esa-sceva/satcom-chunk-collection huggingface dataset:
# Computer Vision Algorithms on a Raspberry Pi 4 for Automated Depalletizing
## 3 Methodology
### Experimental Setup  
The experimental setup for this research involved the utilization of a low computing power _edge_ solution, i.e., one that does not rely on remote resources (a centralized server, cloud computing) to provide computing power. The device was equipped with a camera and operated in a controlled environment. The Raspberry PI 4 acted as the primary computing platform, responsible for running the computer vision algorithms and processing the captured images. The camera was mounted in a fixed position, vertically placed above the pallet and roughly centered, ensuring consistent image capture and analysis.  
The Raspberry PI 4 serves also as the central controller of our setup, orchestrating the various components. Images are captured at a resolution of 1280 \(\times\) 720, RGB 24 bpp, using the PI camera model HBV-1708 with autofocus and a 2592 \(\times\) 1944 maximum resolution (see Figure 2). The software was developed in Python 3.8 using OpenCV and other libraries under the Raspberry Pi operating system.  
Palleit items are detected on the Raspberry Pi 4, and non-maximum suppression [36] is applied to remove multiple overlapping observations. Then, the position of their center is estimated and in the final system can be transmitted to the robot to remove the identified item from the stack and place it onto the target position (which can be a conveyor belt or another pallet, depending on the task). Ideally, at each new iteration, the algorithm must guarantee that the topmost layer is emptied before moving to the next one. This can be achieved using a depth sensor (for instance, a rgb+d camera or a laser rangefinder). Since the setup did not involve a depth sensor, and since this function is implemented almost trivially by always targeting the object with the maximum z-order, the present work does not cover this aspect.  
The experiments cover several feature extraction methods. The foreseen setup of the final equipment is illustrated in Figure 3.  
Figure 3: Reference setup.  
Figure 2: Raspberry PI 4 and camera.  
In the rest of this paper, we will evaluate the performance of the four selected algorithms (pattern matching, Haar cascade, SIFT and ORB), comparing them based on their accuracy, speed, robustness to image variability, computer efficiency, detection sensitivity, and resource consumption. The methodology is outlined in Figure 4.  
* Collecting the image data.
* Preprocessing and generating feature vectors using four feature descriptors, pattern matching, Haar cascade, SIFT, and ORB, individually.
* Comparing outcome algorithms.
* Selecting the most suitable algorithm for a specific depalletizing system.
## 2 Results and Discussion  
### Identification of ARR VOCs In Vivo in Rats  
A total of 153 VOCs of ARR were detected in this study. Based on the structure, the 153 VOCs could be divided into three main categories: 63 terpenoids and their substitutes (including monoterenes and sesquiterpenenes), 7 alkanes and alkenes (including straight chains, branched chains, and ring structures), and 83 aromatic compounds. Among the 153 VOCs, 101 could be identified as original constituents of ARR (98 in the powder-treated group and 43 in the decoction-treated group), and 15 were identified as the metabolites of ARR constituents (12 in the powder-treated group and 6 in the decoction-treated group). The metabolic reactions were mainly phase I reactions (oxidation and reduction), and there were only two cases of phase II metabolic reactions (methylation and esterification). The remaining unclassified 37 compounds were identified in the ARR-treated groups (24 in the powder-treated group and 25 in the decoction-treated group), but could not be found in either the control group or the ARR sample itself, suggesting that they might be metabolites of constituents or endogenous substances generated after the administration of ARR, and more evidence was required to determine their source. Of the 153 VOCs identified, 131 were first reported in the rats after oral administration of ARR (79 original constituents, 15 metabolites, and 37 unclassified compounds, or 79/15/37, as designated hereafter). A list of all compounds detected in this study and chromatograms of all samples are shown in Table 1 and Figures 1 and 2. The structures of all identified compounds are shown in Supplementary Materials Figures S1 and S2. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**No.
