esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance
### Energy Consumption  
Figure 12 shows the energy consumed by the routing protocols. The energy was computed by counting the number of transmitted broadcast and the number of received broadcast packets. ARF showed the lowest energy consumption because it broadcast the routing control messages at short intervals only during routing recovery, while it used long intervals in other cases. Therefore, the total number of sent and received routing packets was very low. Both Proc and MintRoute with the long interval (5,000 ms) showed low energy consumptions with low numbers of broadcast packets, but the received packet totals were also low. Proc and MintRoute with the short interval (10,000 ms) showed the highest energy consumption levels because these protocols always transmitted routing control messages.  
Figure 11: Total number of received packets and broadcast packets.  
messages at short intervals. As a result, ARF could recover a destroyed routing tree due to a large failure at a fast speed while consuming less energy.
# Research on Localization Algorithms Based on Acoustic Communication for Underwater Sensor Networks
## 3 Localization Algorithm
### Computation Algorithm  
Based on the computation algorithm to perform, we divide the computation algorithm into two classes, namely, centralized and distributed techniques. To put it simply, the centralized technique estimates the position of the sensor node at a sink node. Each sensor node does not need to know its own location. However, the distributed technique estimates the sensor node's position individually. That is to say that each sensor node collects related information such as the distance to anchor, anchor position, the distance to neighbors or the angle information to realize localization, and then the localization algorithm is performed in each sensor node. Then, the centralized or distributed techniques are classified into two subcategories as estimation-based and prediction-based schemes. In the estimation-based method, the latest available information is used to obtain the current position of the sensor node. For the prediction-based scheme, the previous node position, the distance information, and the anchor positions are employed to predict the node position at the next time instant. Thus, it is suitable for use in a mobile UWSN or a hybrid UWSN.
## 3 Simulation Results
### Homogeneous Environment
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Detector** & **CA** & **KLQ(6,3)** & **OS(28)** & **IBQ(6,3)** & **VIHCEMOS** & **KLQVI(6,3)** \\ \hline Average & & & & & & \\ detection probability & 0.6755 & 0.6613 & 0.6519 & 0.6623 & 0.6705 & 0.6710 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the average detection probability of each detector in homogeneous environments. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Sum of Squares** & **Degree of Freedom** & **Mean Square** & \(F\) & \(P\) \\ \hline Between groups & 8.1 \(\times\) 10\({}^{-4}\) & 2 & 4.1 \(\times\) 10\({}^{-4}\) & 30.8367 & 1.0671 \(\times\) 10\({}^{-7}\) \\ Within group & 3.5 \(\times\) 10\({}^{-4}\) & 27 & 1.3 \(\times\) 10\({}^{-5}\) & & \\ Total & 1.2 \(\times\) 10\({}^{ronment, which has better detection performance than that of the HCE detection algorithm used in VIHCEMOS-CFAR in homogeneous environments. Therefore, KLQVI-CFAR can still maintain a high detection probability when the environmental type is misjudged. The detection probability of each detector is reflected in its target recognition capability. A higher detection probability is associated with a stronger detection ability, while a lower detection probability is indicative of weaker performance, potentially resulting in the failure to effectively recognize targets. Therefore, in order to more intuitively compare the detection performance of various detectors in a homogeneous environment, the detection probability of each detector is given as shown in Figure 4.  
As can be seen in Figure 4, the curves from top to bottom are CA, KLQVI(6,3), VIHCEMOS, IBQ(6,3), KLQ(6,3) and OS(28), respectively. The order of the above curves represents the performance of different detectors from high to low in homogeneous environments. Thus, it can be seen that in homogeneous environments, CA-CFAR has the best detection performance, while OS(28) has the worst detection performance. Experimental results show that the FOSTA algorithm can compensate for the performance degradation of VI-class CFAR detectors in homogeneous environments caused by environmental misjudgment. Figure 4: The detection probability of each detector in homogeneous environments. Figure 3: Environmental misjudgment probabilities of VIHCEMOS-CFAR and KLQVI-CFAR in homogeneous environment.
