esa-sceva/satcom-chunk-collection huggingface dataset:
# Object Detection in Autonomous Driving Scenarios Based on an Improved Faster-RCNN
## 3 Proposed Algorithm: AD-Faster-RCNN
### Feature Fusion Module Design  
In the original Faster-RCNN algorithm, the backbone network extracts only the high-level features of the last layer and sends them to the RPN to generate the region of interest, ignoring position and detail information at the bottom layer, which results in the poor detection of small objects. Therefore, later generations proposed a top-to-bottom feature pyramid structure (FPN) combined with Faster-RCNN to transfer high-level semantic information and fuse the features of different scales.  
However, there are still shortcomings: (1) only semantic information is enhanced, and positioning information is not transmitted; (2) these top-to-bottom and bottom-to-top methods pay more attention to adjacent resolutions and nonadjacent layers. The semantic information contained will be diluted in the process of information fusion.  
In response to these issues, this paper proposes a new feature fusion method of path aggregation-balanced FPN (PAB-FPN) based on an FPN, as shown in Figure 6. First, the feature maps C2-C5 generated by ResNet-50 are merged through a top-down feature pyramid to obtain feature maps P2-P5, and P6 is obtained by two downsamplings from P5 to enhance robustness. Then, bottom-up pyramid fusion is added behind the FPN, and the strong positioning feature at the bottom layer is transferred to obtain the feature map N2-N6. Then, the five-level features are uniformly scaled to the N4 size, and the integration operation is performed. The specific operation formula is as follows:  
\[C=\frac{1}{L}\sum_{l=l_{\text{min}}}^{l_{\text{max}}}C_{l} \tag{6}\]  
After passing the non-local [26] module refinement, the obtained feature map is scaled to the original size and added to the original feature in order to enhance the original feature and obtain Z2-Z6. In this process, feature maps of each scale can obtain equal information from other feature maps, making the process of feature fusion more balanced.
# Threat Analysis and Risk Assessment of a Driver Monitoring System
## 6 Risk Analysis
### STRIDE Application to System Components
#### 6.3.5 Sensors  
Sensors capture environmental and biometric data, forming the DMS foundation. Their exposure makes them vulnerable. **Spoofing** attacks could introduce falsified data (e.g., fake images and incorrect readings), undermining accuracy and leading to erroneous decisions. **Tampering** with sensor data or physical connections poses a severe risk, potentially disrupting information flow or introducing malicious inputs. **Repudiation** risks arise when sensor actions or data origins cannot be verified, hindering traceability and accountability. **Information Disclosure** is critical for sensitive data (e.g., camera images), with privacy violations having significant legal/ethical implications. **DoS** attacks could overwhelm communication channels, disrupting functionality and causing delays.  
Mitigation strategies include robust authentication (MACs) [38] against **Spoofing**, possibly augmented by hardware-based methods or AI-driven anomaly detection looking for subtle signs of data manipulation. Tamper-resistant hardware, physical enclosures, and error detection (cyclic redundancy check (CRC)) address **Tampering**. Lightweight digital signatures provide accountability against **Repudiation**; moreover, blockchain technology presents a potential future solution for creating tamper-evident, verifiable recordsof sensor data provenance or critical readings, particularly for sensitive biometric data. Encryption of data exchanges is essential against **Information Disclosure**, supplemented by secure firmware practices and potentially privacy-preserving computation techniques where applicable. Redundant configurations and access controls provide resilience against **DoS**, enhanced by anomaly detection and adaptive filtering algorithms, potentially leveraging AI.  
Challenges and residual risks persist. Advanced adversaries might bypass **Spoofing** authentication or sophisticated AI detection models. Physical **Tampering** protection involves cost/complexity trade-offs, and CRC is only reactive. **Repudiation** measures face key management and performance overhead issues, and blockchain implementation in real-time sensor streams is challenging. Encryption (**Information Disclosure**) is not infallible against key/protocol vulnerabilities or side-channel attacks. **DoS** defenses strain resources and may not stop sophisticated attacks. Balancing security, performance, cost, and privacy remains a key challenge, demanding continuous evaluation, proactive threat-modeling, the development of robust AI security techniques, and potentially novel uses of technologies like blockchain for these critical components.
