esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Security Analysis
### Attacks by Low-Level Network Members  
Regarding the risk of substitution attacks by unscrupulous low-level network members. Our proposed HMPS technique incorporates several inherent mechanisms to counter such threats, detailed here.  
Hierarchical verification: Each node in the signing chain is required to verify the signatures from all preceding nodes. This layered verification ensures that any alteration or substitution of the original message (or its associated signature) by a low-level node would immediately cause a mismatch in the verification process. If even a single signature is invalid or missing, the entire chain is rejected, effectively preventing the propagation of a tampered message.  
Digital signature binding: By binding the signature to the message via cryptographic hash functions (as shown in Algorithms 1-5), any modification to the message or substitution of its components (including images) results in a hash mismatch. This binding provides a robust cryptographic guarantee that only the authentic, original message will yield the correct signature upon verification.  
Integrated encryption: The encryption process (Enc and Dec functions) integrates the signature with the message. This integration means that any unauthorized substitution not only disrupts the signature chain but also affects the decryption process, thereby exposing any tampering attempts.  
Supplementary tools: While the HMPS technique is primarily focused on ensuring integrity and authenticity, we recognize that in scenarios where the consequences of substitution attacks are particularly severe (e.g., affecting reputation, logistics, or financial markets), additional measures could be implemented. These may include the following:  
Consensus: A safety layer can be added to the level of the message origin where it is not accepted by LEC unless it is signed by more than one source. For instance, when reporting an accident, the first vehicle sends a picture to the other vehicles that witnessed the same accident. If a number \(x\) of the vehicles agree to the validity of the accident presence, they all sign the notification message, and it is then sent to LEC. Watermarking for images: embedding robust, cryptographic watermarks within images to verify their authenticity. Redundancy and consensus protocols: Incorporating distributed consensus mechanisms where multiple independent verification instances are required before accepting a message or image as authentic. Secure time stamping: using trusted time stamping services to provide an immutable record of when a message was created and signed, further reducing the window for substitution attacks. Together, these mechanisms ensure that any attempt by a low-level node to substitute false messages or images would be detected and rejected during the hierarchical verification process, preserving the integrity and trustworthiness of the system.
# Opportunistic Multi-Technology Cooperative Scheme and UAV Relaying for Network Disaster Recovery
## 6 Performance Evaluation
### Cooperative Data Relaying with UAVs
#### 6.2.2 Obtained Results  
Figures 10-12 shows the number of stops, the length, and time of a drone tour as a function of the number of nodes in the network for the different scenarios. More specifically, Figure 10 shows how the number of stops of a drone reduces for the two schemes that offer cooperation, i.e., TSP-COPE and CEISP-COPE. In fact, while the number of stops of the TSP scheme with no cooperation increases linearly with the network density, the CEISP-COPE instead shows how the number of stops increases slowly with the network density. Moreover, there is a clear trend, i.e., almost a stable number of stops, for networks with high node density. As such density increases, the multi-tier cooperation among nodes becomes more efficient as nodes have more neighbors, hence more clusters are formed. In fact, the number of stops reduces by more than 70% for a density of 500 nodes. Figures 11 and 12 show how our proposed CEISP-COPE scheme, specifically, outperforms the schemes with no cooperation among the nodes to relay data. This is clearly shown by the fact that the tour length shortens by more than half, and that the drone flying and hovering time reduces by \(\approx\) 60% for a high network density. In details, Figure 11 shows that the difference in the tour length for the four schemes increases with the node density; the CEISP-COPE tour length is, in fact, half that of the TSP one. Similarly, the flying and hovering time of a drone shown in Figure 12, increases very slowly for the schemes that provide cooperation, while such values are at least 30% higher for the TSP scheme and high node densities in the network. For instance, the CEISP-COPE drone tour time reduces by around 50% compared to the TSP for 500 nodes in the network. However, CEISP outperforms TSP-COPE and that can be explained by the fact that the cellular transmission range results in a drone to dictate fewer anchor points to visit than the number of \(n_{3}\) tier CHs designated based on node cooperation, hence TSP-COPE.  
The energy expenditure of a drone depends mostly on the hovering time [27]. Our cooperative schemes, i.e., TSP-COPE and CEISP-COPE, offer low hovering times compared to flying ones, even for high network densities. Such a trend, presented in Figure 12, shows that the hovering time (proportional to the number of stops) keeps at low levels for all network densities. That is because more clusters with big cardinality are formed as the network density increases, hence a limited number of \(n_{3}\) tier nodes relay the data of all the underlaying tier nodes. Moreover, even the flying (movement) time, which constitutes most of a drone tour time, is further reduced by our cooperative schemes.  
