esa-sceva/satcom-chunk-collection huggingface dataset:
# Sub-Band Backdoor Attack in Remote Sensing Imagery
## 3 Investigation of All-Bands BadNets Attack in Remote Sensing Dataset
### All-Bands BadNets Attack in Remote Sensing Imagery  
As shown in Figure 1, the attacker trains the model with the poisoned dataset for a certain number of epochs. The attacker will ensure that the model has a great validation accuracy performance and a backdoor attack success rate (ASR) for the target label to keep the stealthiness of the backdoor attack. Then, the attacker will handle the model to the victim. ASR refers to the ratio of the number of successful attacks to the number of samples poisoned with backdoor triggers.  
Let the multispectral image \(x\) have \(n\) bands and \(x(i)\) denote the \(i\)th band in \(x\), we use the following functions to add the backdoor trigger to each band in the image,  
\[x^{\prime}(i)=(1-m)x(i)+m\Delta,i=1,\ldots,n \tag{1}\]  
where \(\Delta\) is a \(2D\) matrix (height, width) defining the trigger pattern, and \(m\) is the mask that represents how much information the original image needs to be overwritten by the  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Bands** & **Spatial Resolution (m)** & **Central Wavelength (nm)** \\ \hline B01-Aerosol & 60 & 443 \\ \hline B02-Blue & 10 & 490 \\ \hline B03-Green & 10 & 560 \\ \hline B04-Red & 10 & 665 \\ \hline B05-Red Edge1 & 20 & 705 \\ \hline B06-Red Edge2 & 20 & 740 \\ \hline B07-Red Edge3 & 20 & 783 \\ \hline B08-NIR3 & 10 & 842 \\ \hline B08A-Red Edge4 & 20 & 865 \\ \hline B09-Water Vapor & 60 & 945 \\ \hline B10-Cirrus & 60 & 1375 \\ \hline B11-SWIR 1 & 20 & 1610 \\ \hline B12-SWIR 2 & 20 & 2190 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specification of the EuroSat [51] dataset collected by the Sentinel-2 satellite. It consists of 13 bands, 27,000 images belonging to 10 classes with each image of size 64 \(\times\) 64.  
trigger pattern. The mask has a value of either 0 or 1, with 0 indicating that the pixel value remains unchanged, and 1 indicating that the pixel value is changed into the trigger pattern. Natural images have three channels, while remote sensing imagery typically has more than three channels; 'band' is used to denote 'channel' in the remote sensing community. Note that the trigger pattern is embedded into each band of the remote-sensing imagery.
## 3 Results
### Raw Materials Characterization
#### 3.1.1 Mineralogical Analysis  
Mineralogical analysis was carried out in order to estimate the amorphous or crystalline nature of IBA and biochar. The XRD pattern of IBA is a typical amorphous/crystalline structure. The main phases are \(\alpha\)-quartz (\(\alpha\)-SiO\({}_{2}\)), Calcite (CaCO\({}_{3}\)), and aluminosilicates as Albite (NaAlSi\({}_{3}\)O\({}_{8}\)) and Gehlenite (Ca\({}_{2}\)Al(AlSi)O\({}_{7}\)), reflecting the chemical analysis of a typical ash rich in calcium and sodium (Figure 4). The whole characterization of IBA has been reported in the literature [27].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**OXIDES** & **MK** & **IBA** & **Biochar** \\ \hline SiO\({}_{2}\) & 58.97 & 53.90 & 26.10 \\ \hline Al\({}_{2}\)O\({}_{3}\) & 34.70 & 5.67 & 2.42 \\ \hline Fe\({}_{2}\)O\({}_{3}\) & 1.40 & 2.67 & 1.27 \\ \hline CaO & 0.10 & 22.55 & 10.35 \\ \hline MgO & 0.10 & 3.40 & 0.90 \\ \hline Na\({}_{2}\)O & 0.10 & 4.03 & 0.54 \\ \hline K\({}_{2}\)O & 0.70 & 0.94 & 2.57 \\ \hline TiO\({}_{2}\) & 1.30 & 1.55 & 0.02 \\ \hline LOI \({}^{*}\) & 2.63 & 5.30 & 55.83 \\ \hline \hline \end{tabular} \({}^{*}\)(550 \({}^{\circ}\)C for biochar and 1000 \({}^{\circ}\)C for IBA).  
\end{table}
Table 2: Chemical analysis of MK, IBA, Biochar.  