## 4 Materials for SLA
### Graphene Composites
#### 4.2.2 Three-Dimensional SLA Printing of Graphene and GO
Four-dimensional printing has potential applications in many fields due to the unique characteristics of the printed structure to undergo a transformation in shape, properties or functionality in time (4 th dimension) when it is exposed to an external stimulus (temperature, electric or magnetic field, humid atmosphere etc.) [121]. This strategy can produce 4D biofabricated constructs for regenerative medicine [122], control systems, soft robotics [123], among others. As an example, [PERSON] et al. reported the 4D printing of shape memory polymers based on tert-butyl acrylate and di(ethylene glycol) diacrylate reinforced with 0.1 wt%, 0.3 wt% and 0.5 wt% graphene nanoparticles [123]. The authors stated that the addition of graphene nanoparticles led to an increase in tensile stress, a reduction in surface roughness and a dramatic increase in the recovery time. In conclusion, the portfolio of graphene-based resins for SLA offers many perspectives to design new hybrid objects with outstanding properties. Up to date, scientists made great progress in attaining superior printability, tunable properties, controlled architectures and biological function of 3D-printed constructs (Figure 5). Future work could support the development of micro-devices for monitoring and therapy, electroactive scaffolds for neurogenesis and myogenesis, membranes in ocular regenerative medicine, among others. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Filler** & **Polymer Matrix** & **Resin Preparation** & **3D Printing** & **Post-Processing** & **Applications** & **Ref. ** \\ \hline \multirow{4}{*}{ \begin{tabular}{c} Graphene, GO, \\ graphite nanoplateels \\ \end{tabular}  } & \multirow{4}{*}{ \begin{tabular}{c} Photocurable \\ acrylic-based resin was \\ Formalsh \\ Clear FLGPCL4 \\ (Formalabs, Somenville, MA, USA) \\ \end{tabular}  } & \multirow{4}{*}{ \begin{tabular}{c} Dispersion of the nanofiler \\ (0.1 wt%) in the polymer \\ resin; ultrasonication for \\ 30 min; degonsification; \\ ultrasonic bath for 15 min \\ \end{tabular}  } & \multirow{4}{*}{\ \begin{tabular}{c} 405 nm \\ \end{tabular}  } & \multirow{4}{*}{ \begin{tabular}{c} Biomedical \\ applications \\ \end{tabular}  } & \multirow{4}{*}{[115]} \\  & & & & & \\ \cline{1-1} \cline{6-6}  & & & & & \\ \cline{1-1
# Estimation of Saprolite Thickness Needed to Remove _E. coli_ from Wastewater
## 2 Materials and Methods  
### Theory  
The size and shape of bacterial cells are important parameters that play a key role in particle retention [14]. The typical size of an individual _E. coli_ cell is about 1.1 to 2 \(\upmu\)m in width and 2 to 10 \(\upmu\)m in length [4; 14]. Due to the size and rod-shape of the bacterial cell, _E. coli_ should be filtered when wastewater that is applied to a soil passes through pores that are \(<\)10 \(\upmu\)m in diameter [15]. The diameter of pores that can hold water as a function of soil water potential can be estimated according to the capillary rise equation by:  
\[D\ (\mathrm{cm})\ =\ -0.3/h\ (\mathrm{cm}) \tag{1}\]  
where \(D\) is the diameter of the pore, and \(h\) is the soil water pressure head (-cm) [16]. For pores \(\leq\)10 \(\upmu\)m (0.001 cm) in diameter, the pressure head \(h\) is \(-\)300 cm. Our previous study [11] showed that a sandy loam saprolite column that was 60 cm long filtered out _E. coli_ that was in a simulated wastewater, which had an _E. coli_ concentration of 1 \(\times\) 10\({}^{5}\) CFU/100 mL, while saprolite-filled columns of 30 and 45 cm allowed some _E. coli_ to pass through. We hypothesize that 60 cm of a sandy loam saprolite has a sufficient volume of pores \(<\)10 \(\upmu\)m in diameter to remove _E. coli_. Water retention measurements in our previous study showed that the sandy loam saprolite's water content at a soil water pressure head of \(-\)300 cm was 0.28 cm\({}^{3}\)/cm\({}^{3}\), which is equivalent to the volume of the pores having diameters \(<\)10 \(\upmu\)m in the material. We used this information to develop a general equation for predicting the minimum thickness of saprolite that needs to be used to filter _E. coli_ out of wastewater. The total volume of pores \(\leq\)10 \(\upmu\)m in diameter (\(PV_{\mathrm{10\ \upmu m}}\)) per unit cross-sectional area of a column of saprolite of thickness \(T\) can be estimated from:  
