&\cdots&0\\ 0&0&\ddots&\vdots\\ \vdots&\vdots&\ddots&1\\ 0&0&\cdots&0\end{array} \right)\right)=\left( \begin{array}{cccc}1&t&\cdots& \frac{t^{k-1}}{(k-1)!}\\ 0&1&\ddots&\vdots\\ \vdots&\vdots&\ddots&t\\ 0&0&\cdots&1\end{array} \right)\in\mathbb{R}^{k\times k},\]  
which is divergent with respect to \(t\). Similar reasoning as (4) implies that \(x_{i}(t)/x_{j}(t)\) does not approach \(\alpha_{j}/\alpha_{i}\) for all \(i,j\) and generic initial conditions \(x(0)\). Thus, scaled consensus to \((\alpha_{1},\cdots,\alpha_{n})\) is not achieved, and this completes the proof of necessity. **Remark 2**. : _The scaled consensus protocol specified by (3) is first studied in [6]. It is proven that [6] if \(G\) is strongly connected, then the scaled consensus can be reached. Theorem 1 improves the result by showing that a weaker spanning tree condition, actually, is not only sufficient, but necessary._  
**Remark 3**. : _It is clear that the scaled consensus states are represented by \(w_{1}^{\mathsf{T}}x(0)v_{1}=w_{1}^{\mathsf{T}}x(0)(\alpha_{1}^{-1},\cdots, \alpha_{n}^{-1})^{\mathsf{T}}\) and the scaled states \(\alpha x(t)\to w_{1}^{\mathsf{T}}x(0)1_{n}\), a common asymptote, as \(t\) grows. In fact, the quantity \(w_{1}^{\mathsf{T}}x(t)\) is conserved under the dynamics (3) since \(\frac{d}{dt}(w_{1}^{\mathsf{T}}x(t))=-w_{1}^{\mathsf{T}}\mathrm{sgn}(\alpha) \mathcal{L}\alpha x(t)=0\)._  
Similarly as in [6], we may extend Theorem 1 by considering a tracking dynamics on the scaled consensus manifold as:  
\[\dot{x}(t)=-\mathrm{sgn}(\alpha)\mathcal{L}\alpha x(t)+\dot{f}(t)\alpha^{-1} 1_{n}, \tag{5}\]  
where the forcing input \(f:[0,+\infty)\to\mathbb{R}\) is continuously differentiable. Setting \(y(t)=x(t)-f(t)\alpha^{-1}1_{n}\), we can prove the following result using the same argument as in Theorem 1. **Corollary 1**. : _The multi-agent system (5) achieves scaled consensus to \((\alpha_{1},\cdots,\alpha_{n})\) if and only if \(\mathcal{G}\) has a spanning tree. In particular, \(x(t)\) converges to the time function \((w_{1}^{\mathsf{T}}x(0)+f(t))v_{1}\) as \(t\) grows._
# Testbed for LoRaWAN Security: Design and Validation through Man-in-the-Middle Attacks Study
## 2 Background
### LoRa and LoRaWAN Basics
#### 2.1.1 Encryption Algorithms  
The advanced encryption standard (AES) algorithm in the electronic code book (ECB) mode is used to encrypt the on-air communication in LoRaWAN. The AES-ECB is a block cipher in which the message is divided into blocks of a fixed length--in LoRaWAN's case, 128 bits. It is characteristic of this cipher that the cryptogram depends only on the message block and the key.  
The encryption in LoRaWAN proceeds as follows. Of the entire LoRaWAN message that is illustrated in Figure 2, only part of the frame payload (FRMPayload) is encrypted. This FRMPayload is encrypted with the application-specific key AppSKey in case a frame carries application data. If FRMPayload carries media access (MAC) commands, it is encrypted with the network key NwkSKey. The used encryption method can be specified from the FPort value, which signalizes the intended target of the message. For each message, a sequence of blocks \(A_{i}\) is constructed, where \(i\) takes values from \(1\) to the length of the message divided by \(16\) and rounded up.  
The structure and contents of block \(A_{i}\) are defined according to the LoRaWAN specification [6]. The first byte has the value \(|0x01|\), the next \(4\) bytes are \(|4\times 0x00|\) followed by the byte for the frame direction (\(0\) for uplink frames and \(1\) for downlink frames), another \(4\) bytes carry the device address and the frame counter. Then, a byte with value \(|0x00|\) and a byte of parameter \(i\) are appended. The complete string thus appears as: \(|0x01|4\times 0x00|Dir|DevAddr|Fcnt|0x00|\). This sequence of \(A_{i}\) blocks is encrypted using the \(K\) key, resulting in a string \(S\). The \(K\) key is selected according to the FPort value: if the value is \(0\), the NwkSKey key is selected, for other values the AppSKey is used. Finally, an exclusive disjunction (XOR) operation is applied to string \(S\) to create an encrypted FRMPayload.  