Figure 10: Number of stops.
## 3 Results and Discussion
### Optimization for the Removal of the FD&C Red 40 Dye in an Aqueous Solution
#### 3.2.3 Effect of the Initial Concentration of FD&C Red 40 Dye  
The variation in the initial concentration of FD&C Red 40 dye is of great importance as it allows for evaluating the performance of the CS-TiO\({}_{2}\)-GLA beads in their role as adsorbents [31]. The dye molecules diffuse through the solution to the surface of the beads, where they adsorb and gradually penetrate the boundary layer [32].  
At low initial dye concentrations, the ratio between the number of available adsorption active sites on the beads is higher than the number of molecules in solution so that the material can rapidly adsorb FD&C Red 40 dye (with a high percentage of removal). However, as the initial concentration increases, the number of adsorption active sites decreases concerning the amount of adsorbate in solution, reducing the removal percentage [32]. In turn, the higher the FD&C Red 40 dye concentration, the greater the electrostatic repulsion between the molecules (due to the dissociated sulfonate groups) when they enter the surface of the beads. Therefore, some dye molecules are not adsorbed and remain in the aqueous phase [31]. As a final effect, the percentage of removal decreases.
# Microbial-Based Biotechnology: Production and Evaluation of Selenium-Tellurium Nanoalloys
###### Abstract  
Using seleno-compounds and telluric compounds is a practical approach for developing solutions against drug-resistant bacterial infections and malignancies. It will accelerate the search for novel treatments or adjuvants for existing therapies. Selenium and tellurium nanospheres can be produced by lactic acid bacteria. The bacteria can differentiate the selenium and tellurium when the medium contains both selenite and tellurite. Therefore, our question in this study was the following: are they making alloys from the selenium and tellurium and what will be the composition, color, and shape of the nanoparticles? We used a simple microbial synthesis to produce nanoselenium, nanotellurium, and their alloys from sodium selenite and sodium tellurite using _Lactobacillus case_. This bacterium produced red spherical amorphous elemental selenium nanospheres with a diameter of 206 \(\pm\) 33 nm from selenite and amorphous black nanorods with a length of 176 \(\pm\) 32 nm and a cross-section of 62 \(\pm\) 13 nm from tellurite. If the initial medium contains a mixture of selenite and tellurite, the resulting nanoparticles will contain selenium and tellurium in the same ratios in the alloy as in the medium. This proves that _Lactobacillus case_ cannot distinguish between selenite and tellurite. The shape of the nanoparticles varies from spherical to rod-shaped, depending on the ratio of selenium and tellurium. The color of nanomaterials ranges from red to black, depending on the percentage of selenium and tellurium. These nanomaterials could be good candidates in the pharmaceutical industry due to their antipathogenic and anticarcinogenic properties.  
green synthesis: _Lactobacillus casei_, nanoalloys; selenium; tellurium
# Robust JND-Guided Video Watermarking via Adaptive Block Selection and Temporal Redundancy
## 2 Related Works
### Block Selection for Robust Embedding in Video Watermarking  
The effectiveness of a watermarking method strongly depends on where the watermark is embedded. Poor selection of blocks, such as those in uniform or highly visible regions, can lead to poor robustness and degraded visual quality. Early methods relied on uniform grid sampling or random block allocation to select embedding positions [31]. These approaches are simple but disregard the key perceptual and structural properties of the content. In modern systems, this has largely been replaced by perception-driven selection using models of the HVS.  
A common strategy involves computing visual saliency maps to identify low-attention regions. Saliency-based methods such as the spectral residual approach or contrast-based filtering have been used to isolate background areas more suitable for embedding [16; 32]. More recent approaches utilize deep saliency networks to capture context-aware attention fields [33]. Others incorporate frequency analysis, selecting blocks with strong mid- or high-frequency content (e.g., edges or textures), as these are less affected by quantization and compression. This is highly effective in DCT-based watermarking [10].  
In addition, semantic masking has gained momentum. This approach explicitly avoids placing watermarks in regions containing high-level semantic information, such as faces, text, or logo areas. [PERSON] et al. [23], for instance, developed a multiscale framework that combines saliency with object detection to guide watermark placement away from semantically meaningful zones. Some systems go further, using learning-based models to classify regions by expected robustness. [PERSON] et al. [34] proposed a CNN-based robustness predictor for image watermarking that could be extended to video.