\[PV_{\mathrm{10\ \upmu m}}\ =\ \theta_{-300}\ \times T \tag{2}\]  
where \(\theta_{-300}\) is the volumetric water content of saprolite at \(-\)300 cm of pressure head as determined from its respective water retention curve. For the sandy loam saprolite material studied previously, where \(\theta_{-300}\) was 0.28 cm\({}^{3}\)/cm\({}^{3}\) and \(T\) was 60 cm, \(PV_{\mathrm{10\ \upmu m}}\) was 17 cm\({}^{3}\)/cm\({}^{2}\), which we suggest is the minimum volume of pores with diameters of \(<\)10 \(\upmu\)m needed to filter _E. coli_ from saprolite. Using this value and rearranging Equation (2), we obtain a general equation for estimating the minimum thickness (\(T\)) of saprolite needed to remove _E. coli_ from wastewater:  
\[T\ (\mathrm{cm}) = 17\ \left(\mathrm{cm}^{3}/\mathrm{cm}^{2}\right)/\theta_{-300}\ \left(\mathrm{cm}^{3}/\mathrm{cm}^{2}\right) \tag{3}\]  
We hypothesize that Equation (3) can be used to estimate the minimum thickness of saprolite material that can be used for on-site wastewater disposal if the water content at \(-300\) cm of pressure head (i.e., \(\theta_{-300}\)) is known.
# Learning from the Past: Parametric Analysis of Cob Walls
## 3 Results and Discussions
### FEM Out-of-Plane Analysis  
The DOE using a CCD rotatability design with four factors (length, height, thickness, and acceleration) generated 49 design points. The order in which the design points were generated by the CCD was random to avoid the effect of any nuisance variable in the output parameters (randomization is of paramount importance in the design of physical experiments, but it does not affect the numerical parametric analysis performed in this work).  
The minimum value computed for \(F_{ys}\) was equal to 3.0. This value was obtained for a wall with 4.5 m length, 2.74 m height, 0.53 m thickness, and subjected to a lateral acceleration of 0.86 m/s\({}^{2}\). The full quadratic model obtained through the DOE was refined by removing the non-significant terms. By taking into account only those statistically significant terms previously discussed, the expression shown in Equation (8) was obtained. Equation (8) can be used to estimate the value of \(F_{ys}\) for every combination of wall height (\(H_{wall}\) in m), thickness (\(T_{wall}\) in m) and lateral acceleration (\(a\) in m/s\({}^{2}\)) within the range of values explored by the analysis presented in this paper. In this equation, the length of the wall is not included, as its effect on the out-of-plane response is non-significant, and this confirms the comment made at the beginning of the section regarding the fact that the length of the wall was expected not to influence the out-of-plane response of the wall.  
\[F_{ys}=19.51-9.55H_{wall}+11.76T_{wall}-7.41a+1.33H_{wall}^{2}-9.9T_{wall}^{ 2}+6.34T_{wall}a, \tag{8}\]  
If Equation (8) is evaluated for a wall height of 3.05 m, a thickness of 0.40 m, and an acceleration of 0.981 m/s\({}^{2}\), then, \(F_{ys}=1.1\). This indicates that even under the most adverse combination of values for the parameters explored, and based on the assumptions adopted, the material would not reach its yield point. Equation (8) was also used to create the response surfaces shown in Figure 7. Each one of the response surfaces shows information regarding the effect of two input parameters, while the third one is kept constant at its most critical value, on the response, \(F_{ys}\). These response surfaces reflect the quadratic effect that the input parameters have on the response.  
