esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 New Technologies in Greenhouses
### Computational Modeling and Simulation
#### 5.1.1 Benefits of Computational Modeling and Simulation in Greenhouses  
Computer modeling and simulation offer several advantages for the operation and management of greenhouses. These advanced methods allow for a more detailed and accurate understanding of internal conditions, enabling adjustments and optimizations that would be difficult to achieve with traditional methods alone. The main benefits are as follows:  
* Optimizing Energy Efficiency: Optimizes energy consumption in greenhouses, resulting in a more sustainable and economical operation. It predicts the thermal behavior of the greenhouse and allows adjustments that reduce energy use [(43)].
* Improved Microclimatic Control: Allows you to accurately predict and control microclimatic conditions inside the greenhouse, including temperature, humidity and \(CO_{2}\) concentration, which improves plant growth and health [(258)].
* Development of Accurate Models: Computational modeling allows for the creation of detailed models that consider several factors, such as solar radiation, airflow and evapotranspiration, providing an accurate prediction of the greenhouse's internal environment [(194)].
* Greenhouse Planning and Design: Computer simulation helps in the planning and design of greenhouses, allowing the evaluation of different configurations and materials to optimize energy performance and growing conditions [(45)].
# Conception of a System-on-Chip (SoC) Platform to Enable EMG-Guided Robotic Neurorehabilitation
## 1 Introduction
This approach offers a robust and flexible solution for improving the control of therapeutic exoskeletons, particularly in applications that require adaptive and real-time responses. The proposed system is intended to facilitate iterative development, improve signal reliability, and support diverse experimental setups, making it a valuable contribution to the field of rehabilitation robotics. To address the substantial computational demands associated with processing eight differential channels at a sampling rate of 8 kSPS, this work proposes the use of System-on-Chip (SoC) devices based on FPGAs. These platforms allow for the parallel processing of channels and the execution of necessary signal conditioning procedures, while also facilitating the integration of ROS2 into the embedded microprocessor within the SoC architecture. For implementation, a development board featuring the Zynq-7000 platform is employed, leveraging its tightly coupled ARM processor and FPGA fabric to balance real-time processing requirements with system flexibility.
## 2 System Model
### LEO Satellite Channel Model  
Without a loss of generality, the LEO satellite channel model takes into account the large-scale fading with path loss exponent \(\alpha\). Further, the small-scale fading follows the Rician fading. The probability distribution function (PDF) of channel power gain can be given by the following:  
\[f_{|h_{0}|^{2}}=ae^{-\beta_{X}}{{}_{1}F_{1}(m_{s};1;\delta x)} \tag{1}\]  
where \(a=\frac{1}{2b}\left(\frac{2m_{s}}{2m_{s}+\Omega_{s}}\right)^{m_{s}}\), \(\beta=\frac{1}{2b}\), \(\delta=\frac{\Omega_{s}}{2b(2 bm_{s}+\Omega_{s})}\), \(\Omega_{s}\) is the average power of the line-of-sight path, \(2b\) is the average power of the scattered path, \(m_{s}\) is the fading severity parameter, and \({}_{1}F_{1}\) is the confluent hypergeometric function.  
It is noteworthy that the interfering terminals located at the beam edge have smaller elevation angles caused by the wide coverage of the LEO satellite beam. The smaller elevation angles result in a low K factor, which will change the Rician distribution into a Rayleigh distribution. Therefore, we consider that the Rayleigh fading model is applicable for the interfering terminal with a low-probability line-of-sight (LOS) channel at the edge of the LEO satellite beam.  
We have compared our channel model with the series of models presented in the article [16]. In the article, they have collected 13 different models for NGSO satellites where each one of them highlights a predominant channel feature. Among them, Model 1 conforms to our channel modeling. Model 1 divides the channel model into two states: the good state means that terminals correspond to unshaded areas with high received signal power or the LOS component which is represented by Rician statistical distributions fading; the Bad state means that terminals correspond to areas with low received signal power also called non-LOS (NLOS) which is represented by Rayleigh or log-normal distribution fading. In our paper, we also divide our channel model into two situations: For NB-IoT terminals located at the center of the satellite beam, they can have stronger LOS components. Due to the presence of the direct path, the small-scale fading of the channel model for these terminals is more in line with the Rice distribution. NB-IoT terminals located at the beam edge have smaller elevation angles caused by the wide coverage of the LEO satellite beam. The Rayleigh fading model is applicable when the LOS portion of signals received at the terminal's place is smaller than that of NLOS components.  