# Wildlife Monitoring Using a Multi-UAV System with Optimal Transport Theory
## 4 Simulation Results
### Unicycle Robot Dynamics  
Given the UAV located at \(x_{T}=[x_{T},y_{T}]^{T}\) with \(x_{T},y_{T}\in\mathbb{R}\) at any time \(T\in\mathbb{N}\), the UAV position for the next time step \(T+1\) is updated by using the following unicycle model:  
\[\left\{\begin{array}{l}x_{T+1}=x_{T}+v\cos(\theta_{T}+\omega\Delta t)\Delta t \\ y_{T+1}=y_{T}+v\sin(\theta_{T}+\omega\Delta t)\Delta t\\ \theta_{T+1}=\theta_{T}+\Delta\theta_{T},\end{array}\right. \tag{14}\]  
where \(v\) and \(\omega\) denote the linear and angular velocity of the UAV, respectively, \(\theta_{T}\) and \(\Delta\theta_{T}\), respectively, indicate the heading angle and change of the heading angle for the UAV, and \(\Delta t\) is the time interval between consecutive discrete-time steps.  
From the current location \(x_{T}\) at time \(T\), if the next goal point is given by \(sx_{T+1}\), then the positional error is defined as \(x_{e}=sx_{T+1}-x_{T}\) and the required transnational velocity \(v\) to compensate the positional error can be determined by  
\[v=\frac{K_{x}\cdot x_{e}}{\Delta t}, \tag{15}\]  
where \(K_{x}\) denotes the positional error gain.  
Also, for the current heading angle error \(\theta_{e}=\delta\theta_{T+1}-\theta_{T}\), where \(\delta\theta_{T+1}=\arctan\left(\frac{\delta y_{T+1}-y_{T}}{\delta x_{T+1}-x_{T}}\right)\), the angular velocity \(\omega\) required for minimizing the heading angle error is obtained from the following equation.  
\[\omega=\frac{K_{\theta}\cdot\theta_{e}}{\Delta t}, \tag{16}\]  
where \(K_{\theta}\) represents the angular error gain.
# LDPC Decoder Design Using Compensation Scheme of Group Comparison for 5G Communication Systems
## 4 Proposed Compensation Scheme for NPMSA  
The NPMSA is adopted in a LDPC decoder design to reduce the hardware area cost, to relieve the complexity of routing, and to accelerate the operation of numerous comparators. However, the accuracy of the algorithm is decreased compared to the NMSA. Therefore, the balance between hardware complexity, overall calculation speed, and decoding capability is important. To resolve the algorithm accuracy problem, the rExMin algorithm [17] and the SMA-MSA [18] were proposed as the alternative methods for the NPMSA. Their concepts are based on reducing the gap between the accurate second minimum and the probabilistic second minimum to recover the decoding capability. We continue these concepts and modify the probabilistic second minimum using a flexible adjustment.
## 2 Summary of Design Specifications for Beam Bracing System with Torsional Bracing
### LTB Strength of I-Grider Sections for Bridges  
In the AASHTO design standard [22], the LTB strength of the prismatic steel I-grider is provided. The LTB strength based on the unbraced length (\(L_{b}\)) is provided as below.  
If \(L_{b}\leq L_{p}\), then  
\[F_{nc}=R_{b}R_{h}F_{yc} \tag{5}\]  
If \(L_{p}\leq L_{b}\leq L_{r}\), then  
\[F_{nc}=C_{b}\bigg{[}1-\bigg{(}1-\frac{F_{gr}}{R_{h}F_{yc}}\bigg{)}\bigg{(} \frac{L_{b}-L_{p}}{L_{r}-L_{p}}\bigg{)}\bigg{]}R_{b}R_{h}F_{yc}\leq R_{b}R_{h} F_{yc} \tag{6}\]  
If \(L_{b}>L_{r}\), then  
\[F_{nc}=F_{cr}\leq R_{b}R_{h}F_{yc} \tag{7}\]  
where \(F_{nc}\) denotes the lateral-torsional buckling resistance of compression flange, \(L_{b}\) denotes the unbraced length, \(L_{p}\) limits the unbraced length to achieve the nominal flexural resistance of \(R_{b}R_{h}F_{yc}\) under uniform bending, \(L_{r}\) limits the unbraced length to achieve the onset of nominal yielding in either flange under uniform bending with consideration of compression flange residual stress effects, \(C_{b}\) is the moment gradient modifier, \(R_{b}\) is the web load-shedding factor, \(R_{h}\) is a hybrid factor, \(F_{yc}\) is the yielding strength of steel girders, \(F_{gr}\) is the compression flange stress at the onset of nominal yielding within the cross-section, and \(F_{cr}\) is the elastic lateral-torsional buckling stress.
