esa-sceva/satcom-chunk-collection huggingface dataset:
# IoT-Driven Resilience Monitoring: Case Study of a Cyber-Physical System
## 5 Cyber-Physical System
### Data Infrastructure: Data Streaming and Persistence Middleware
#### 5.2.2 Data Persistence  
The Data Persistence service consists of two microservices that make it possible to store historical records of data flowing back and forth between the the SPVS and the Energy DT's services, namely sensor readings and action commands. These two microservices implement the interface and the interaction with the reference DBMS instance. Among the possible database solutions, MongoDB was identified as the most suitable for the case study, thanks to its document-based nature that fosters high performance querying of IoT data [84] alongside an efficient handling of several insert queries. When comparing to relational DBMSs such as MySQL, MongoDB allows to outperform in terms of resources usage and overall latency [85; 86].  
MongoDB's document-based approach allows to store and process heterogeneous data types, such as sensor data, time-series data, and metadata, in a single document. Supporting an efficient and effective local data storage on edge devices, MongoDB allows to reduce the need to stream data towards remote servers or cloud infrastructures. Concerning the DBMS interface microservice, a Python script represents the most suitable solution for the present case study in terms of disk usage and responsiveness performance. Indeed, the usage of state-of-the-art software solutions, such as Apache NiFi, can be counterproductive and challenging due to the high computational needs they require to run on the resource-constrained fog device used for the testbed. A Python script provides a subscription for each MQTT topic related to both the physical and the digital counterpart, acting as a man in the middle. In this sense, it intercepts the corresponding data flows exchanged by the two counterparts and prepares the carried data entities for storage by interacting with the MongoDB microservice.
# Orbital-Based Automatic Multi-Layer Multi-Pass Welding Equipment for Small Assembly Plates
## 2 Design and Analysis of Rail-Mounted Automatic Welding Equipment
### Operational Logic and Workflow of the Equipment  
#### 2.3.1 Operational Logic  
The operational logic and workflow of the rail-mounted automatic welding equipment include overall mechanical installation, power distribution and supply, load bearing, and communication connections. The logical components of this operating system can be understood in conjunction with Figure 15.  
The electric control cabinet supplies power to the welding machine and the main station, which is a mobile control cabinet. The main station provides power to the motors and servo drives, reducers, wire feeders, laser sensors, and the robotic arm body. The software control adopts a client/server (C/S) architecture with modular implementation. The slave station (client), namely the upper computer (HMI), connects wirelessly to the main station to improve transmission efficiency and reduce unnecessary safety hazards caused by cables. The upper computer achieves control over the welding machine, robotic arm, laser sensor, and motor servo drives by invoking services and also handles overall timing, algorithms, and strategies. The welding machine, laser sensor, and robotic arm communicate with the main station using DeviceNet, ModbusTCP, and EtherCAT protocols, respectively. The output and input ports of the motor servo drives are connected to the motors and the main station, respectively; together with the main station and robotic arm, they use the EtherCAT protocol, adding an external axis control system. The laser sensor and welding torch are mounted on the robotic arm, which, along with the motor, reducer, and wire feeder, is installed on the base of the rail cart. The carrier vehicle bears the shielding gas, welding machine, electric control cabinet, and main station, with the type of shielding gas selectable according to requirements.
## 3 Proposed Methodology
### Model Validation  
To validate our model, we used four performance metrics: Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and R squared value (\(R^{2}\)). MAE and MAPE only consider the amount of the error, not the sign. Therefore, it eliminates the possibility of positive and negative errors canceling each other out. RMSE takes the square of the error before averaging it, and therefore, it gives more weight to the large error. It is therefore more useful when the high error is undesirable like stock price prediction. Finally, the \(R^{2}\) value indicates the risk associated with a model while predicting a financial asset's price.  
**Mean Absolute Error (MAE)** measures the absolute average error between the real data and predicted data and is calculated using the Equation (11).  
\[MAE=\frac{1}{N}\sum_{j=1}^{N}|y_{j}-y_{j}^{{}^{\prime}}| \tag{11}\]  
where \(y_{j}\) = actual value, \(y_{j}^{{}^{\prime}}\) = predicted value, \(N\) = total number test cases, and \(j\) = value ranging from 1 to N.  
**Mean Absolute Percentage Error (MAPE)** is the mean of absolute percentage error where the error is defined as the absolute difference between actual and predicted value [41]. MAPE is easy to understand as the error is represented in terms of percentage. It is calculated using Equation (12).  
\[MAPE=\frac{1}{N}\sum_{t=1}^{N}|\frac{A_{t}-P_{t}}{A_{t}}| \tag{12}\]  
where \(A_{t}\) = actual value, \(P_{t}\) = predicted value, \(N\) = total number of test cases, and \(t\) = value ranging from 1 to N.  