Subsequently, the message integrity code (MIC), allowing the verification of the integrity of the message, is generated. This code is calculated over all fields of the message, i.e., \(message=|MHDR|FHDR|FPort|FRMPayload|\). Within LoRaWAN, a cipher-based message authentication code (CMAC) is used to authenticate messages. The CMAC  
Figure 2: LoRaWAN message structures.  
authentication code is based on the use of the AES block cypher in the cipher block chaining (CBC) mode. In LoRaWAN, authentication is carried out by creating a block marked B, which is then concatenated with the message (\(|M HDR|F HDR|FPort|FRMPayload|\)) as the key, NwkSSKey is used and the CMAC cypher is executed.
# Multivariable Adaptive Super-Twisting Guidance Law Based on Barrier Function
###### Abstract  
For tactical missiles, sliding mode control and super-twisting algorithms have been widely studied in the area of guidance law design. However, these methods require the information of the target accelerations and the target acceleration derivatives, which is always unknown in practice. In addition, guidance laws utilizing these tools always have chattering phenomena and large acceleration commands. To solve these problems, this article introduces a barrier function based super twisting controller and expands the controller to a multivariable adaptive form. Consequently, a multivariable adaptive super-twisting guidance law based on barrier function is proposed. Moreover, the stability of the guidance law is analyzed, and the effectiveness and the robustness are demonstrated by three simulation examples. Compared with previous guidance laws using sliding mode control or super-twisting algorithm, the one proposed in this paper does not require the information of target accelerations, nor target acceleration derivatives; it has smaller super-twisting gains so that has smaller acceleration commands; it can increase and decrease the gains to follow the target accelerations and maintain the sliding mode, and it does not chatter.  
guidance law; tactical missile; sliding mode control; super-twisting algorithm; barrier function +
Footnote †: journal: Applied Sciences  
1
# Detection of Pitt-Hopkins Syndrome Based on Morphological Facial Features
## 3 Results  
An evaluation test of the proposed system was carried out on the collected database composed of 61 individuals (29 healthy and 32 PTHS patients). The evaluation aims at demonstrating the ability of the system to recognize facial features associated with PTHS and discriminate healthy and syndromic individuals with a high accuracy rate. The limited number of input files allowed a manual inspection of the data processing in the pipeline and a visual check of the performance in the image recognition steps. The system was able to run well in the first step of the proposed pipeline: acquisition and pre-processing. All images of the dataset were correctly acquired from a folder of the hard disk and they were pre-processed as expected. In the second step, the system also worked well. The face present in each picture of the database was detected and the bounding box correctly returned. Face alignment and registration is the third step of proposed pipeline. Furthermore, for this step, the system correctly executed the alignment of the face using the five landmarks approach based on the four corners of the eyes and on the nose tips. As the output of this step, all the face images detected were aligned, registered and finally scaled at 600 \(\times\) 600 pixels. In the next step of the proposed pipeline, landmarks localization was implemented. All the facial images were correctly described and contoured by landmarks. None of the face images of the collected dataset showed incorrectly localized landmarks. The main differences between PTHS patients and healthy controls were represented by the face features around the nose, the mouth and the face contours as is shown in Figure 4. The figure shows the mean face shape produced using the mean value of each facial landmark for both populations. In order to perform the classification, six geometrical facial features (R1, R2, R3, RNose, RMouth, and NoseAngle) were used. Support vector machines (SVMs), random forest (RF), and the k-nearest neighbor (k-NN) were applied on the dataset composed of 61 individuals, and evaluated by using 10-fold cross validation and computing weighted arithmetic average accuracy and F-score. The best classifier was k-NN, with an accuracy of 91.8% and an F-score of 0.918. SVM obtained an accuracy of 90% and an F-score of 0.9, and RF an accuracy of 87.19% and an F-score of 0.878. Moreover, to prove the robustness of the proposed system, a comparison with human performance was performed. All 61 images of the dataset were shown to a pediatrician. The pediatrician assigned to each facial image a label: PTHS or not. The procedure determined the recognition rate of a human. The accuracy rate was 74% and F-score was 0.722. This result confirmed that in some cases, the facial dysmorphism is not quite evident and a computer-aided system would be useful for PTHS pre-diagnosis. Figure 5: Box plots of distance features for both groups (PTHS and control). R1-PTHS = Group 1; R1-Control = Group 2; R2-PTHS = Group 3; R2-Control = Group 4; R3-PTHS = Group 5; R3-Control = Group 6.