Figure 1: System architecture of LEO satellite-based NB-IoT networks.
## 4 Results
### Accuracy Assessment of the ESTARFM Predictions
During the onset of the vegetative season (DOY 152), the differences increase, especially for the densely vegetated areas of the image (regions in dark red on the Landsat false color composite). For example, the rivers characterized by gallery forests display higher values of absolute difference, which can partially be attributed to temporarily inundated areas along the course of the streams. The agricultural areas and regions with less dense vegetation cover (bright colors on the Landsat false color composite) maintain low absolute differences for the vegetative season (DOY  
Figure 8: Accuracy and error measures of the ESTARFM prediction: coefficient of determination (R\({}^{2}\)), mean absolute error (MAE) and root mean square error (RMSE) between predicted and observed NDVI values for the four tiles (paths 194, 195 and rows 52, 53) and dates (DOY) 2014. 152). In the early dry season of December (DOY 344), the overall differences decrease again with major deviations only for the waterbody in the northern part. An additional way to assess the quality of the fusion is to analyze the consistency between different Landsat paths of the predicted time series. The overlapping areas between path 195 and 194 of the ESTARFM time series show a median deviation which is generally close to zero with a distinct temporal pattern over the course of the year (Figure 11). The first and third quartile shown in light blue ranges between \(-\)0.05 and 0.08. During the rainy season from DOY 100 to 320 (mid-April to mid-November), the differences between the two Landsat paths increase, which goes in line with the tendency towards increasing errors during the rainy season as described before. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Row**} & \multicolumn{3}{c}{**DOY 191**} & \multicolumn{3}{c}{**DOY 271**} & \multicolumn{3}{c}{**DOY 287**} & \multicolumn{3}{c}{**DOY 303**} \\ \cline{2-13}  & **MAE** & **RMSE** & \(\mathbf{R^{2}}\) & **MAE** & **RMSE** & \(\mathbf{R^{2}}\) & **MAE** & **RMSE** & \(\mathbf{R^{2}}\) & **MAE** & **RMSE** & \(\mathbf{R^{2}}\) \\ \hline
52 & 0.05 & 0.07 & 0.74 & 0.10 & 0.12 & 0.41 & 0.07 & 0.10 & 0.53 & 0.03 & 0.05 & 0.76 \\
53 & 0.06 & 0.07 & 0.58 & 0.07 & 0.09 & 0.30 & 0.06 & 0.08 & 0.33 & 0.05 & 0.06 & 0.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mean error values of the ESTARFM time series during the main rainy season validated with eight Landsat scenes of path 195 containing cloud cover of up to 80%. Figure 9: Scatterplots of observed Landsat _versus_ predicted ESTARFM NDVI values for Landsat path 194 for three selected dates representing the late dry season in early 2014 (DOY 40), the vegetative season (DOY 152) and the early dry season at the end of 2014 (DOY 344). Upper three plots show Landsat row 52, lower three plots Landsat row 53.
# Towards Integrated Digital Twins for Industrial Products: Case Study on an Overhead Crane
## 3 Results
### Case 3: AI-Enhanced Brake Condition Monitoring  
This case presents a methodology on how brake condition monitoring services can be enhanced with flow-based artificial intelligence (AI). This work was presented in a project seminar by [PERSON] and [PERSON] [53]. The case was developed by two consortium members: the crane vendor and an IoT service provider. The case builds on an existing data collection implementation and adds value through cloud-based data processing.  