# Semi-Active Magnetic Levitation System for Education
## 2 Material and Methods  
Before presenting the details about the implementation of the magnetic levitation system (MagLev), we recall how a MagLev prototype should work [21; 22]. The MagLev contains a coil, which is assembled as an electromagnet. The electromagnet represents the MagLev control system's actuator. By regulating the current passing through the coil, the MagLev controls the electromagnetic force the electromagnet generates. The MagLev employs an algorithm to control the current in the coil. The algorithm is responsible for regulating the induced electromagnetic force, which in turn counteracts the effect of the gravitational force acting on the object. The algorithm requires real-time measurements from the position of the object being levitated. This task is accomplished through a Hall-effect sensor, as detailed below. From the practical viewpoint, the low-cost magnetic levitation prototype consists of an electromagnet, a Hall-effect sensor model A1324 from Allegro(c), an Arduino Uno microcontroller, and a power amplifier, as depicted in Figure 1. It is necessary to use a computer to program the algorithm into the Arduino Uno; in this project, we use a small single-board computer known as Raspberry-Pi (see Figure 2).  
Figure 3 presents a schematic that displays the construction of the prototype. The Hall-effect sensor monitors the position of the object being levitated. This sensor's analog voltage signal is measured by the Arduino Uno microcontroller (MC) through an analog-to-digital converter (ADC). The control algorithm running into the microcontroller then calculates the necessary control value that will be supplied to the electromagnet by using a pulsed-width-modulated output pin. This process is accomplished in the discrete-time domain with a sampling rate fixed at ten milliseconds.  
Figure 1: A low-cost experimental platform for electromagnetic levitation control design. Main parts (**left**): (**a**) actuator coil; (**b**) Hall-effect sensor; (**c**) power amplifier; (**d**) Arduino Uno microcontroller; and (**e**) a fan to dissipate heat from the power amplifier. (**Right**): Photo of the object being levitated.  
Figure 2: Communication scheme. The leftmost board represents a Raspberry-Pi used to collect and store measurements from the control system. The control system is implemented in the Arduino Uno board (middle), which communicates in real time with the MagLev (right).
## 3 Results and Discussions
### Antioxidant Properties
#### 3.2.1 DPPH Scavenging Activity  
DPPH assay is widely performed to assess the free radicals scavenging power of herbal extracts or antioxidant compounds [25]. In this test, the antioxidant compounds reduce picylhydrazyl (purple radical) to picylhydrazine (pale yellow hydrazine). The discoloration indicates the free radical scavenging ability of tested extract. This ability was evaluated spectrophotometrically by the determination of the IC\({}_{50}\) values (the concentration required to scavenge 50% of DPPH radicals). IC\({}_{50}\) value is inversely related to antioxidant capacity of the sample. Table 1, Figure 2 displays the results of antioxidant activity for each extract.  
A remarkable difference was noted in the antioxidant activity of the different extracts of _Centaurea_ for both species. This might be explained by the different constituents in each extract [29]. According to the literature, several compounds (e.g., cysteine, glutathione, ascorbic acid,tocopherol, polyhydroxy, hydroquinone, pyrogallol, gallic acid, p-phenylene diamine, and p-aminophenol) have been well recognized as reducing agents of DPPH due to their hydrogen donation capacity [34]. In the present work, the lowest IC\({}_{50}\) are recorded in chloroform, ethanol, and methanol extracts for both _entaurea_ species that range from \(0.58\pm 0.016\) to \(0.94\pm 0.01\) mg/mL and from \(1.96\pm 0.05\) to \(2.94\pm 0.02\) mg/mL for _C. kroumirensis_ and _C. sicula_, respectively. Interestingly, the hexanic extract of _C. kroumirensis_, which showed the highest antioxidant ability (IC\({}_{50}=0.49\pm 0.01\) mg/mL), does not exhibit the most important TPC. The noteworthy differences could be explained by the synergistic effect of polyphenols extracted in hexanic sample and other non-phenolic antioxidants present in _Centaurea_ plant (e.g., vitamin C [35], carotenoids and tocopherols [36], sesquiterpene lactones [37]), which significantly contribute to the free radical scavenging activity [20; 38]. The chloroform extract has similar IC\({}_{50}\) to methanol and ethanol extract of _C. kroumirensis_ and _C. sicula_, respectively. The EO and SFE (I-II) extracts for both _Centaurea_ species do not exhibit significant antioxidant activities.