Figure 7: Response surfaces for \(F_{ys}\) for the out-of-plane FEM model: (**a**) acceleration fixed at 0.981 g; (**b**) thickness fixed at 0.4 m; and (**c**) height fixed at 3.05 m.
## 4 Real-Time Implementation of an Auralization System
### Rotation of the Acoustic Field  
Although sound field rotation can be generated by interpolating HRTFs, this methodology has the limitation that it is restricted to binaural reproduction only. The use of a spherical harmonic representation to rotate the acoustic field is suitable for several audio reproduction techniques, which increases the flexibility of the proposed approach. In general, the implementation of the rotation in the auralization system using a spherical harmonic transformation can be divided into three main steps: the encoding, rotation and decoding stages, respectively. The general concept can be defined as the encoding of each of the directional impulse responses into a finite number of spherical harmonic coefficients, the application of the rotation and, finally, the return to the plane wave domain again through a decoding process. Nevertheless, to reduce the computational cost required by the generation of the rotation in the acoustic field, the encoding stage is computed by using as an input the difference between the angles of the directional impulse responses \(\tilde{\mathbf{y}}_{l}(\theta_{l},\phi_{l})\) and the rotation angle, rather than by multiplying the spherical harmonic coefficients by a rotation operator. Figure 17 shows the signal processing chain for the rotation operator.  
One relevant consideration on the use of this approach is the number of audio files to be processed in real time (\(L\times N\)). Due to the very large amount of operations, the number of spherical harmonic coefficients was limited to the fifth order (36 coefficients) for the encoding and decoding stages. The encoding is performed using real-valued spherical harmonics [31], which are calculated from their complex pairs. As a consequence of the reduced order used for rotation, the area of accurate reconstruction is reduced following the relation \(N=kr\). In order to preserve the translation area, the translation operator is applied before the rotation. In this case, the area reduction given by the lower spherical harmonic order does not reduce the region where the translation operator accurately reconstructs the target field. This means that the outcome of the rotation operation  
Figure 16: Region of accurate translation given by the PWE.  
Figure 17: Rotation scheme.  
has only to be accurate in the radius corresponding to the listener's head (\(r_{l}\)). The truncation of the spherical harmonic series up to fifth order is sufficient to cover the listener's radius for the frequency range considered.
## 4 Measurement Error Caused by the Stability of the LD Wavelength  
The unstable LD wavelength will affect the measurement accuracy of the MLDI. A passive wavelength stabilization method based on the principle of automatic temperature control and the principle of the laser beam drift compensation was proposed to stabilize the LD wavelength in our previous research [6]. However, the stability of the laser diode wavelength can only be controlled at the level of \(10^{-6}\) for a short period of time. In order to realize the high stability for a long period of time, an active wavelength stabilization method, which includes two consecutive steps, is proposed, as shown in Figure 6.  
Figure 4: Flowchart of the elimination of the nonlinear errors.  
Figure 5: Corrected signals: (**a**) sinusoidal and cosine signals and (**b**) Lissajous figure.  
Figure 6: Flowchart of the active control of the laser wavelength stability.  
Step 1: Temperature control of the LD  
The temperature control of the LD is based on the principle of automatic temperature control. A closed-loop control system is adopted to improve the control precision of the LD surface temperature, as shown in Figure 6. The surface temperature of the LD is detected by a thermistor, which is installed on the LD. A differential signal is generated by differing the surface temperature with the desired temperature and input to the PID controller 1. Two thermoelectric coolers (TECs) will be employed to heat or cool according to the outputs of the PID controller, which are the drive voltages of the TECs. The LD's surface temperature can then be effectively controlled within a commanded range, in which the full width at half maximum (FWHM) is the smallest. The FWHM is an important parameter to characterize the stability of the LD wavelength.  
Step 2: Wavelength control of the LD  
However, the capability of the wavelength stabilization by using the temperature control is limited. Therefore, a wavelength control method according to the real-time measured wavelength by the laser wavelength meter (LWM) is further proposed, as shown in Figure 6.  