Figure 4: XRD patterns of raw materials: Metakaolin (a), IBA (b) and Biochar (c) (Q = Quartz; C = Calcite; G = Gehlenite).  
Biochar is obtained at 900 \({}^{\circ}\)C, therefore its XRD pattern (Figure 4) shows a predominantly amorphous nature, visible as broad band in the 20-30 20 range, corresponding to organic carbon, as reported in [PERSON] et al. [22]. Indeed, the peak attributable to cellulose disappears when 400 \({}^{\circ}\)C is reached [48]. In this case, the only crystalline phases present can be identified mainly as Calcite (CaCO\({}_{3}\)), with traces of Quartz (SiO\({}_{2}\)), as observed in other studies [49].
## 3 Results and Discussion  
As shown in Figure 1, wine spoilage organisms were first isolated from rotten grapes using a beef extract-peptone medium or Czapek-Dox medium. The supplemented concentration of COS was then optimized by investigating the antimicrobial effects of various concentrations of COS (0, 100, 500, and 1000 mg/L) on each type of wine spoilage organism. Furthermore, the antimicrobial effects of the optimized concentrations of COS and SO\({}_{2}\) were compared. Finally, the antimicrobial effects of COS on the known wine spoilage microorganisms were estimated, and the effects of COS on the cell growths of _Saccharomyces cerevisiae_ and _Pichiapastoris_, a type of wild yeast that can cause wine spoilage, were evaluated.  
Figure 1: The workflow for investigation into whether chitoligosaccharide (COS) can be applied as an alternative to SO\({}_{2}\) in winemaking.
# Factors Affecting Cybersecurity Awareness among University Students
## 4 Results
### Description of the Independent Variable (X) Used  
#### 4.2.1 Password Security (X\({}_{1}\))  
A password is a secret set of characters or words used to authenticate access to digital systems and computer systems. A password is one of the most critical factors in protecting data and information, but it is also hazardous because it is vulnerable to attack [55]. In good computer security practice, passwords must be between 8 (eight) and 24 (twenty-four) characters long and include at least one uppercase letter, one number, and one unique character [57]. These are usually formed from frequently used words, although this is not recommended as they are easier to guess or decipher.  
From the information in Table 2, it is known that as many as 32% of students agree and 29% even strongly agree that one ought to use a strong password. However, most students (41%) disagree that passwords should be changed periodically; most students (39%) use their old passwords to create new passwords. Most students (30%) are also more likely to use one password for all websites/accounts and consider using long passwords very inconvenient. However, students are not willing to tell or share their passwords with friends. Based on these results, students still lack awareness of password security.
# A Low-Cost IoT-Based System to Monitor the Location of a Whole Herd
## Results  
### System Functioning  
The system functioning can be described as follows: (1) the GPS unit is in an idle state (low power consumption) most of the time; (2) the GPS unit is activated and gathers location data every several minutes (depending on the configuration); (3) simultaneously, the BLE communications module, which has been set up to maximize the reading range (>75 m depending on habitat characteristics), scans the surrounding tags during several seconds (depending on the configuration); (4) the collar composes the information coming from the GPS unit and the BLE module to be sent through the Sigfox communications module; and (5) the messages sent by GPS collars are received by the server of the cloud solution, where they are extracted and analyzed. First, the encoded data from the collars are decoded to recompose the collar location data and the identification-power pairs of the tags. Once this information has been extracted, it is analyzed and stored in databases.  
As mentioned in Section 2.1, GPS collars use Sigfox technology to send the data to the server. Sigfox is a LPWA network operator that uses binary phase-shift keying (BSPK) modulation in an ultra-narrow band (100 Hz) and unlicensed ISM bands, e.g., 868 MHz in Europe, 915 MHz in North America, and 433 MHz in Asia [17]. The use of Sigfox involves two challenges for the proposed solution. The first one is that the maximum size of each message is 12 bytes. The second one is the duty cycle of the transmission technology. In the case of the ISM band used by Sigfox, the limitation is a maximum use of 1% each hour, which translates into a maximum number of messages of 140 per day (every 10 min). Furthermore, the maximum number of messages is also related to energy consumption.  