# A Lab-Made E-Nose-MOS Device for Assessing the Bacterial Growth in a Solid Culture Medium
## 2 Materials and Methods  
### Bacterial Strains and Inoculum Preparation  
Two Gram-positive spherical-shaped (_S. aureus_ ATCC653 and _E. faecalis_ ATCC29212) and one Gram-negative rod-shaped (_P. aeruginosa_ ATCC15442) bacteria were selected as target food-borne pathogens. _E. coli_ ATCC29998 was also included in this study since it is a hygiene/fecal indicator. As previously described [29], the _inocula_ were prepared by mixing 300 \(\upmu\)L of glycerol (Sigma-Aldrich, St. Louis, MO, USA) with 700 \(\upmu\)L of an overnight bacterial culture. The growth was promoted in Brain Heart Infusion (BHI) (PanReac ApplChem, ITW Reagents, Barcelona, Spain) at 37 \({}^{\circ}\)C, under orbital agitation at 90 rpm (Orbital incubator S1500, Stuart). Each _inoculum_ was cryopreserved at -20 \({}^{\circ}\)C until being used.
## 3 Results and Discussion
### Fourier-Transform Infra-Red (FTIR) Spectroscopy  
Figure 9A-D shows the FTIR spectra of EDA, CDs-EDA, L-Ph, and CDs-LPh obtained to ascertain the functional group present on the CDs. The model is showing the absorption bands of EDA (A) and CD-EDA (B) with the EDA spectra showing peaks at 3357.4 and 3288 cm\({}^{-1}\), which correspond to -N-H stretching of primary aliphatic amines [39, 89, 90]. FTIR with medium or small and broad absorption bands around 3500-3200 cm\({}^{-1}\) could correspond to N-H aliphatic amine [91] but, the peaks were absent in L-Ph maybe because there was only one molecule of -NH\({}_{2}\) in L-Ph, making NH\({}_{2}\) less abundant to be detected, the broad and medium absorption band at 2964.1 cm\({}^{-1}\) in L-Ph is due to -OH of carboxylic acid stretching. A functional group appeared around 1650-1590 cm\({}^{-1}\) for all the bands, which corresponds to the -NH\({}_{2}\) scissoring vibration of a primary amine. The amino-functional group appeared in all the four bands, signifying the presence of amine in the EDA, CDs-EDA, L-Ph, and CDs-LPh. By extension, this confirmed the functionalization of amine on the surface of the carbon dots (CDs-EDA and CDs-Lph), respectively. The CDs show similar broad and intense peaks at 3280.4 cm\({}^{-1}\) (CDs-EDA) and 3322.8 cm\({}^{-1}\) (CDs-LPh), corresponding to normal polymeric hydroxyl (O-H) stretching vibration, an indication that the CDs has -OH functional group. The other peaks at the fingerprint region at 1360.0 cm\({}^{-1}\) (CDs-LPh), 1405.9 cm\({}^{-1}\), 1085.7 cm\({}^{-1}\), 1041.4 cm\({}^{-1}\), and 879.4 cm\({}^{-1}\) could be due to sp\({}^{2}\) and sp\({}^{3}\) stretching vibrations and out-of-plane bending modes of C-H groups [1, 92].  
Figure 8: UV-Visible spectra of (**a**) CDs-EDA and (**b**) CDs-LPh.
# LoRaWAN Communication Protocols: A Comprehensive Survey under an Energy Efficiency Perspective
## 3 Communication Protocols
### Multi-Access Protocols
#### 3.2.1 Enhanced ALOHA  
Enhanced ALOHA protocols make modifications on the top of the ALOHA standard approach used by LoRaWAN to address ALOHA issues without changing the basic functionality in LoRaWAN.  
In [38], the authors introduced a new MAC protocol, named reliability scalability-LoRa (RS-LoRa), which dynamically specifies the SF and TP to reduce collisions and improve network reliability and scalability. They designed a two-step lightweight scheduling algorithm, where, in the first step, the GW sends a beacon to synchronize the nodes within its cell and specifies the allowed SF and TP for each node to allow concurrent transmissions. In the second step, depending on the coarse-grained information supplied by the GW, nodes select their own SF and TP settings combination and channel. Nodes transmit their packets in an ALOHA manner. Through this approach, the collisions are reduced, and the reliability is improved by decreasing the packet error rate. Improved network reliability can further improve network scalability. Finally, the throughput increases as the network reliability improves. However, this approach comes with the cost of additional energy consumption.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  
In [55], the authors presented a slotted ALOHA scheme to avoid collisions by splitting the channel time into slots. It is fundamental to synchronize the end-devices according to the GW's clock. To this end, GW and end-device save a timestamp of the end time of the uplink transmission. GW piggyback its timestamp in the ACK packet. The end-device calculates the offset between the timestamp of the transmission and the reception of the ACK packet, and the end-device updates its clock accordingly. This scheme improves throughput, reduces packet collisions but slightly increases energy consumption.