# A Cyber-Physical Approach for Residential Energy Management: Current State and Future Directions
## 2 Related Work  
Home Energy Management Systems (HEMS) have centered the attention of the research community over the last decade. Since the emergence of Smart Grids, HEMS have been defined to play a key role in centralizing the management of services. The main idea behind this concept is to provide customers with complete functionalities for internal information exchange. The authors of [3] pointed out, among other challenges, the need of having a robust and large-bandwidth communication infrastructure that can cope with the enormous volume of data, which indeed has been hardly discussed in recent years [37; 38; 39; 40; 41; 42].  
In that sense, the authors of [38] identified interference and wall penetration losses as the main challenges to be handled in smart homes. They built a simulator which allowed them to prove that cognitive radio communication technologies can help overcome the challenges by providing more flexibility in terms of unused spectrum. In addition, the authors highlighted the need for a 5G network which connects the smart homes and joins these into a smart city infrastructure. On the other hand, in [41], the authors discussed on how conventional wireless communication technologies, such as WiFi or Zigbee, are insufficient for communication range, energy consumption, and cost. As a solution, the authors advise low power wide area networks (LPWAN) which can operate at low data rates while covering kilometer ranges. Among these technologies, they remarked long range (LoRaWAN), which is an open standard, with built-in security, GPS-free geolocation, ability to have long range communication, low energy consumption, and options to have private deployments. However, its deployment in real-time application is limited due to its low data rate and duty cycle constraints.  
The vast majority of previous works concerning HEMS is slightly related to Data Analytics. The usage of power data has provided HEMS with different functionalities suchas load monitoring, load forecasting, comfort-level analysis, and scheduling the use of appliances in order to increase energy efficiency.
###### Abstract
MSCAE is obtained by fusing multiple convolutional autoencoders with different convolutional kernel sizes. Convolutional autoencoders with small convolutional kernels can perform feature extraction for locally degraded features. In contrast, convolutional autoencoders with large convolutional kernels can perform feature extraction considering the degenerate trend of the whole sequence. Combining both of the advantages can solve the problem of underutilization of temporal information. Meanwhile, the construction of labels using quadratic functions and the training of MSCAE using composite loss functions can realize end-to-end HI extraction and automate HI extraction. After obtaining the HI of MSCAE, the LSTM is used for bearing RUL prediction. Unlike other methods, HI obtained by the proposed method does not require the additional step of failure threshold determination, and the failure threshold is directly set to 0. The main contributions of this paper are listed below. 1. A novel autoencoder-based HI construction method, called MSCAE, is proposed. Compared with the traditional HI construction method, it can effectively use local and global temporal information to obtain a more robust HI and, at the same time, realize the automatic HI extraction. 2. The HI obtained by MSCAE is compared with single-scale convolutional autoencoders with different convolutional kernel sizes, and the superiority of the proposed method is verified by a comprehensive evaluation index consisting of monotonicity, correlation and robustness. 3. The HI obtained by MSCAE does not require artificial determination of the failure threshold, which is directly set to 0 and can be directly used for RUL prediction. The remaining parts of this paper are organized as follows. Section 2 introduces the relevant theoretical knowledge covered in this paper. Section 3 presents the proposed HI construction method. Section 4 conducts relevant experiments and comparisons to verify the validity of the proposed method. Section 5 concludes this paper.
## 4 Results
### Kinematics and Algorithm Validation  
Important insights can also be drawn from outcomes related to kinematics and the functioning of the algorithm. As previously mentioned, the goal of the control algorithm presented in [17] is to generate commands for the robotic joints (\(\dot{q}_{r}\)) to reach a desired target, \(\bar{x}_{e}\), known only to the human.  
In this section, we briefly describe the validation of the algorithm. Given the ultimate focus on SCI subjects, the tetralegic participant, SCI 1, was selected as the primary subject for this analysis.  
For **Test 1**, since the position of \(\bar{x}_{e}\) is predefined in the virtual environment for the three targets (denoted as \(\bar{x}_{e1}\), \(\bar{x}_{e2}\), and \(\bar{x}_{e3}\)), a straightforward comparison with the actual positions can be made.  
Figure 11a shows the position achieved by the robot's base in the \(x\) and \(y\) coordinates before training (blue) and after training (magenta), compared to the desired positions for the first two targets, \(\bar{x}_{e1}\) and \(\bar{x}_{e2}\). Figure 11b illustrates the comparison between the actual and desired end-effector positions for the third target, \(\bar{x}_{e3}\), along all three coordinates, both pre- and post-training. As observed, the participant successfully reached all targets even during the pre-training phase. However, the time required to achieve these targets was significantly reduced in the post-training phase.  