The brake that holds the rope of the crane is a critical component from both functional and safety perspectives. It is also a maintenance-intensive component and hence the crane vendor provides brake monitoring. The pre-existing monitoring services were enhanced with flow-based AI methods to provide deeper insight. As a result, the AI-enhanced twin knows the state of the real counterpart, similarly as people know the state of their health. Based on the health information, the twin provides proactive observations to maintenance personnel. For example, the personnel can be alerted to service the brake earlier than scheduled due to a faster-than-expected wearing speed or to inspect the brake control system if it has triggered an excessive amount of fault signals in a short period of time.  
Technical implementation of the brake monitoring consists of \"flow nodes\" (Figure 9) that perform a certain activity to data and forward the result to the next node. The nodes also act as a visualization of the data processing pipeline and can be grouped into four sections based on their function:  
1. Data ingestion creates a time-series data lake by combining the MQTT data channels of multiple flow nodes.
2. Data selection nodes subscribe to topics, propagating notifications of new data to the following processing nodes.
3. Signal processing prepares data for AI with time aggregation, signal aggregation, transform, and other basic math functions.
4. Fuzzy inference (AI) nodes use fuzzy logic to determine crisp indices (e.g., 0-100%) and linguistic terms (e.g., excellent, good, degrading, worn, critical) for the selected attributes of the analyzed data.  
The flow-based AI method provides three types of advantages: (1) Data interpretation: the meaning of data is easier to understand with the linguistic terms. (2) Root-cause identification: e.g., warning in overall brake system health is caused by the wear speed of brake-lining. (3) Predictive maintenance: automatic notices, warnings, and alerts for operators, service personnel, and owners of the crane.  
The brake condition monitoring use case leverages the whole physical-digital-physical loop of a digital twin if the human maintenance at the end is accepted as part of the loop: the physical crane generates data that are processed digitally to create insights and alerts that enhance the physical condition of the crane through maintenance actions. In the future, if these algorithms prove reliable enough, critical alerts could trigger restrictions on crane control parameters.
## 2 Basic Concepts and Work Scope
### Positioning Data Aggregation Domains for Fingerprinting-Based Positioning
#### 2.4.1 Position Estimates Without Positioning Data Aggregation  
Case 1 (in Table 2), in Figure 7a, consists of pointwise mapping from the fingerprints to a position estimate, in which the pattern-matching method takes as input a set or vector of fingerprints and outputs a position estimate. In this case, there is a direct mapping from the fingerprints to a position estimate, so there is no positioning data aggregation. By pointwise, we refer to a case in which a position is estimated at a single spatial point, through a set or vector of fingerprints, without side information, in a single sensing or receiving element of the positioning device.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multicolumn{4}{c}{**Pattern-Matching Method**} & \multirow{2}{*}{**Aggregation**} \\ \cline{2-2} \cline{4-6}  & **Input (Fingerprints)** & **Output (Positions)** & **Number of Instances** & **Output Handling** & **Domain** \\ \hline
1 & Single set or vector & Single set or vector & One & None & (No aggregation) \\
2 & Multiple sets or vectors & Multiple sets or vectors & Multiple sets or vectors & One per input set & Aggregation & Positions \\
3 & Multiple sets or vectors & Single set or vector & One & None & Fingerprints \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cases for pattern-matching method and positioning data aggregation domains.
## 3 Results and Discussion
### Mass Balance
#### 3.1.3. Sugars Quantification  
Tables 4 and 5 show the total sugar contents of each product fractions (EA-S, EA-S-CS, EA-L, and EA-S-OL). Glucose content in the solid fractions showed no significant change while lignin content increased compared to the raw material. It means that the glucose in the EFB took limited damage by the steam explosion process and the organosolv treatment. It is possible that long reaction times increased the accumulation of degradation by-products and mass losses by volatilization [22]. As the  
Figure 3: Yields of the solid fractions from acid catalyzed steam explosion (ACSE) and alkali-catalyzed organosolv treatment (ACO) of EFB.  