# Distributed Orbit Determination for Global Navigation Satellite System with Inter-Satellite Link
## 5 Distributed Orbit Determination
### Reduced-Order Increased Measurement Covariance EKF  
To accelerate the data-processing in the ICEKF method, the reduced-order increased measurement covariance EKF (IMCEFK) [12] is carried out. This method includes the error of the state vectors of the _jth_ satellite into the measurement covariance matrix \(R_{k}^{i}\) between the _ith_ satellite and the _jth_ satellite. If we regenerate the ISL measurement error in Equation (42) as \(\varepsilon_{k}^{i}=\mathbf{H}^{j}\delta\mathbf{x}_{k}^{j}+\varepsilon^{ij}(t _{k})\), then the corresponding measurement covariance matrix is:  
\[\begin{split} E\big{[}\varepsilon_{k}^{i}\varepsilon_{k}^{i} \big{]}&=E\bigg{[}(\mathbf{H}_{k}^{j}\delta\mathbf{x}_{k}^{j}+ \varepsilon^{ij}(t_{k}))(\mathbf{H}_{k}^{j}\delta\mathbf{x}_{k}^{j}+ \varepsilon^{ij}(t_{k}))^{\mathrm{T}}\bigg{]}\\ &\hskip 28.452756 pt=\mathbf{H}_{k}^{j}\overline{\mathbf{P}}_{k}^{j} \mathbf{H}_{k}^{\mathrm{T}}+\mathbf{R}_{k}^{i}\end{split} \tag{50}\]  
where \(\overline{\mathbf{P}}_{k}^{j}\) is the state vector covariance matrix of the _jth_ satellite. Equation (50) implies that ISL measurements contain not only the measurement errors, but also the _jth_ satellite state vector error; thus, the measurement covariance matrix is assembled as:  
\[R_{k\hskip 28.452756 ptassembled}^{i}=\mathbf{H}_{k}^{j}\overline{\mathbf{P}}_{k}^ {j}\mathbf{H}_{k}^{j\mathrm{T}}+\mathbf{R}_{k}^{i} \tag{51}\]  
where the subscript _assembled_ indicates that it is an assembled measurement covariance matrix.  
Next, the \(R_{k}^{i}\) in Equation (44) is replaced by \(R_{k~{}amp}^{i}\), and the gain matrix becomes:  
\[K_{k}^{i}=\overline{P}_{k}^{i}H_{k}^{\mathrm{T}}\left(H_{k}^{\mathrm{T}} \overline{P}_{k}^{i}H_{k}^{i}+H_{k}^{i}\overline{P}_{k}^{i}H_{k}^{\mathrm{T}}+ R_{k}^{i}\right)^{-1} \tag{52}\]  
After repeating the steps in Equations (42), (43), (51), (45)-(47), the orbits for _ith_ satellites are determined. In this way, a reduction of the number of iterations is expected. The computation amount of IMCEKF is:  
\[4N_{i}^{2}(N_{i}^{2}-1)+(N_{i}-1)N_{i}(N_{i}+1)/6+(2N_{i}^{2}+7N_{i}+1)\times(m /n) \tag{53}\]  
In some situations, iteration may not be required [18]. To summarize, compared to ICEKF, the IMCEKF is a reduced-order approach and needs to transmit not only the local state vector but also its covariance matrix to the other satellites.
## 5 Discussion  
Our work focused explicitly on existing DPIA and FRIA frameworks, with the ultimate goal of providing a unified framework covering both these tools; revisiting our research questions stated in Section 1.2, the following conclusions are derived:  
1. The DPIA (in the AI domain) and the FRIA aim to proactively identify and mitigate risks to human rights. The DPIA is focused on data protection and privacy, leveraging a structured, regulatory-driven methodology, whereas the FRIA broadens the scope to encompass a range of fundamental rights beyond privacy, adopting a more principle-based approach. On the other hand, the DPIA may be obligatory for processes that do not employ the use of AI systems.
2. Merging DPIA and FRIA methodologies into a unified framework involves harmonizing their structured and principle-based approaches to ensure comprehensive risk evaluation. Such an integrated framework needs to consider their complementary strengths as well as their strong interconnections, facilitating a thorough assessment that encompasses both data protection as well as other fundamental rights. In any case, key elements include a detailed analysis of the AI system's impact on fundamental rights, a clear documentation process, and mechanisms for continuous monitoring and updating in response to new insights or system changes.
