esa-sceva/satcom-chunk-collection huggingface dataset:
# Privacy-Preserved Visual Simultaneous Localization and Mapping Based on a Dual-Component Approach
## 3 Method
### Approach Overview  
Our approach comprises an image inpainting method and a point cloud encryption algorithm, as illustrated in Figure 1. After privacy preservation, each uploaded video frame \(I_{gt}\) is first processed by the image inpainting method to inpaint privacy-sensitive areas. The method initially performs object detection using the you only look once (YOLO)v8 network to identify the location of privacy-sensitive areas within \(I_{gt}\). Compared to two-stage detectors such as Mask R-CNN [21], single-stage detectors like the YOLO series models offer better real-time performance and lower computational resource requirements.  
Therefore, YOLOv8 is more suitable for tasks such as SLAM, which demand real-time processing and can be deployed in resource-constrained environments, such as mobile devices. The resulting image with privacy-sensitive areas masked is then passed to the EdgeConnect network. The EdgeConnect network first generates the contour prediction \(C_{pred}\) of privacy-sensitive areas using the contour generator \(G_{1}\) and then generates the image \(I_{pred}\) after inpainting privacy-sensitive areas using the image complementation network \(G_{2}\). Compared to DeepFill v2 [22], EdgeConnect leverages image edge information to generate more stable results for the areas to be repaired. Moreover, in contrast to Stable Diffusion [23], which focuses more on stylized creation, EdgeConnect is better suited for generating content that aligns with the original image style. Additionally, EdgeConnect requires fewer computational resources, making it more suitable for integration with SLAM tasks. The privacy-protected image is then inputted into ORB-SLAM3, which generates a 3D point cloud \(P\) of the scene. In order to protect \(P\), it is processed by the point cloud encryption algorithm, which generates a 3D line \(l_{i}\) with a random direction and normalized length for each 3D point \(p_{i}\). Finally, the encrypted 3D line cloud \(L\) is obtained by replacing each 3D point \(p_{i}\) with the corresponding 3D line \(l_{i}\), and then returned to the user.
## 2 Materials and Methods
### Signal Loss and Residual Signal Calculation  
For each filtered signal, mean signal loss (%) and residual signal (%) were calculated using the RMS following [PERSON] et al. [16] and [PERSON] et al. [5], respectively. Signal loss was determined using the Formula (4), while residual signal (%) was calculated using the Formula (5):  
\[Signal\ Loss\ (\%)=\frac{\left(RMS_{full}-RMS_{filter}\right)}{RMS_{full}} \tag{4}\]  
\[Residual\ Signal\ (\%)=\frac{RMS_{filter}}{RMS_{full}} \tag{5}\]  
where RMS\({}_{\text{full}}\) is the mean RMS value of the raw signal and RMS\({}_{\text{filter}}\) is the mean RMS value of the filtered signal (low-pass 10 Hz, bandpass 40-450 Hz, and bandpass 7-200 Hz).
# Radiator Enablers for Wireless Communication Evolution
## 3 Radiators' Novel Technologies, Design, and Implementation Issues
### Holographic Metasurfaces
#### 3.3.3 Antenna Element Selection  
In a typical dynamic metasurface antenna design, for operation in the 60 GHz regime presented in (ref. [72]), the selection and arrangement of antenna elements are integral to holographic metasurfaces. It uses complementary electric inductive-capacitive-type (CELC) metamaterial elements with integrated PIN diodes, enabling 1-bit reconfigurability. Such a design allows for real-time, field programmable gate array-based (FPGA) beam steering and, therefore, generates beam patterns for users in applications requiring integration with sensing, imaging, and contactless monitoring. Tensor holographic metasurfaces (ref. [77]) further extend this innovation toward generating dual beams with different polarization states, namely, linear polarization (LP) and right-handed circular polarization (RHCP), with high gain and low reflection coefficients. In particular, the use of dense antenna arrays in HMIMO systems has also been highlighted to enable near-continuous apertures that are crucial for spatial multiplexing and the precise control of electromagnetic waves in near-field communications (ref. [78]). These works represent the flexibility and effectiveness of antenna elements in solving the problems posed by 6G systems (ref. [79]).