To evaluate the effectiveness of the training sessions, we analyze the residual motions performed by the subject, SCI 1. Figure 12 illustrates the pose of the user's frame, \(x_{c}\), positioned on the right upper arm, in terms of both position and orientation, before and after the training sessions for the entire **Test 1**. As shown, the movements performed post-training are significantly smoother and less pronounced compared to those observed during the pre-training session. By specifically focusing on the translati  
Figure 11: Robot coordinates before (blue) and after (magenta) the training sessions for the SCI subject during **Test 1** vs. the actual targets’ positions.  
that, after training, the movement remains smooth, slight, and consistent throughout the task, without sudden changes that could indicate a muscle spasm or an involuntary motion by the user at any given moment. However, as highlighted in the discussion section, further improvements are needed to enhance the control's ability to distinguish between voluntary and involuntary movements. Regarding the orientation, more pronounced fluctuations can be observed after training. This is due to variations in the orientation component of the user's frame, \(x_{c}\), particularly when the last of the three targets is reached. Specifically, at this stage, the user must raise their upper arm, as shown in the third sub-phase of the training sessions in Figure 5c. This motion results in fluctuations in the orientation range.
# Optimization-Based Input-Shaping Swing Control of Overhead Cranes
## 3 Design of Input Shaper
### Design of Shaper Based on Analytical Method  
Let a typical second-order system \(G(s)=\frac{\omega_{s}^{2}}{\beta^{2}+2\zeta\omega_{n}s+\omega_{n}^{2}}\) as an example to illustrate the design process of the shaper (\(\omega_{n}\) and \(\xi\) are the natural frequency and the damping ratio, respectively). The mathematical description of a shaper can be expressed as (4):  
\[H(t)=\sum_{j=1}^{D}A_{j}\delta(t-t_{j}) \tag{4}\]  
where \(D\), \(A_{j}\), and \(t_{j}\) represent the number of pulses, the amplitude, and the time of the \(j_{th}\) pulse. The following constraints need to be met to design a shaper [21].  
1. Constraint of residual vibration In order to make the system response stable, the residual vibration of a system should converge to zero in a limited time.
2. Constraint of pulse amplitude In order to ensure that the system displacement and other parameters do not change, the input signal of the system is required to have the same steady state value before and after shaping, that is, the sum of the amplitudes is equal to 1, as shown in (5): \[\sum_{j=1}^{D}A_{j}=1\] (5)  
Figure 2: Principle of shaping.  
Constraint of optimal pulse time  
In order to minimize the delay of the system response, the first pulse time is usually zero, as shown in (6):  
\[t_{1}=0 \tag{6}\]  
Through the above constraints, the pulse amplitudes and times of different shapers can be obtained by selecting different numbers of pulses, such as ZV and ZVD shapers, and they can be expressed as (7) and (8) [22,23]:  
\[\left[\begin{array}{c}A_{i}\\ t_{i}\end{array}\right]=\left[\begin{array}{cc}\frac{1}{1+K}&\frac{K}{1+K}\\ 0&\frac{\gamma}{2}\end{array}\right] \tag{7}\]  
\[\left[\begin{array}{c}A_{i}\\ t_{i}\end{array}\right]=\left[\begin{array}{cc}\frac{1}{K^{2}+2K+1}&\frac{2K }{K^{2}+2K+1}&\frac{K^{2}}{K^{2}+2K+1}\\ 0&\frac{T}{2}&T\end{array}\right] \tag{8}\]  
where \(K=\exp\left(\frac{-2\pi}{\sqrt{1-\xi^{2}}}\right)\), and \(T=\frac{2\pi}{\omega_{n}\sqrt{1-\xi^{2}}}\). For crane system (2), the swing angle dynamics can be transformed into a typical second-order system, as shown in (9):  
\[\frac{\theta(s)}{a(s)}=\frac{-\frac{1}{l}}{s^{2}+\frac{b}{m}s+\frac{\xi}{l}} \tag{9}\]  
where \(a=\bar{x}\) is the control input. In the meantime, it can be derived from (9), where \(\omega_{n}=\sqrt{\frac{\xi}{l}}\), and \(\xi=\frac{b}{2m}\sqrt{\frac{l}{\xi}}\). So far, the amplitude and time of a shaper can be calculated using (7) or (8).