* Stakeholders in the EU, by adopting such a framework, would benefit from several advantages, including a comprehensive approach to risk assessment that aligns simultaneously with more than one regulatory requirement and ethical standards, potentially enhancing public trust in AI systems. The integrated framework addresses compliance with the GDPR and the AI Act by providing a comprehensive tool for evaluating both data protection and fundamental rights impacts, thereby promoting ethical AI system utilization. This approach ensures that AI systems are developed and deployed in a manner consistent with EU regulatory frameworks and ethical considerations. However, obstacles such as the complexity of integrating methodologies, resource constraints, and the need for specialized knowledge to apply the framework effectively could arise; as it became evident, the role of the Provider of the system is also crucial in conducting a proper assessment.
* By fostering a comprehensive evaluation of transparency, accountability, and impacts on fundamental rights, including data protection and privacy, the proposed framework aims to contribute significantly to enhancing public confidence in AI technologies. Therefore, it may encourage the development of AI systems that are not only compliant with legal standards, but also aligned with ethical principles, thereby supporting the responsible use of AI across diverse industries. Making the output of the FRIA/DPIA publicly available, the overall transparency, as well as the user's trust, will be increased, whilst at the same time, the relevant stakeholders will be obligated to indeed implement what they are supposed to do.
## 3 Spectral Library and Dataset
### Radiometric Calibration  
Raw hyperspectral image consists of the digital counts the HSI imager captured at different wavelengths. It can be seen as the sum of upwelling radiance reflected from the objects, reflections from scatters in the air and ambient light. Besides, there is noise inherent in the sensor [1]. Reflectance is a percentage of how much light at each wavelength is reflected from the objects, removing the effect of illuminator and optical properties of the air column [1]. Assuming all surfaces of the objects behave like Lambertian reflectors, the reflectance at a specific wavelength \(\lambda\) can be expressed as:  
\[R(\lambda)=\frac{L_{\text{u}}(\lambda)}{L_{\text{d}}(\lambda)} \tag{1}\]  
Figure 2: A selection of the objects made of materials likely to be present at realistic scenarios. (**a**) Photo of the objects. (**b**) Positions of different objects representing by different colors.  
here, \(L_{\text{u}}(\lambda)\) is the upwelling radiance, and \(L_{\text{d}}(\lambda)\) is the downwelling radiance. However, the aforementioned result demonstrates that SC laser spot has a non-uniform intensity and spectrum components distribution; thus, it is inappropriate to use the space-independent radiance to calculate the reflectance of objects. A previous study [2] has given a clear argument about it. Considering the nonuniformity of laser spot, the reflectance at a given wavelength should be modified as:  
\[R(\lambda)=\frac{L_{\text{u}}(x,y,\lambda)}{L_{\text{u\_ref}}(x,y,\lambda)} \times R_{\text{ref}}(\lambda) \tag{2}\]  
where \((x,y)\) represents the location of the pixel. \(L_{\text{u}}(x,y,\lambda)\) and \(L_{\text{u\_ref}}(x,y,\lambda)\) are the space-related spectral upwelling radiance from objects and reference plate, respectively, and \(R_{\text{ref}}(\lambda)\) is the reflectance spectrum of the Spectralon reflectance standard plate.  
Raw hyperspectral data cube of the objects captured by active HSI system in the dark is shown in Figure 3a. After calibration by Equation (2), the resulting data cube is shown as false-color map in Figure 3b. In Figure 3b, there is a circle of blue area, which indicates an obvious nonuniformity between the edge and center of the laser spot. The phenomenon can be attributed to the position misalignment of laser spot on reference plate and objects, and the distance of objects to the reference plate. The calibrated data cube is used to make the spectral library and dataset.
## 3 Grid-Based Event Detection
### Isolating Faulty Nodes Using Confidence Level Evaluation
Hence a sensor node \(v_{k}\) with a permanent fault will gradually lose its confidence level \(c_{k}\), and then be isolated from the rest. On the other hand, if the isolation is due to transient or intermittent faults, the node can be reinstated when the behavior of the node changes later such that its confidence level reaches the upper bound (1 in the simulation).
# Depth Estimation from a Hierarchical Baseline Stereo with a Developed Light Field Camera
## 4 Experimental Results
### Results on Real-World Scenes  
In order to test the effectiveness of the proposed method, qualitative evaluations are performed on real-world scenes using the capturing system with a lab-designed camera (Figure 2c).  
Figure 5 shows depth estimation results for objects in a pseudo-color manner. The depth values are not in the same range due to different label numbers and settings, with a simple rescale operation applied for [11; 12; 13]. Although it is hard to correctly transform the estimated values into a unified range without exact imaging parameters, the depth details can still be easily identified from Figure 5 by the pseudo-color representation.  