## 3 Scattering Matrix with Grouping Scheme and Power Allocation
### Scattering Matrix with Grouping Scheme of the RIS Elements  
When the RS power ratio is given, Equation (11) can be directly optimized by the quasi-Newton method [27] because the problem is an unconstrained optimization problem. However, the computational complexity increases as the number of RIS elements increases. In this paper, the problem was optimized sequentially for each group to reduce the complexity. Then, the sub-problem is given as follows:  
\[\max_{\mathbf{\Phi}_{g}} R_{g}\] (15) s.t. (12)-(14),  
where \(R_{g}\) is the sum-rate of the effective channel corresponding to the group. The channel can be expressed as \([\mathbf{h}_{k}]_{1,\mathcal{S}_{g}}\mathbf{\Phi}_{g}[\mathbf{G}]_{\mathcal{S }_{g},1:N_{T}}\).  
Algorithm 1 specifies the overall optimization of Equation (11) with the grouping of RIS elements scheme. To obtain the grouping strategy, the scattering matrix needs to be initialized. In this paper, the conventional RIS scattering matrix that maximizes the channel gain was used as the initial scattering matrix. After the initialization of the scattering matrix, user scheduling is performed in descending order of the effective channel gain. By performing user scheduling, the scattering matrix can be more adaptive to each UE channel. Then, each RIS element is grouped by allocating the RIS element having the largest channel gain for each UE according to the user scheduling. In this scheme, the number of groups should be greater than the number of UEs in the system. Figure 2 shows the grouping strategy obtained through the proposed grouping scheme and the grouping between adjacent RIS elements represented by general grouping. In Figure 2, the scattering matrix index is based on the index of the figure, and the RIS elements in the same group appear in the same color, e.g., the 1 st, 3 rd, 6 th, and 8 th elements of Figure 2b are grouped into the same group.  
**Input:**\(\mathbf{H}\), \(\mathbf{G}\), \(\mathbf{P}\), \(N_{I}\), \(N_{G}\), \(N_{K}\), \(P_{T}\), \(\sigma_{k^{2}}\), \(\forall k\)  
**Output:**  
User scheduling: \(\widehat{\mathbf{K}}\)  
Grouping strategy: \(\mathcal{S}_{1},\mathcal{S}_{2},\cdots,\mathcal{S}_{N_{G}}\)  
Scattering matrix: \(\mathbf{\Phi}\)  
```
1:Initialize \(\mathbf{\Phi}\)
2:for\(k=1:N_{K}\)do
3:\(\widehat{\mathbf{K}}[k]=\underset{k}{\max}\|\mathbf{h}_{k}\mathbf{\Phi}\mathbf{G }\|^{2}\)
4:\(\left\|\mathbf{h}_{\widehat{\mathbf{K}}[k]}\mathbf{\Phi}\mathbf{G}\right\|^{2}=0\)
5:endfor
6:for\(g=1:N_{G}\)do
7:\(\widehat{g}=g\) mod \(N_{K}\)
8:for\(i=1:N_{I}/N_{G}\)do
9:\(\widehat{m}=\underset{m}{max}\left\|\mathbf{h}_{\widehat{\mathbf{K}}[\widehat {g}]}\mathbf{\Phi}_{m,m}\mathbf{G}\right\|^{2}\)
10:\(\mathcal{S}_{g}=\mathcal{S}_{g}\cup\{\widehat{m}\}\)
11:endfor
12:\(\mathbf{\Phi}_{g}\leftarrow\) Solving Equation (15) by quasi-Newton method
13:endfor
```  
**Algorithm 1** Optimization scattering matrix.  
Figure 2: (**a**) General grouping; (**b**) proposed grouping; the numbers in the figure indicate the index of RIS elements.