## 4 Materials and Methods
### Phytotoxic Studies  
#### 4.4.1 Non-Contact Germination Test with EOs  
Seeds (15) of the target species _L. multiform_ and _S. alha_ were sown in 9 cm diameter Petri dishes lined with filter paper (Whatman No. 1) wetted with 4 mL of sterilized water. The EOs of _J. communis_ or _L. decidua_ were pipetted (2, 20, or 50 \(\upmu\)L) into a small handmade aluminum container placed in the center of each Petri dish to avoid direct contact with seeds. To evaluate the phytotoxic activity of the EOs using a different substrate, the seeds (15) were also sown in 9 cm diameter Petri dishes filled with 25 g of non-fertil soil (Vigorplant(r) SER CA 98 V7, Fombio (Lo), Italy) wetted with 15 mL of sterilized water. Sterile 6 mm diameter disks (1 or 3) impregnated with different amounts (2, 20, or 50 \(\upmu\)L) of _J. nana_ or _L. decidua_ EO were placed at the same depth as the seeds and covered with soil. In their respective controls, the EOs were absent and replaced by distilled water (2, 20 or 50 \(\upmu\)L). Tests were carried out under a biological hood with vertical laminar flow. Subsequently, the suitably sealed (double layer of Parafilm) and initialed Petri dishes were incubated for 16 h light at 23 \({}^{\circ}\)C and 8 h darkness at 18 \({}^{\circ}\)C in a climatic chamber for 7 days. The experimental design included 3 quantities of each EO (treated samples) or distilled water (control samples) \(\times\) 2 target species \(\times\) 3 replicates \(\times\) 2 runs.
# Optical Channel Selection Avoiding DIPP in DSB-RFoF Fronthaul Interface
## 2 System, Methods, and Network Components
### Radio-Frequency-over-Fiber in DSB Format
After substituting (9) into (7), we obtain the dependence on the electric field strength after the signal passes through the dispersive optical path:  
\[ \begin{array}{c}E_{IM}(t,f_{RF},\lambda_{0})=\exp\left(j2\pi\frac{c}{ \lambda_{0}}t\right)+\frac{m_{IM}}{2}\bigg{\{}\exp\left[j2\pi\left(\frac{c}{ \lambda_{0}}+f_{RF}\right)t-\pi D(\lambda_{0})L\frac{\lambda_{0}^{2}}{c}f_{ RF}^{2}\right]+\\ \qquad\qquad+\exp\left[j2\pi\left(\frac{c}{\lambda_{0}}-f_{RF}\right)t-\pi D (\lambda_{0})L\frac{\lambda_{0}^{2}}{c}f_{RF}^{2}\right]\bigg{\}}.\end{array}  \tag{10}\]  
The electrical signal behind the photodetector is proportional to the intensity of the incoming light, so the square law detection applies [35; 26; 36]:  
\[i_{IM}(t)\propto|E_{IM}(t,f_{RF},\lambda_{0})|^{2}. \tag{11}\]  
When we substitute (10) into (11), we obtain a relationship containing a constant component,\(f_{RF}\) dependent components and higher-order components. When we assume that the modulation index \(m_{IM}\) is small, the higher-order terms become negligibly small. If we also omit the constant component which does not carry information, the signal takes the following approximate form:  
\[i_{IM}(t,f_{RF},\lambda_{0})\approx 2m_{IM}\cos\left(\pi D(\lambda_{0})L\frac{ \lambda_{0}^{2}}{c}f_{RF}^{2}\right)\cos(2\pi f_{RF}t) \tag{12}\]Consequently, the normalized power response, downstream of the photodetector, on the excitation of the fiber-optic path by the IM signal, is determined by the following relationship [46]:  
\[P_{IM}(f_{RF},\lambda_{0})\approx\cos^{2}\left(\pi D(\lambda_{0})L\frac{\lambda_{ 0}^{2}}{c}f_{RF}^{2}\right). \tag{13}\]  
Based on formula (13), it can be seen that the power of the received RF radio signal periodically declines to zero. We define this phenomenon as a dispersion induced power penalty (DIPP), and it is the main element of further considerations. After that, we can go to the logarithmic scale to determine the decay ranges of the received RF signal and to combine the DIPP with the absorption loss of the optical path. All components of the optical signal around the \(\lambda_{0}\) carrier undergo the same attenuation due to absorption. On this basis, we can determine the degree of fading of the sidebands signal as a result of the DIPP carrier-to-interference ratio (DIPP-CIR) interaction, in relation to the optical carrier as the source of the constant component:  