\begin{table}
\begin{tabular}{c c c c} \hline  & \multicolumn{2}{c}{**Holocellulose (wt\%)**} & \multicolumn{2}{c}{** Lignin (wt\%)**} \\ \hline Raw EFB & 70.6 \(\pm\) 0.2 & 18.0 \(\pm\) 0.2 \\ \hline \multirow{4}{*}{EA-S} & 220-20 & 69.0 \(\pm\) 0.2 & 24.4 \(\pm\) 0.8 \\  & 0.5\% & 56.7 \(\pm\) 0.2 & 33.7 \(\pm\) 0.2 \\  & 1.0\% & 52.0 \(\pm\) 0.1 & 38.0 \(\pm\) 0.1 \\  & 2.0\% & 45.6 \(\pm\) 0.1 & 45.9 \(\pm\) 0.8 \\ \hline \multirow{4}{*}{EA-S-CS} & 220-20 & 64.1 \(\pm\) 0.1 & 23.2 \(\pm\) 0.8 \\  & 0.5\% & 56.5 \(\pm\) 0.4 & 25.3 \(\pm\) 0.4 \\ \cline{1-1}  & 1.0\% & 49.3 \(\pm\) 0.3 & 27.9 \(\pm\) 0.4 \\ \cline{1-1}  & 2.0\% & 42.8 \(\pm\) 0.1 & 30.1 \(\pm\) 0.3 \\ \hline \end{tabular} EA-S: solid fraction obtained from acid catalyzed steam explosion treatment of acid impregnated EFB. EA-S-CS: cellulose rich solid fraction obtained from alkali catalyzed organosolv treatment of EA-S fraction.  
\end{table}
Table 3: Component analysis of acid pretreated EFB solid fractions.  
acid concentration increased, however, the amount of glucose in the solid steadily decreased. When the acid concentration increased to 2.0%, the glucose amount decreased from 415 to 380 mg/g fraction. In contrast, concentration of glucose in EA-L dropped with 0.5 wt% acid pretreatment but increased by increasing acid concentration. Xylosyl groups were the most affected hemicellulose components during acid impregnation and the pretreatment process. The amount of xylose in the solid drastically decreased with increasing dilute acid concentration. As a result, concentration of xylose in the liquid fraction increased to twice of the control (10.1-21.9 mg/mL liquid). At 2.0% dilute acid concentration, more than 70% of the xylan in the EFB was hydrolyzed during two-step pretreatments. Arabinosyl, galactosyl, and mannosyl residues were removed from the solids by steam explosion with more than 0.5% acid concentration (Table 5).
# Evaluating 60 GHz FWA Deployments for Urban and Rural Environments in Belgium
## 2 Fixed Wireless Access
### Network Design  
Link distances of wireless systems using carrier frequencies in the mmWave band are typically limited to 200 m, which results in significantly smaller cells than sub-6 GHz deployments. In particular, when employing directional antennas at 28 GHz in midtown Manhattan, USA, only 33.3% of receiver locations within 200 m of the transmitter experienced an outage [15]. In contrast, 64.9% of places experienced it for distances under 425 m. Similarly, at 73 GHz, it is found that only 13.9% of locations within 200 m of the transmitter experienced an outage [16]. Early studies on mmWave cell deployment show that improving the base station location diversity enhances the system's coverage, particularly in rain and vegetation [17; 18].  
Authors in [19] compare an FWA deployment in the 3.5 GHz and 28 GHz bands for the urban city of Citra Raya Kikupa, Indonesia, showing that the mmWave band deployment is recommended due to lower cost, despite the fact that it requires almost doubling the nodes compared to the sub-6 GHz band. In [20], authors developed a design tool to optimise the placement of ENs and CPE working in the 28 GHz band for a non-legacy network, showing that their multi-iteration greedy algorithm outperforms traditional meta-heuristic algorithms. The optimisation of ENs placement on lamp posts in the street intersection of an urban scenario is studied in [21], showing that a 0-1 knapsack problem formulation could optimise the network deployment under certain constraints. A network-aware edge node placement problem is studied in [22]. Here, the authors developed a framework to implement an edge cloud network under 5G constraints, showing that their method can improve up to 30% the number of deployed ENs compared to heuristic and mixed integer linear programming (MILP) methods. Finally, a comprehensive tutorial on the diverse aspects of the design and implementation of 5G FWA networks is presented [23].