# Non-Orthogonal eMBB-URLLC Radio Access for Cloud Radio Access Networks with Analog Fronthauling
###### Abstract  
This paper considers the coexistence of Ultra Reliable Low Latency Communications (URLLC) and enhanced Mobile BroadBand (eMBB) services in the uplink of Cloud Radio Access Network (C-RAN) architecture based on the relaying of radio signals over analog fronthaul links. While Orthogonal Multiple Access (OMA) to the radio resources enables the isolation and the separate design of different 5G services, Non-Orthogonal Multiple Access (NOMA) can enhance the system performance by sharing wireless and fronthaul resources. This paper provides an information-theoretic perspective in the performance of URLLC and eMBB traffic under both OMA and NOMA. The analysis focuses on standard cellular models with additive Gaussian noise links and a finite inter-cell interference span, and it accounts for different decoding strategies such as puncturing, Treating Interference as Noise (TIN) and Successive Interference Cancellation (SIC). Numerical results demonstrate that, for the considered analog fronthauling C-RAN architecture, NOMA achieves higher eMBB rates with respect to OMA, while guaranteeing reliable low-rate URLLC communication with minimal access latency. Moreover, NOMA under SIC is seen to achieve the best performance, while, unlike the case with digital capacity-constrained fronthaul links, TIN always outperforms puncturing.  
network slicing; RoC; URLLC; eMBB; C-RAN Article
## 2 Materials and Methods
### Determination of the Pixel Color
#### 2.3.3 Determining the Hues of Each Pixel  
The bands of the spectral reflection of the ground object of the \(j\)-th class are reduced to three bands using manifold learning methods. These three bands are denoted as \(V^{\prime}_{j}\), \(S^{\prime}_{j^{\prime}}\), and \(I^{\prime}_{j}\) according to the significance of the bands, and each of them have the size \(a\times b\). To fully utilize the color space and have better visual separability, a separation factor \(r\) is introduced into the pixel hue determining method to adjust the differences between similar features.  
Figure 2: The HSV (hue, saturation, and value) color system.  
The separation factor \(r\) represents the floating limitation of each pixel's hue around the hue class. A higher \(r\) indicates a greater difference between pixel hues in the same class, along with more chromatism, and a better distance-preserving property of the output image. In addition, a lower \(r\) indicates greater differences among the classes of the output image. Theoretically, the range of \(r\) can be set as [0, 0.5]. When \(r~{}=~{}0\), all the pixel hues are equal to the hues of their relevant classes. When \(r~{}=~{}0.5\), the fluctuation range of pixels in each class is exactly half of the hue distance between classes; at this time, the range of all pixels of each class occurs in the entire hue space of the HSV color space. When \(r>0.5\), the range of pixels representing different classes in the images will cross. Therefore, we should avoid values within this range except for particular demands. Consequently, to obtain clearer display results, a separation factor \(r_{j}\) of the \(j\)-th class is described by the following equation:  
\[H_{j,i}~{}=~{}r_{j}I_{j,i}h+h_{j} \tag{2}\]  
\[0<r_{j}\leq 1/3 \tag{3}\]  
where \(H_{j,i}\) is the hue value of the \(i\)-th pixel in the \(j\)-th class, \(I_{j,i}\) is the result of normalized \(I_{j}\) of the \(i\)-th pixel to \([-1,1]\), and \(h\) is the category label color difference. Normally, for all categories, \(r_{j}\) is selected as the same value regardless of \(j\).
## 4 Experiment Setup
### Dataset  
#### 4.2.1 CWRU Dataset  
The CWRU dataset [29], provided by the Case Western Reserve University Bearing Data Center, is a widely adopted benchmark for bearing fault diagnosis. It contains vibration signals collected under various fault types and motor load conditions. The dataset includes the following four categories:  
* Normal baseline data;
* 12 K drive end bearing fault data;
* 48 K drive end bearing fault data;
* Fan end bearing fault data.  