As we know, most filters are used to generate smooth and continuous depth maps based on surrounding neighboring pixels. Depth discontinuities refer to abrupt or sudden changes corresponding to boundary edges between surfaces. In [12], over-filtering leads to depth discontinuities being blurred, creating ambiguities for thin shapes.  
Another difficulty lies in densely changing textured surfaces. It is challenging to discern whether pixels with repeated textures are on the same depth plane or different depth planes. And ambiguities often occur over shadows and large specular reflection surfaces.  
By exploiting light field imaging, which captures more rays and generates high-dimensional data, the proposed hierarchical baseline stereo-matching method aims to recover accurate geometric details and handle illumination changes rapidly (in less than one second).  
Figure 5: Experimental results for real-world samples. (**a**) Central sub-aperture image. (**b**) [PERSON] et al. [11]. (**c**) [PERSON] et al. [12]. (**d**) [PERSON] et al. [13]. (**e**) Our results.  
Figure 6 shows depth estimation results for the hand gesture with point cloud outputs. Objects with strongly textured Lambertian surfaces under uniform lighting are the best cases for the proposed framework. Because of the angular viewpoint difference in the light field raw images, we can recover accurate local geometric details for a small object at a distance.
## 4 SAR Raw Signal Processing
### Omega-k Algorithm for LFM-CW SAR System  
In this paper, the raw signal processing of a continuous wave linear frequency modulated synthetic aperture radar is based on the Omega-k algorithm; therefore, we modifiedthe omega-k algorithm as shown in Figure 9, and each one of the blocks is described in the following sections.
# Experimental Test of Continuous Wave Frequency Diverse Array Doppler Radar +
## 2 Data Collection I: Far-Field Beampattern
### Far-Field Signal Model  
Our signal model follows Correll and Rigling in [10,11], where the theoretical FDA far-field pattern at a point having radius \(R\) and angle \(\theta\) at fast-time \(t\) is the coherent summation  
\[F(t;R,\theta)=\sum_{n=0}^{N-1}e^{2\pi f_{n}(t-R_{n}/c)}. \tag{1}\]  
Here, \(f_{n}=f_{0}+n\Delta f\), where \(f_{n}\) is the transmit frequency at the \(n\)th element of a linear FDA having carrier frequency \(f_{0}\), frequency offset parameter \(\Delta f\), \(N\) total elements, and element indexing \(n=0,1,...,N-1\). \(R_{n}=R-nd\sin\theta\), where \(R_{n}\) is the distance from the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Label** & **Description** \\ \hline Collection I & Far-field Beampattern \\ Collection II & Stationary Target Reflection \\ Collection III & Moving Target Reflection \\ Collection IV & micro-Doppler Signature \\ \hline \hline \end{tabular}
\end{table}
Table 1: Labeling and descriptions of data collections in this paper.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Component** & **Manufacturer/Part \#** & **Collection** \\ \hline Log-Periodic Tx Antenna & WASVJB 0.85-6.5 GHz & I, II, III \\ Log-Periodic Rx Antenna & Walfront 0.6–6.0 GHz & I, II, III \\ Log-Periodic Tx Antenna & WASVJB 2.1–11 GHz & IV \\ Log-Periodic Rx Antenna & WASVJB 2.1–11 GHz & IV \\
2-way Power Splitter & Minicircuits ZAPD-30-S+ & I, II, III \\
4-way Power Combiner & Minicircuits ZN4 PD1-50-S+ & I, II, III \\ \hline \hline \end{tabular}
\end{table}
Table 2: RF component list for the transmit (Tx) antennas, receive (Rx) antennas, power splitters, and power combiners.  
\(n\)th transmit element to the far-field point \((R,\theta)\) for an array with inter-element spacing \(d=\lambda_{0}/2\).  
Note that Equation (1) is used to track the coherent combination of the far-field pattern at a given point \((R,\theta)\) and does not contain a scalar to model the fall-off of incident power. Additionally, this idealized model assumes isotropic radiation and no mutual coupling, no amplitude variation, no phase noise, and no unknown frequency offsets or element location inaccuracies.
# CT-RPL: Cluster Tree Based Routing Protocol to Maximize the Lifetime of Internet of Things
## 3 Cluster Tree-Based Routing Protocol  
The paper proposes a cluster tree-based routing protocol to enhance the lifespan of the network. CT-RPL involves three processes: (i) cluster formation, (ii) CH selection, and (iii) route establishment. The virtual clusters are created according to the Euclidean distance of the network nodes. The CH selection is carried out by using a game theoretic approach. Finally, the route establishment process uses the routing metrics, namely queue utilization and the expected transmission count, to pick the optimum CH parent for effective data transfer.