**Root Mean Squared Error (RMSE)** is the rooted value of the squared average distance between the real data and the predicted data and is calculated using Equation (13).  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(A_{t}-P_{t})^{2}} \tag{13}\]  
where \(A_{t}\) = actual value, \(P_{t}\) = predicted value, \(N\) = total number of test cases.  
**R Squared Value** or the coefficient of determination, is an indicator of goodness of fit of a model. It indicates how close the regression line (i.e., the predicted value curve) is to the actual data values. The R squared value lies between 0 and 1, where 0 indicates that the model cannot capture the correlation among the input and output data, while 1 indicates that the model is perfectly fitted by the dataset. In the financial market, the \(R^{2}\) value is widely used to determine the risk-adjusted return of a financial asset [3]. A higher \(R^{2}\) value indicates lower risk associated with the model and vice versa.
## 3 Proposed Method
### Adjustment of Adaptive Contention Window  
The energy consumption caused by the contentions is minimized by using an ACW adjustment technique in the MAC layer. the path with higher contention is required to wait and the nodes with less residual energy are required to accomplish the immediate data transfer. Hence, this ACW is used to minimize energy consumption and improve data delivery. The information shared among the various layers is used to create adaptability, which mainly depends on the protocol design method. Next, this design considers the channel features in the higher layers and it considers the traffic's stochastic arrival in the lower layers. The performance part of the cross-layered method stems from the selection of appropriate data that are being used.
## 2 System Model with Collision Design and Analysis of Our Proposed System
### Collision Avoidance  
Every layer can efficiently handle horizontal collisions through energy efficiency and cluster formation. The decentralized clustering process helps the head communicating nodes of the cluster to select unique subsections from the other clusters in that region. There  
Figure 1: Detailed overview of the designed paradigm.  
fore, communicating nodes from neighboring clusters may communicate with one another without falling into any collisions. Additionally, some of the cluster's communicating nodes connect to the system's cluster head (CH) utilizing the handshake mechanism. This technique consists of layering the network area, allocating time periods, and controlling energy levels to prevent vertical collisions. Additionally, the network can be divided into four types of layers: layer 1, layer 2, layer 3, and layer 4, where every layer is subdivided into multiple types of clusters. Adjacent clusters communicate multiple times throughout the operating phase, whereas non-adjacent clusters send concurrently. Furthermore, energy-efficiency and time-stamp approaches are utilized to isolate various levels within a cluster in order to avoid collisions among communicating nodes belonging to nearby layers. The collision probability as a function of the total number of communication nodes \(N\) can be expressed with \(p_{c}\), where \(t\) is the time for transmission and \(W\) is the contention window, as follows:  
\[p_{c}=1-\left(t-\frac{1}{W}\right) \tag{1}\]  
where \(W\) denotes the contention window, that controls how to transmit multiple nodes simultaneously. The energy consumed because of collisions \(E_{c}\) is expressed as a function \(C\) over the total number of collisions, \(E_{t}\), per packet transmission, and \(R\) retransmission attempts.  
\[E_{c}=C\cdot E_{t}\cdot R \tag{2}\]  
This energy figure also includes the overhead of retransmitting packets due to losses from collisions. The probability of collision-free transmission \(P_{c}f\) is as follows:  
\[P_{c}f=\left(1-\frac{P_{c}}{N}\right)^{N-1} \tag{3}\]  
The equation shows the increasing probability of collision-free transmission as we increase the contention window or network efficiency.
# Roller-Integrated Acoustic Wave Detection Technique for Rockfill Materials
## 2 Methodologies
### Sound Compaction Value  
During rolling construction, the compacted layer is synthetically affected by the static pressure and vibration force. The comprehensive effect can change the material's status from static to vibrating, then reduce the friction force and cohesion between soil (or rock) particles. Thus, small soil (or rock) particles fill the voids between larger particles and the material gradually becomes dense. When rolling construction begins, filling materials are relatively loose (Figure 10a), the sound pressure curve is relatively smooth (Figure 10d), the corresponding sound spectrum is predominant at the fundamental frequency (\(f_{0}\)), and the amplitude of the SCV is relatively low (Figure 10g). With an increase in the number of compactions, the compacted material becomes more dense (Figure 10b), and the higher harmonic components gradually increase (Figure 10e). Meanwhile, the SCVs change gradually (Figure 10h). As the number of compaction times increases to a certain degree, the content of higher harmonic components tends to be stable (Figure 10f), and the compacted material becomes denser and tends to be stable (Figure 10c). The SCVs further change and tend to be stable (Figure 10i). This paper defines a real-time SCV as the compaction index for characterizing the compaction status of rockfill materials as follows:  
\[\text{SCV}=k\times\text{SHA}\times 100\%, \tag{16}\]  
where \(k\) is the calibration coefficient of the material.  
Figure 10: Illustration of changes in sound pressure and SCV with increasing ground stiffness.