In this study, we utilized the CWRU dataset for two primary experimental setups. Firstly, for a four-class classification task, we focused on the 12 K drive end bearing fault subset. This subset includes fault types such as inner race (IR), outer race (OR), and ball (B) faults, along with normal (N) samples. Data for these conditions were collected under four different motor speeds: 1797, 1772, 1750, and 1730 revolutions per minute (RPM). The selected samples were grouped into four classes: Normal, IR, OR, and Ball. The class distribution for this four-class setup is as follows: 828 Normal samples, 709 IR samples, 708 Ball samples, and 1652 OR samples.  
Secondly, to enable a more rigorous evaluation of fine-grained fault diagnosis capabilities and to constitute a more challenging task, we configured a 10-class classification task, also derived from the drive-end bearing data. For this 10-class setup, the number of samples for each class was fixed at 50, creating a more difficult scenario with limited data per class. This setup includes the normal state and three distinct fault sizes (0.007\", 0.014\", and 0.021\") for each of the three fault types: ball bearing, inner race, and outer race. This fine-grained classification, with a balanced and limited number of samples per class, allows for a more precise assessment of SpikeCAN's ability to distinguish between various fault types and severities under more demanding conditions.
## 2 Background  
### Scatter-Gather Implementation  
The main operation of RSA decryption (or sign) is the modular exponentiation; calculate \(b^{e}\bmod n\) for a secret exponent \(e\). OpenSSL library performs the modular exponentiation by a fixed-window exponentiation algorithm [22] (See Algorithm 1). In a pre-computation phase, the algorithm computes a set of multipliers \(m_{i}=m^{j}b\bmod n\) for \(0\leq j<2^{w}\), where \(w\) is a window size. In an exponentiation phase, it scans each fraction of \(e\) of size \(w\) from \(e_{\lceil k/w\rceil}\) to \(e_{0}\). For each digit \(e_{i}\), it multiplies \(r\), the intermediate result from squaring, by the pre-computed multiplier \(m_{e_{i}}\). In the OpenSSL library, the window size is set to \(w=5\), so there are 32 multipliers in total.  
```
1:\(k\)-bit exponent \(e=\sum_{i=0}^{\lceil k/w\rceil}e_{i}\cdot 2^{wi}\), window size \(w\), base \(b\), modulus \(n\)
2:\(b^{e}\bmod n\)
3:procedureExponentiation(\(w,b,n,e\))
4:// Pre-computation phase
5:\(m_{0}\gets 1\)
6:for\(i\gets 1\)to\(2^{w}-1\)do
7:\(m_{i}\gets m_{i-1}\cdot b\bmod n\)
8:endfor
9:// Exponentiation phase
10:\(r\gets 1\)
11:for\(i\leftarrow\lceil k/w\rceil-1\)to\(0\)do
12:for\(j\gets 1\)to\(w\)do
13:\(r\gets r^{2}\bmod n\)
14:endfor
15:\(r\gets r\cdot m_{e_{i}}\bmod n\)
16:endfor
17:return\(r\)
18:endprocedure
```  
**Algorithm 1** Fixed-window exponentiation  
Scatter-gather implementation is a constant-time programming technique to avoid secret-dependent access at the cache line granularity [23]. Instead of storing multipliers consecutively in memory, it scatterseach multiplier across multiple cache lines (Figure 1). When using the multiplier (i.e., in gathering phase), the fragments of the required multiplier are gathered to a buffer for the multiplication.
## 2 Materials and Methods
If not specified below, the default design parameters are \(R_{in}\) = 600 \(\upmu\)m, \(N\) = 4.5, \(t_{me}\) = 2 \(\upmu\)m, \(t_{ox}\) = 0.6 \(\upmu\)m, \(W\) = 30 \(\upmu\)m, and \(S\) = 10 \(\upmu\)m.  