# A Joining Procedure and Synchronization for TSCH-RPL Wireless Sensor Networks
###### Abstract  
Wireless Sensor Networks have become a key enabler for Industrial Internet of Things (IoT) applications; however, to adapt to the derived robust communication requirements, deterministic and scheduled medium access should be used, along with other features, such as channel hopping and frequency diversity. Implementing these mechanisms requires a correct synchronization of all devices in the network, a stage in deployment that can lead to non-operational networks. The present article presents an analysis of such situations and possible solutions, including the common current approaches and recommendations, and proposes a new beacon advertising method based on a specific Trickle Timer for the Medium Access Control (MAC) Time-Slotted Channel Hopping (TSCH) layer, decoupling from the timers in the network and routing layers. With this solution, improvements in connection success, time to join, and energy consumption can be obtained for the widely extended IEEE802.15.4e standard.  
WSN; synchronization; IoT; IIoT; TSCH +
Footnote †: journal: Sensors
# FPGA-Based Pulse Compressor for Ultra Low Latency Visible Light Communications
## 4 FPGA Implementation
### The Receiver  
The architecture of the receiver is sketched in Figure 7.  
The signal received by the photodetector is amplified and converted to digital at the rate \(1/T_{RC}=12.5\) MHz and 12-bit resolution. The samples are then moved in the FPGA. Here, they enter the compressor filter that is realized through a Finite Impulse Response (FIR) filter at \(N_{S}\) taps, whose coefficients \(W(i)\) are obtained by mirroring in time the chirp samples:  
\[W(i)=CH(N_{S}-i)\quad 0\leq i\leq N_{S}-1 \tag{9}\]  
This approach is particularly beneficial for the FPGA implementation, since the FIR architecture is already available as Intellectual Property (IP) in the most diffuse integrated development environments for FPGA. In this case, we implemented the FIR through the FIRII core IP [27] of Intel/Altera. The coefficients are expressed with a 16-bit resolution, and the filter output has a theoretical resolution of 34-bit for the 56-sample chirp up to 36-bit for the 224-sample chirp; however, only the 16 Most Significant Bits (MSBs) are moved to the next processing block.  
The next block applies the threshold. It produces a single bit for every input word: 1 if the input value is over the threshold, 0 otherwise. The threshold is programmable, but a value of half-maximum works for most of the cases. Its output is the bitstream theoretically corresponding to the bitstream in input to the modulator integrated in the transmitter (see Figure 5), but it should be noted that here the bitstream is oversampled by a factor of \(T_{RC}/T_{B}\).  
Figure 7: Architecture of the receiver.  
The packet reconstruction block locates the packets in the oversampled bitstream, verifies the checksum, and extracts the payload. Its architecture is detailed in Figure 8.  
The oversampled bitstream flows at \(1/T_{RC}\) rate in a shift-register with 185 flip-flops. From the shift register, the prospective 24-bit packet is extracted in parallel by undersampling 1 bit every 8 shift positions. For every prospective packet, the preamble is searched in the P\({}_{23\text{-}20}\) positions with a simple AND gate; the CRC is calculated and compared (COMP) to the sub-word \(P_{3\text{-}0}\). If both the aforementioned conditions are true, the sequencer activates the data valid and transfers the payload \(P_{19\text{-}4}\) in output. However, since the input bitstream is oversampled, there is the risk that the same packet is detected and outputted up to eight times. For this reason, the sequencer, after a packet is detected, starts a hold-off period where the packet detection is suspended. The packet lasts 24 bits that corresponds to 24 \(\times\) 8 = 192 \(T_{RC}\); thus, the hold-off period must last from a minimum of 8 \(T_{RC}\) to a maximum of 192 \(T_{RC}\). We used a value of 32 \(T_{RC}\).  
The reconstructed packets are finally buffered in a FIFO memory, where they are available for the applications.