A closed-loop control system is adopted to improve the precision of the wavelength control. After comparing the actual wavelength variation with the desired wavelength variation, a differential signal is generated and input into the Backpropagation Neural Networks-based PID (BPNN-PID) controller, which is composed of an input layer with three neurons, a hidden layer with five neurons and an output layer with three neurons. The PID controller parameters \(K_{i}\), \(K_{p}\) and \(K_{d}\), which are the three neurons of the output layer, are obtained by training the BPNN according to a group of measured wavelength variations. The obtained PID controller parameters were used to control the drive voltage of the TECs according to the instantaneously measured wavelength variations. Therefore, the LD's surface temperature will be precision controlled, from which the LD wavelength can be effectively controlled within a commanded range.
## 4 Industry Perception Study
### Perceived Challenges to DePaaS
#### 4.4.2 Perceived Challenges to Use DePaaS  
The sample pool was asked to list potential challenges to use DePaaS as per the 'De-PaaS process' explained in Section 3 of this article.  
Responses to DePaaS usage related questions highlighted multiple concerns. Every respondent (100%) questioned security of uploaded software code, defect metadata and other details.  
Responses indicated that choosing the appropriate project in CPDP context would be difficult (97%). This could be due to concerns about the model selector algorithm (87%). Similar concern was expressed about finding the data relevant for the chosen project (88%).  
Respondents believed that using DePaaS may not easy as there are too many metrics to choose from (53%) which makes feature selection more complex (88%). Model selection and tuning need more skills (56%) which might need a deeper knowledge of programming and software metrics (53%). Some responses indicated that the DePaaS concept is too complex (25%) and difficult to teach (22%).  
Some responses indicated the possibility of false alarms (66%) and non-repeatable results (72%). These challenges pertain to the SDP model chosen and not to the DePaaS framework.
# Field Data Forecasting Using LSTM and Bi-LSTM Approaches
## 2 Materials and Methods  
In this section, we present the methodology for our new approach to predicting future soil moisture, which is based on deep learning LSTM models and uses a low-cost setup.  
The LSTM was invented in 1997 by [PERSON] and [PERSON], however, it has gained popularity as an RNN architecture in recent years for a variety of applications [24]. The LSTM deviated from traditional neuron-based neural network architectures by introducing the concept of a memory cell. Based on its inputs, the memory cell can remember an important value rather than just the most recently computed value. Recent CNN and LSTM applications have resulted in image and video captioning systems that use natural language to caption an image or video. The CNN processes images or videos, and the LSTM is trained to translate the output of CNN to natural language [24; 25].  
The memory cell of LSTM has three gates (input, forgot, and output gate). They are used to control the flow from the input to the output of the cell. The input gate will control the new information when it can enter the memory. The forgot gate will check the existance of information in the memory and determines whether or not the cell can remember new data. Finally, the information in the cell is determined to be used in the output cell by the output gate. Each cell contains weights to control each gate. These weights are optimized by a training algorithm based on an error resulting of network output [25; 26]. In contrast, the LSTM approach is not used for crop irrigation systems or soil moisture prediction using real-time datasets from smart sensors.  
Data are the most valuable asset in any machine learning approach. We collected a large amount of data from a testbed located in our university's Innovative Village (see de-tails in Section 3.1). The data were then thoroughly preprocessed before we started the LSTM model design lifecycle to test and validate it on our data. The plan shown in Figure 1 highlights the steps in our methodology. It depicts the five steps in creating our pro-posed soil moisture forecasting model.  
1. _Step 1--Data Collection:_ the relevant data are measured using sensors and collected on a cloud database;
2. _Step 2--Data Preprocessing:_ the missing data and irrelevant data will be processed in this step. A new clean dataset is the most important outcome;
3. _Step 3--Modeling and Pattern Selection:_ both LSTM and Bi-LSTM forecasting models are created. Moreover, a set of hyperparameters is tuned to obtain the best performance from the model. the hyperparameters in our case are the parameters that affect the performance of the proposed model comprising time step, batch size, epoch, learning rate, and split ratio;
4. _Step 4--Evaluation and Interpretation:_ the proposed model will be trained, tested, and validated based on the collected data.