Four-probe method was used to evaluate the electrical conductivities of the metal multilayers, which are 1 \(\times\) 1 cm\({}^{2}\) in size with a total thickness of 2 \(\upmu\)m where Al or Cu layer were kept as 1.7 \(\upmu\)m. A total of 12 samples (3 samples for each kind of the metal multilayer) were fabricated to check the consistency and repeatability of the measurement results. Pure 2 \(\upmu\)m single-layer Al and Cu were also included as control groups in the experiment. The distance between two adjacent probes was fixed at 1 mm. The measurement results were imported into the material library of high frequency structure simulator (HFSS) to predict the characteristics of spiral inductors integrated on sapphire before fabrication process. In addition, Auger electron spectroscopy (AES) combined in-situ ion sputtering (PHI 710, ULVAC-PHI, Inc., Chigasaki, Japan) was used to obtain the in-depth profiles of the multilayers. Diverse Ar\({}^{+}\) ion bombardment energies were adopted to etch the sublayers with different thicknesses in order to obtain clear interfaces. Figure 1: (**a**) Schematic diagram depicting fabrication process for a 4.5-turn inductor; (**b**) Optical and segmental microscope images of multiple spiral inductors on a 2-inch sapphire. (M1: Cr/Pt/Au, M2: Cu or Al-based metal multilayers, RF: radio-frequency). On-wafer \(S\)-parameters measurements were carried out using an E5071C network analyzer with a Cascade Microtech M150 probe station, and the system was calibrated using the SOLT technique in the range of 300 MHz to 3 GHz. The apparent inductance (\(L\)), apparent resistance (\(R\)), and quality factor (\(Q\)) [16; 17; 18] were calculated using the following equations, where \(Y_{11}\) was the two-port admittance matrix of the de-embedded coil. \[L=\frac{\text{Im}\left(-\frac{1}{Y_{11}}\right)}{2\pi f} \tag{1}\]  
\[Q=\frac{\text{Im}\left(\frac{1}{Y_{11}}\right)}{\text{Re}\left(\frac{1}{Y_{11 }}\right)} \tag{2}\]  
\[R=\text{Re}\left(\frac{1}{Y_{11}}\right) \tag{3}\]  
Modified equivalent physical model and analytical formulas were constructed in Agilent Advanced Design System (ADS), in which iterative optimization for the elements was performed to extract equivalent parameter values. This process could be time-saving and flexible, benefitting from the frequency-independent elements.
## 4 Advanced SensorNC: In-Network Processing
### Numerical Experiment
#### 4.4.2 Benefit of End-To-End Analysis  
At first, in a baseline comparison, minimum and maximum scaling curves are assumed to be identical and pass through the origin. In Figure 7, we can observe the SensorNC outcomes in terms of delay bounds for the holistic and component-wise methods for 10 different random topologies with 100 nodes each.  
The holistic PMOO is superior to the TFA, which results in delay bounds up to 1.9-times higher than for PMOO.  
Sometimes, we cannot assume identical minimum and maximum scaling curves as in the previous experiment because of non-determinisms in the actual processing. However, often, we can use upper and lower bounds on the amount of processing that is required, i.e., we can model this by differing minimum and maximum scaling curves. However, this bounding may adversely effect the performance of the PMOO analysis method. In order to provide more insights into this, we next changethe minimum and maximum scaling curves to be no longer passing through the origin, but move the maximum scaling curve a bit upwards and shift the minimum scaling curves to the right. Therefore, they are drifting apart from each other, i.e., we set the bucket depth parameters of the maximum scaling curves, as well as the latency parameters of the minimum scaling curves incrementally higher until certain limits are reached. These limits are: for \(\overline{S}^{comm2 comp}\), the maximum bucket depth is 240; for \(\underline{S}^{comm2 comp}\), the maximum latency is 0.006; for \(\overline{S}^{sens2 comp}\), the maximum bucket depth is 300; for \(\underline{S}^{sens2 comp}\), the maximum latency is 0.006; for \(\overline{S}\), the maximum bucket depth is 0.006; for \(\underline{S}^{comp2 comm}\), the maximum latency is 240.  
In Figure 8, for both methods, we can observe the average delay bounds for 10 random topologies under different expansion factors, with the expansion factor as the percentage of how much the drifting limits have used.  