Figure 2 summarizes the communication protocol of the proposed solution. The diagram shows two BLE tags, a GPS collar and the cloud solution. The BLE tags located in the animals send the advertisement messages asynchronously and at regular intervals of several seconds (Tadv). The GPS collars, placed in other animals, are normally in an idle state, but they are activated asynchronously at regular intervals of several minutes (Tmes). During this operating window, the advertisement messages sent by the BLE tags are received by the collars. To ensure the reception of the advertisement messages, the operation window must last several times Tadv. Once the advertisement messages have been received by the BLE module in the collar, they are encoded to ensure their transmission through the Sigfox network. Each message sent by the collar includes GPS location data and identifier-power pairs corresponding to the BLE tags that have been detected. Given the limitation in the size of Sigfox messages, a maximum of eight identifier-power pairs of BLE tags can be sent in each message.  
Figure 3 shows the detail of GPS and BLE devices and two examples of animals fitted with them.
# Interactions in Augmented and Mixed Reality: An Overview
## 3 Research Results
### Audio-Based Modality
#### 3.2.3 Music-Based  
Musical interactions refer to all methods related to sounds arranged in time that composite the melody, harmony, rhythm, or timbre elements. In [PERSON] et al.'s [132] paper, the authors present a technique of interacting with virtual objects to produce musical pre-recorded sounds. As the musician manoeuvers VR controllers and collides with the virtual objects, _musical feedback_ is played. In addition, the musician can interact with full-body movements to produce music, a method that can enhance musical expression. [PERSON] and [PERSON] [133], exploiting the _music visualization_ method, presented a work in progress where a user can visualize the parts of a music clip (intro, middle and outro) and individual audio components, such as kick, synthesizer or violins. Then, the user can manipulate these components, altering the final music outcome.
# Robust Face Recognition Based on a New Supervised Kernel Subspace Learning Method
## 3 Supervised Kernel Locality-Based Discriminant Neighborhood Embedding  
### Main Idea  
As already mentioned in Section 1, DNE cannot correctly preserve local information of data because it only concedes +1 to intra-class and \(-1\) to inter-class neighbors, so it might fail to find out the most significant submanifold for pattern classification. In addition, LPP is designed based on \"locality\" since it has no direct connection with classification, and it still suffers from the \"over learning of locality\" problem [17]. Therefore, LDNE has been proposed to overcome the problems existing in LPP and DNE, considering both the \"locality\" and the \"discrimination\" in a unified modeling setting. However, it does not guarantee an appropriate projection for classification purposes, because many important non-linear data might be lost during its dimensionality reduction process. In some cases, LDNE also cannot distinguish inter-class and intra-class neighbors properly in order to conduct projection for all points, which can degrade the classification performance. In order to address these problems, we propose a new supervised subspace learning method named \"supervised kernel locality-based discriminant neighborhood embedding\" (SKLDNE). Combined with nonlinear data structure, locality and discrimination information, SKLDNE can yield an optimal subspace that best finds the indispensable submanifolds-based structure.  
In our proposed SKLDNE, we first use nonlinear kernel mapping to represent the input data in implied feature space \(F\). Afterwards, a linear transformation is searched to retain within-class geometric structures in the feature space. Hence, we can achieve a nonlinear subspace that is able to estimate the essential geometric structure of the face manifold. As a matter of fact, the proposed SKLDNE is modeled to take the nonlinear data in the feature space while important features of data including \"locality\" and \"discrimination\" are simultaneously preserved. In order to clearly elucidate the performance of our SKLDNE, we have compared it with several dimensionality reduction techniques including PCA, KPCA, LDA, UDP, LPP, DNE, and LDNE on six different publicly available datasets.
## 4 Experiments
### Ablation Study
#### 4.4.1 Number of Views  
This experiment, conducted on the DTU dataset, aims to evaluate the impact of different input view counts (\(N=3,4,5,6\)) on reconstruction outcomes. As observed from the results in Table 3, an increase in the number of views allows for the extraction of more feature information, thereby enhancing reconstruction accuracy and completeness. However, indiscriminate addition of views is not a prudent choice, as it not only consumes computational resources but may also introduce unnecessary interference with the overall reconstruction quality. Determining the optimal view count requires a careful balance between performance improvement and efficiency maintenance.
## 3 Function-Oriented Networking
### Processing and Exchanging FON Data Packets between FON Switches  
Figure 6 illustrates how FON switches process and exchange the FON Packet.  