## 3 Preliminaries
### Review of [PERSON] et al.'s Scheme
#### 3.5.2. Login and Authentication Phase  
All entities establish a common session key in the login and authentication phase. Messages in this phase are sent via a public channel. Figure 2 details the login and authentication phase steps.  
Figure 2: Login and authentication phase of the scheme by [PERSON] et al.  
**Step 1:**: \(U_{t_{i}}\) enters identity \(ID_{i}\) and password \(PW_{i}\) in the mobile device, which computes \(Encpw_{i}^{*}=h(ID_{i}||h(PW_{i}))\) and verifies \(Encpw_{i}^{*}=Encpw_{i}\). If it matches, the mobile device generates a random number \(n_{i1}\) and a timestamp \(T_{s}\), and computes \(C_{i}=UI_{i}\oplus h(S_{k})\oplus n_{i1}\) and \(VerU_{t_{i}}=h(h(S_{k})||n_{i1})\). Then, the mobile device sends \(\{C_{i},VerU_{t_{i}},T_{s},UID_{i}\}\) to \(IoT_{SL_{j}}\) via a public channel.
**Step 2:**: When the authentication request is obtained from \(U_{t_{i}}\), \(IoT_{SL_{j}}\) selects a random number \(n_{i2}\) and computes \(D_{i}=SI_{j}\oplus n_{i2}\) and \(Verf_{sj}=h(SI_{j}||n_{i2})\). Next, \(IoT_{SL_{j}}\) transmits a message \(\{C_{i},VerU_{t_{i}},T_{s},UID_{i},SID_{j},D_{i},Verf_{sj}\}\) to the \(CS\).
**Step 3:**: The \(CS\) receives a message from \(IoT_{SL_{j}}\) and first validates timestamp \(T_{s}\). If valid, the \(CS\) retrieves \(UI_{i}\) against \(UID_{i}\). The \(CS\) computes \(SI_{j}^{*}=h(SID_{j}||S_{k})\), \(n_{i2}^{*}=SI_{j}^{*}\oplus D_{j}\), and \(Verf_{sj}^{*}=h(SI_{j}^{*}||n_{i2}^{*})\) and checks \(Verf_{j}^{*}\stackrel{{?}}{{=}}Verf_{sj}\). The \(CS\) computes \(n_{i1}^{*}=UI_{i}\oplus h(S_{k})\oplus C_{i}\) and \(VerU_{t_{i}}^{*}=h(h(S_{k})||n_{i1}^{*})\) and checks \(VerU_{t_{i}}^{*}\stackrel{{?}}{{=}}VerU_{t_{i}}\). If matched, the \(CS\) generates random number \(n_{i3}\) and timestamp \(T_{s}\). Afterward, the \(CS\) computes \(SK=h(C_{i}||h(S_{k}))\oplus h(n_{i1}\oplus n_{i2}\oplus n_{3})\), \(E_{i}=n_{i1}\oplus n_{i2}\oplus h(SID_{j}||n_{i2})\), \(F_{i}=h(C_{i}||h(S_{k}))\oplus h(SID_{j}||n_{i2})\), and \(L_{i}=n_{i2}\oplus n_{i3}\oplus h(C_{i}||H(S_{k}))\), and then transmits \(\{E_{i},F_{i},L_{i},T_{s}\}\) to \(IoT_{SL_{j}}\).
**Step 4:**: \(IoT_{SL_{j}}\) verifies \(T_{s}\) and calculates \((n_{i1}\oplus n_{i3})=E_{i}\oplus h(SID_{j}\oplus n_{2})\), \(h(C_{i}||h(S_{k}))=F_{i}\oplus h(SID_{j}\oplus n_{2})\), and \(SK=h(C_{i}||h(S_{k}))\oplus h(n_{i1}\oplus n_{i2}\oplus n_{i3})\). Then, \(IoT_{SL_{j}}\) sends the rest of message \(\{L_{i},T_{s}\}\) to \(U_{t_{i}}\).
**Step 5:**: \(U_{t_{i}}\) also confirms the validity of \(T_{s}\). If legitimate, the user computes \(n_{i2}\oplus n_{i3}=L_{i}\oplus h(C_{i}||h(S_{k}))\) and shares a session key \(SK=h(C_{i}||h(S_{k}))\oplus h(n_{i1}\oplus n_{i2}\oplus n_{i3})\) with the \(CS\) and \(IoT_{SL_{j}}\).