Again, the PMOO performs superior to the TFA, and a certain amount of drifting between maximum and a minimum scaling curve are reached. After this point, the TFA becomes actually better than the PMOO as the uncertainty about the scaling becomes too large. Therefore, it can be wise to perform both SensorNC analyses.
# An Overview of the Alternative Use of Seaweeds to Produce Safe and Sustainable Bio-Packaging
## 2 Potential Algal Bioactive Compounds for Bioplastic Production
### Alginate  
Alginate is a polysaccharide derived from alginic acid and its derivatives and salts [56; 57]. Alginates are anionic linear polysaccharides found in high amounts in brown seaweeds, up to 40% of the dry weight, and have been reported as able to form edible films. They are comprised of polymers of alginic acid, with monomer units of \(\beta\)-D-mannuronic acid (M) and \(\alpha\)-L-guluronic acid (G) joined by 1,4 linkages [58] (Figure 1).  
The physicochemical and mechanical properties of gels made from alginate differ based on the M/G ratio and the length of the structure; thus, a high content of galuronic acid results in stronger gelling properties, and a more elastic gel. On the other hand, low M/G ratios result in strong and brittle gels with good heat stability, but show syneresis after  
Figure 1: Chemical structure of alginic acid.  
freeze-thaw processing [56; 57]. Due to its excellent stabilizing and thickening properties, alginate is commonly used in food products and medicine [56; 59; 60].  
Alginates are highly hydrophilic; thus, it is important to combine the matrix with other elements to provide more resistance upon water contact. Moreover, the presence of ions influences the solubility of alignates, while their ability to form gels depends on type of bonds among cations [56; 57].  
The addition of calcium in the alginate matrix provides more stability and resistance to the membrane, which can be an interesting step for the development of biodegradable materials with antimicrobial properties and non-toxic packaging [59; 60].
# An Activity Theory-Based Approach for Context Analysis, Design and Evolution
## 2 Understanding Socio-Human Activities and the Context
### Contradictions Leading to Better Designs  
Analysis oriented by activity theory gives key importance to the identification and treatment of \"_contradictions_\". This has been an important topic in activity theory since its early days and is not completely unrelated to the \"_contradiction_\" concept in Marxist thought.  
This is not surprising, given that activity theory was born in the Soviet Union [31; 33]. It must be clarified that \"_contradictions_\" in Marxism are not logical contradictions, and they should be understood as \"_tensions_\" among different elements. In the end, contradictions are the driving forces behind transformations [37]. In terms of activity theory, contradictions are defined as \"_a disengagement within the elements, between them, between different activities or between different phases of development of a single activity_\" [24].  
In simple words, contradictions are situations or circumstances that modify the natural interactions between the elements of an activity system (shown in Figure 1) and its orientation towards the object [38], producing unexpected outcomes (positive or negative) as a result.  
The important thing is that, in activity theory, contradictions can be leveraged as the \"_fuel_\" that drives changes. They must not be seen as problems or failures. On the contrary, contradictions provide proof that something must evolve and transform continuously [39]. This positive conception of conflicts and inconsistencies also has a long tradition in the fields of software and requirements engineering [40].  
For example, if a design for a contact-tracking mobile application (based on location-awareness technology) is proposed to produce geo-spatial data needed for the analysis of the COVID-19 pandemic, a contradiction arises. On the one hand, for the designers, developers, and researchers, it could be a useful tool to support the visualization of disease escalation in a state of emergency (activity) and to suggest strategic solutions (object).  
However, for civil, private, public, or political groups (community), the broad monitoring of citizens can be interpreted as a dangerous violation of fundamental rights to privacy (rules).  
Thus, contradictions should lead to an \"_evolution_\" cycle by forcing all those involved to rethinking their strategies, actions, and decisions. In this way, some hypothetical scenarios could be considered, such as: _can a voluntary and civic use of the application be promoted? Should the design introduce options to activate/deactivate monitoring? Is it possible to limit the number of geo-locations that a user can provide? Can the data acquired be made anonymous to preserve the users' privacy?_