# SOLIOT--Decentralized Data Control and Interactions for IoT
## 4 IoT Requirements
### Protocol Characteristics  
Crucial for any information exchange is the grounding of the outlined characteristics towards well-known protocols (cf. Figure 5). This step makes the requirements actually implementable. A common practice is protocol bindings, which state the translation of the required functionalities into the abilities of already existing protocols (P1). Closely related is the provisioning of _control information_ (P2). The link-based hypermedia approach exposes _interaction options_ (P3) with the information document itself, while for instance Web Services require special interface descriptions. Similarly, the _coupling between client and server connectors_ ([22], P4) and the required configuration effort at the client-side (P5) need to be provided. While the first is relevant for the scalability of a network  
Figure 4: Overview of Non-Functional Requirements by their location in the network architecture.  
(see also N7), the later targets the issue whether interaction parameters are explicitly presented at the resource, externally defined by e.g., a standard document, or even implicitly stated and therefore hard to implement at the client connector.  
Generally speaking, a binding must state how a specific protocol implements the demands of the functional requirements while complying to the non-functional ones. As such, the interactions with resources (F2 to F4) are crucial. However, Digital Twins in the regarded applications have several unique requirements as they are usually deployed on small, resource-constraint devices. _Network bandwidth_ (P6) is the amount of binary data needed to transport an information unit over the network, closely related to its _energy consumption_ (P7).  
Especially for real-time applications, low _latency_ (P8) is crucial, Latency is directly affected by the selected data format. While the data model and its semantic are described in the following section, the syntactical aspects and especially the supported _Media Types_ (P9) are specified by the protocol binding [22].  
In terms of the running example, both the Manufacturer and the Data Analyst require a safe and trustworthy but also easily accessible platform for the exchange of the Digital Twin. To stay open for further clients and use cases, they decide against the implementation of proprietary or customized interfaces but stick to a mature and widespread standard.  
The Manufacturer and the Data Analyst connect their local machines to a gateway server running SOLIOT. The observed resources are represented by SOLIOT assets. As the whole configuration and every available information might be needed for future applications, all events and states are collected at the SOLIOT server. However, as the created dataset allows insights into their respective operational activities, and even might uncover their competitive advantages and technical knowledge, access must be restricted.
## 4 Discussion  
### Effects of the Pre-Pulse Irradiation  
By considering the ablation phenomena at nanoseconds time delays after the pre-pulse, the observed dynamic spectral shift toward the low frequency and the giant enhancement of THz emission are discussed from the viewpoints of THz wave absorption by water, surface structural modifications of the flow such as roughness/ripple formation, mist ejection, and swelling, an elongation of THz wave source, and THz wave emission characteristics.  
Based on the knowledge of laser ablation of liquids under the similar experimental conditions [52], the structural modifications of the water flow induced by the pre-pulse irradiation, nano- to micro-scale roughness/ripples, droplet/mist ejection, and pressure wave propagation with the swelling on the rear surface are considered to be shown in Figure 4a. The scale length is governed by the speed of sound \(v_{s}=1.481\) km/s (under normal conditions) and the delay time after the pre-pulse irradiation, \(\Delta t\). At the  
Figure 3: Enhancements in THz wave emission in \(\,\mid\!E\!\mid^{2}\) (**a,b**) and X-ray (**c,d**) in the transmission and the reflection sides as a function of the delay time between the pre-pulse and the main-pulse irradiations. The vertical axes are normalized with the emission intensity under the single-pulse excitation condition (open circles).  
delay time of 4.7 ns, for instance, such pressure wave travels \(v_{s}\times\Delta t\approx 7\)\(\upmu\)m, which is comparable with the thickness of the water flow. Hence, the pressure induces the increase of mass density \(+\Delta\rho\) due to the compression and causes the rarefaction \(-\Delta\rho\) at the central part which leads to cavitation on longer time scale.
# An Intrusion Resistant SCADA Framework Based on Quantum and Post-Quantum Scheme
## 5 Formal Analysis of the Proposed Scheme
### Analysis of Key Generation Phase
#### 5.1.1 B92 with Intercept-Resend Attack Model  
The Intercept-Resend attack model is based on active eavesdropping. [PERSON] tries to read the qubits or bits of information, exchanged through quantum and public channels, during key generation [49]. Figure 7 shows the probability of detecting [PERSON]. Figure 8 shows the probability of detecting information leakage more than N/2, while N is the number of qubits.  
Figure 7: Probability of detecting [PERSON] during Intercept Resend Attack.