## 2 Theoretical Development
### Construction of a Real Symmetric Matrix \(\mathbf{J}_{s}\) with All Expected Eigenvalues  
In order to achieve the goal of partial assignment of the natural frequencies of the system, the \(n\) system eigenvalues are denoted by \(\lambda_{1},\lambda_{2},\ldots,\lambda_{n}\). It is expected that a set of \(p<n\) original system eigenvalues \(\mathbf{\Lambda}_{1}=\text{diag}\Big{(}\lambda_{1},\lambda_{2},\ldots,\lambda _{p}\Big{)}\) are replaced by the desired eigenvalues \(\mathbf{\mu}_{1}=\text{diag}\Big{(}\mu_{1},\mu_{2},\ldots,\mu_{p}\Big{)}\) after accomplishing some related system mass and stiffness modifications. Furthermore, \(\mathbf{\Lambda}_{2}=\text{diag}\Big{(}\lambda_{p+1},\lambda_{p+2},\ldots, \lambda_{n}\Big{)}\) contains all the eigenvalues that remain unchanged. \(\mathbf{X}_{1}=\Big{\{}\mathbf{x}_{1},\mathbf{x}_{2},\ldots,\mathbf{x}_{p} \Big{\}}\) is the mass-normalized eigenvector matrix of Equation (2) corresponding to \(\mathbf{\Lambda}_{1}\).  
From the spectral decomposition theorem of symmetric matrices, a new matrix \(\mathbf{J}_{\text{s}}\) can be expressed as follows:  
\[\mathbf{J}_{\text{s}}=\mathbf{U}_{1}\mathbf{\mu}_{1}\mathbf{U}_{1}^{\text{T}} +\mathbf{U}_{2}\mathbf{\Lambda}_{2}\mathbf{U}_{2}^{\text{T}} \tag{5}\]  
which contains the desired eigenvalues of the modified system and has the same eigenvectors as \(\mathbf{J}_{0}\). \(\mathbf{U}_{1}\) and \(\mathbf{U}_{2}\) are the normalized eigenvector matrices of Equation (3), corresponding to \(\mathbf{\Lambda}_{1}\) and \(\mathbf{\Lambda}_{2}\), respectively.  
Based on the relationships of \(\mathbf{U}_{1}=\mathbf{D}_{0}\mathbf{X}_{1}\) and \(\mathbf{U}_{2}\mathbf{\Lambda}_{2}\mathbf{U}_{2}^{\text{T}}=\mathbf{J}_{0}- \mathbf{U}_{1}\mathbf{\Lambda}_{1}\mathbf{U}_{1}^{\text{T}}\), Equation (5) can be changed to:  
\[\mathbf{J}_{\text{s}}=\mathbf{J}_{0}+\mathbf{D}_{0}\mathbf{X}_{1}(\mathbf{\mu }_{1}-\mathbf{\Lambda}_{1})\mathbf{X}_{1}^{\text{T}}\mathbf{D}_{0}. \tag{6}\]  
However, it should be noted that \(\mathbf{J}_{\text{s}}\) is not easy to determine in practice, although it contains all the desired eigenvalues; that is, it is difficult to find the relationship between \(\mathbf{J}_{\text{s}}\) and the mass and stiffness matrices. Then, it is useful to reconstruct a matrix \(\mathbf{J}\), which is in the same matrix form as \(\mathbf{J}_{0}\) so that it can be easily used to obtain the system matrices of new mass-spring systems. Besides, many machines have fixed or complex structural configurations, which are not easy to change or even not allowed to be changed. So, there are many special physical limitations to structural modifications in engineering applications. In order to solve this problem, it is critical to find a method that can not only reconstruct a matrix \(\mathbf{J}\), but also satisfy special constrains. In this paper, the method introduced in Ref. [40] will be applied to solve this problem, and then a partial frequency assignment for the torsional vibration control of shafting systems will be achieved.