1. A network user sends a FON packet to FON Switch 1.
2. FON Switch1 sends it to proper FON Function through func_type of FON Packet.
3. After proper process, requests FON to send FON Packet.
4. Deliver packet to FON Switch 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Field** & **Description** \\ \hline
**result** & Inform success of failure to the request. 0 for success, error codes for failure \\
**tot\_len** & Inform entire response length including header \\
**error\_string** & Inform specific error type in text when occurring an error \\
**data** & notify all other information except success or failure to FON Function (filled in target field) \\ \hline \hline \end{tabular}
\end{table}
Table 4: The fields of Synchronous Response Message Header.  
Figure 5: Format of Synchronous Response Message Header.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Value of msg\_type** & **Description** \\ \hline
**MSG\_TYPE\_REG = 0** & For registering a new FON Function on FON \\
**MSG\_TYPE\_DEREG = 1** & For deregistering a new FON Function on FON \\
**MSG\_TYPE\_SENDTO = 2** & Request to Request FON for Transmitting FON Packet \\
**MSG\_TYPE\_TABLE\_ADD = 3** & Add (modify) Entry to FON’s Forward Table \\
**MSG\_TYPE\_TABLE\_DEL = 4** & Remove Entry in FON’s Forward Table \\
**MSG\_TYPE\_TABLE\_GET = 5** & Acquire All entries in FON’s Forward Table \\
**MSG\_TYPE\_HOST\_GET = 6** & Check current FON ID in FON \\ \hline \hline \end{tabular}
\end{table}
Table 3: Synchronous Message Type Values.
# Object-Based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2
## 2 Materials and Methods
### Segmentation of Time Series
#### 2.2.2 Segmentation Accuracy Assessment  
The geometry of the agricultural fields delineated through segmentation has been evaluated using five segmentation accuracy metrics [58]. For TA1, we manually digitized 200 polygons as reference objects. For TA2 we digitized 681 polygons, representing all crop parcels with center-pivot irrigation, in order to have a complete overview of the segmentation performance in terms of accuracy. The manual delineation of the reference polygons considered every possible change of geometry through the periods analyzed, for example, a split of a larger parcel into smaller parcels for TA1 or a split of a center-pivot irrigation parcel into smaller slices in TA2. A minimum percent overlap of 50% between reference polygons and segmented objects needs to be achieved in order to match two corresponding objects [59]. The segmentation accuracy measures used are area fit index (AFI) [60], over-segmentation (OS), under-segmentation (US), root mean square (D) [61], and quality rate (QR) [62] (Table 3). Except for QR, the closer the values of the computed metrics are to 0, the better is the geometric accuracy of the resulting segments i.e., delineated agricultural fields. In case of QR, the closer its value is to 1, the better is the geometric correspondence between delineated objects and the reference objects (Table 3).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measure** & **Equation** & **Ideal Value** & **Range** \\ \hline Over-segmentation & \(OS\ =\ 1-(C\cap R)\div R\) & 0 & \([0,1]\) \\ Under-segmentation & \(US\ =\ 1-(C\cap R)\div C\) & 0 & \([0,1]\) \\ Area fit index & \(AFI=(R-C)\div R\) & 0 & OS: AFI \(>\) 0; US: AFI \(<\) 0 \\ Root mean square & \(D=\sqrt{(OS^{2}+US^{2})\div 2}\) & 0 & \([0,1]\) \\ Quality rate & \(QR=1-(C\cap R)\div(C\cup R)\) & 1 & \([0,1]\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Segmentation accuracy measures, their equations, ideal values and their range (C represents the total area of evaluated objects; R denotes the total area of reference objects). Area fit index (AFI), over-segmentation (OS), under-segmentation (US), root mean square (D), quality rate (QR).
## 5 Simulation Characterization
### Simulation Context  
We used the opportunistic network simulator ONE to evaluate proposed approaches and their performances, their impact on network efficiency, and the improvement of user privacy [30]. It is a well-known simulator designed to evaluate OMSNs and delay-tolerant networks in general. The simulator integrates principal opportunistic routing schemes and movement models, making network scenarios more realistic. We selected three routing algorithms (Epidemic, Prophet, and Spray and Wait) and three movement models (random way, bus, and working day) for the simulation scenarios.