\[ \begin{split}\text{DIPP}-\text{CIR}_{IM}&=10\log \frac{P_{C}}{P_{SBs}}=10\log\cos^{2}\left(\pi D(\lambda_{0})L\frac{\lambda_{0}^ {2}}{c}f_{RF}^{2}\right)\\ &=20\log\cos\left(\pi D(\lambda_{0})L\frac{\lambda_{0}^{2}}{c}f_ {RF}^{2}\right).\end{split}  \tag{14}\]  
Given that the optical signal is also subject to absorption loss, the power of the received RF signal downstream of the photodetector is approximately as follows:  
\[P_{out}(f_{RF},\lambda_{0})\approx P_{in}-\text{ DIPP}-\text{CIR}_{IM}(f_{RF},\lambda_{0})-A_{pp}(\lambda_{0}), \tag{15}\]  
where \(A_{pp}(\lambda_{0})\) is the optical path loss.
# Demonstrating the Potential of a Low-Cost Soil Moisture Sensor Network
###### Abstract
For example, Wi-Fi can support high data rate but can only support short-range data transmission. ZigBee and Bluetooth support a short range with low data rate designed for applications requiring point-to-point short-range data transmission. Cellular networks (2G to 5G) support long-range and high data rate transmission, but often struggle due to high power consumption and data transmission costs due to limited bandwidth and high-cost infrastructure. While each of these protocols are suitable for a certain set of applications, there is no one solution fit for all wireless communication protocols. For smart farming and environmental monitoring, a wide coverage over a large geographical area is often required and usually sensors must be battery-powered due to their remote location. LPWAN is a wireless technology with characteristics such as large geographical coverage, low bandwidth, and data rate suitable for sensor observations captured at long intervals and low power consumption, which make it suitable for environmental monitoring applications. LoRaWAN technology is currently one of the most promising protocols for wireless communication. This network is relatively easy to implement, has a ready-to-use security layer and a guarantee of wide coverage with minimal maintenance and low energy consumption, which is ideal for large-scale use [19]. LoRaWAN-enabled soil moisture sensors are commercially available, such as DecentLab (Dubendorf, Switzerland), AMIT Wireless (Tainan, Taiwan), Agro Sense (Budapest, Hungary), ICT International (Armidale, Australia), TekBox (Woodhurst, UK), SensoTerra (Utrecht, The Netherlands) and Seed Studio (Shenzhen, China). Among these suppliers, Seed Studio though their Sense Cap range provide a full solution, including gateway and data management at the lowest cost on the market (sensor node--EUR 185, gateway--EUR 385). The overall sensor network set-up is simple, fast and does not require specialised training or technical knowledge. The objectives of this study are to (1) determine the analytical performance In measuring dielectric permittivity in liquids of known \(\varepsilon\), (2) evaluate the performance in measuring different \(\theta\) in soil samples and through comparison with TDR instrumentation (3) study the data communication capabilities of the sensor network at various ranges and node sampling frequency and (4) provide recommendations for sensor deployment and how to limit the sources of error associated with drift and field installation.
# HEAD Access Control Metamodel: Distinct Design, Advanced Features, and New Opportunities
## 3 Access Control Models
### The Background  
To ensure security and privacy, different AC models are implemented in the literature to enforce AC policy and prevent any illegal access to resources. Figure 1 summarizes the features of common AC models, DAC, MAC, RBAC, and ABAC, and their major components which are used to formulate and enforce organizational AC policies [6; 15].