# Cutting-Edge Trajectory Optimization through Quantum Annealing
## 4 Lunar Landing and Rendezvous Applications
### Rendezvous Problem  
The Clohessy-Wiltshire equations are a set of linearized equations commonly used in orbital mechanics to describe the relative motion between two objects in close proximity during a rendezvous or docking maneuver. These equations provide an approximation of the relative dynamics in a simplified form, making it easier to analyze and plan maneuvers [38]. The Clohessy--Wiltshire equations are particularly useful when the relative distances between the two objects are small compared to the orbital distances. They are derived by linearizing the equations of motion around a reference orbit. The equations are typically used for missions where the spacecraft is performing relatively short-durationmaneuvers, such as rendezvous, docking, or formation flying. The basic form of the Clohessy-Wiltshire equations is as follows:  
\[\begin{split}\ddot{\mathbf{s}}(t)&=\begin{bmatrix}0&2n&0\\ -2n&0&0\\ 0&0&0\end{bmatrix}\dot{\mathbf{s}}(t)+\begin{bmatrix}3n^{2}&0&0\\ 0&0&0\\ 0&0&-n^{2}\end{bmatrix}\mathbf{s}(t)+\mathbf{u}(t)\\ &=H\mathbf{s}(t)+\Lambda\mathbf{s}(t)+\mathbf{u}(t),\end{split} \tag{25}\]  
where \(n=\sqrt{\mu/R}\) is the mean motion of the reference orbit, \(R\) is the radius of the target body's circular orbit, and \(\mu\) is the standard gravitational parameter. In this application, the motion is around the Earth, thus \(\mu\) = 398,600 km\({}^{3}\)/s\({}^{2}\). From Equation (22), the polynomial approximation and its derivatives can be included in the dynamical formulation (Equation (25)) to find the expression of control vector \(\mathbf{u}\):  
\[\begin{split}\mathbf{u}(t)&=\ddot{\mathbf{s}}(t,\mathbf{ \alpha}_{2})-H\mathbf{s}(t,\mathbf{\alpha}_{2})-\Lambda\mathbf{s}(t,\mathbf{\alpha}_{2} )\\ &=\tilde{M}\mathbf{\alpha}_{2}+\tilde{N}-H(\dot{M}\mathbf{\alpha}_{2}+ \tilde{N})-\Lambda(M\mathbf{\alpha}_{2}+N)\\ &=(\tilde{M}-H\dot{M}-\Lambda M)\mathbf{\alpha}_{2}+(\tilde{N}-H\tilde{N} -\Lambda N)\\ &=P\mathbf{\alpha}_{2}+R.\end{split} \tag{26}\]  
The control vector (in Equation (26)) is a linear function of variable \(B\mathbf{\alpha}_{2}\) and it has the same form as provided in Equation (10). Thus, by following the transposition procedure from Equation (14) to Equation (18), once again, the QUBO Formulation of the problem can be derived.
## 3 Results and Discussion
### EN 13779  
With regard to the EN 13779 standard, two analyses are necessary: first, to understand how long the CO\({}_{2}\) reference values for each indoor air category (IDA) are exceeded, and second, whether the prescribed ventilation rates for each category are being experienced. As mentioned previously, the visitation period did not show an excessive presence of CO\({}_{2}\) due to the low number of occupants, as the reference value for IDA 1 of 350 ppm above outdoor concentrations is very rarely surpassed. Therefore, the most critical period to examine is the mass attendance status. Figure 25 presents the two-step analysis mentioned in the previous paragraph.  
Sim_02, during the mass period, spent all the time in IDA 1, with CO\({}_{2}\) concentrations below the 350 ppm threshold, but the prescribed ventilation rate was ensured only 47% of the time.  
The Sim_2, Sim_3.1 and Sim_3.2 ventilation strategies then presented themselves as a viable solution for improving the IAQ during the mass period, although the current ventilation (Sim_02) showed acceptable results. Finally, Sim_4 was able to maintain the CO\({}_{2}\) concentration below 350 ppm for 76.8% of the time. As this simulation is the only one that showed values outside of IDA 1, further analysis is shown.  
Figure 26 shows the time spent in each IDA category. This ventilation strategy is the same as that in Sim_02 and therefore represents the predicted increase in occupancy for 2027, according to the Portuguese government, with no changes to the ventilation behaviour of the Sao Cristovao church. In this scenario, the EN 13779 ventilation rates are not met in any percentage of time, in any IDA class, for the mass attendance status.  
Figure 24: Comparison of the percentage of time respecting EN 16798-1-recommended ventilation rates for the closed attendance status.  
Figure 25: Comparison of all simulations for the percentage of time spent on IDA 1 and respecting EN 13779-recommended ventilation rates for the mass attendance status.