# GPR Data Interpretation Approaches in Archaeological Prospection
## 5 Deep Learning Algorithms to Interpret GPR Data  
The automatic interpretation of GPR data from archaeological prospection is rather an unexplored but emerging research topic due to the recent developments of Deep Learning (DL) algorithms. Especially, the Convolutional Neural Networks (CNNs) are effective in performing various computer vision tasks like classification, object detection, and image segmentation [38]. These tasks can be useful in the GPR data interpretation process to identify targets faster and help to discriminate complex patterns that characterize the GPR images. CNNs are based on representation learning [39], where important information, called features, is extracted automatically from the data. The latter is the main difference over traditional Machine Learning (ML) algorithms, where the features are handcrafted and extracted manually. A semi-automatic approach following the traditional ML methods is given in Reference [40], where the geometry of the interpreted anomalies is extracted and visualized in 3D. However, this method is found to be sensitive to noise and requires preprocessing steps, including manual interpretation.  
Few studies exist in the literature exploring the automatic GPR data feature extraction capability of DL algorithms. An example utilizes a modification of CNNs, known as Fully Convolutional Networks (FCNs), that performs image segmentation through the U-net architecture [41]. In this study, the trained model takes as an input a GPR C-scan and outputs the linear features in segments that are attributed to structures. The results show high potential in detecting linear features compared to the manual interpretation performed in GIS. However, the authors mention limitations related to challenging training network processes and requiring a large amount of data.  
Even though similar studies for archeological prospection are currently lacking, there are some interesting examples that use GPR data derived from civil engineering applications, which show that DL is a promising direction worth investigating. In some of these examples, classification of cavities, pipes, manholes, and subsoil background from urban streets is performed, using as CNNs' training data both B-scans and C-scans [42], a combined representation of B-scans and C-scans [43], and 3D data [44]. Other approaches use object detection algorithms based on CNNs to locate rebars in concrete [45], pavement distress [46], and internal defects in tunnel lining [47]. The above-mentioned examples give an insight into the capabilities of the various CNN-based algorithms and must be adapted to GPR data from archaeological prospection.
## 2 Battery and Hydrogen Energy Storage System
### BESS, HESS, and Other Energy Storage Systems  
Battery energy storage systems (BESSs) have emerged as a promising technology for addressing challenges in modern power systems, particularly with the increasing integration of renewable energy sources. BESSs offer high efficiency, with round-trip efficiencies exceeding 90%, and rapid response times within milliseconds. These systems are highly scalable, suitable for applications ranging from residential to utility-scale installations. BESSs can mitigate issues associated with solar power generation, such as ramp rate control, frequency regulation, and voltage stability. They also enable energy time-shifting and output leveling, enhancing the economic viability of solar resources. BESSs are considered a key technology for demand-side management and grid flexibility [95; 96; 97]. Despite their numerous advantages, challenges remain in optimizing BESS deployment, including sizing, operational control, and environmental impact considerations [98]. Battery energy storage systems (BESSs) are crucial for integrating renewable energy sources and improving grid stability. While BESSs deployment is increasing globally, it falls short of projections for a net-zero scenario. BESSs face challenges such as capacity degradation, high initial costs, and environmental concerns regarding raw material extraction and
## 2 Statistical Analyses of Individual Hazards
### Assessment of the Peak Flood Discharge after a Dam Breakage
#### 2.3.1 The Method of Calculating the Peak Outflow Rate  
The higher the landslide dam or the larger the reservoir capacity of the dam lake, the more potential energy will be stored in the dam. Because the shape of the breach is difficult to predict, the development process of the breach can be ignored, and the relationship between the influences of some related factors on the peak outflow rate can be estimated on the basis of a statistical analysis of the cases of dam breaches.  
The estimation model proposed by [PERSON] and [PERSON] [10] should be improved by considering the influencing factors of the dam's volume and its erodibility. According to the 34 dam breaching cases listed in Table A3, a new formula is proposed:  
\[\left\{\begin{array}{l}Q_{p}=\frac{\beta\times(V_{l}\times H_{d}\times\gamma _{w})^{0.5}}{V_{0}},\ H_{d}\leq 20\text{ m}\\ Q_{p}=\beta\times(V_{l}\times H_{d}\times\gamma_{w})^{0.5},\ H_{d}>20\text{ m}\end{array}\right. \tag{17}\]  
Figure 5: Schematic diagram for estimating the reservoir capacity of a barrier lake. (a) V-shaped valley. (b) U-shaped valley.  
where \(\beta\) is the coefficient of the erodibility related to the dam materials. According to the results of analyzing the database, the suggested values of \(\beta\) are listed in Table 3.  
Figure 6 shows a comparison between the measured peak outflow rate and the calculated peak outflow rate, and presents a good degree of fitting with a correlation coefficient of 0.934. However, the correlation coefficient of [PERSON]'s formula only reached 0.787 in this database.
# Neural Radiance Fields with Hash-Low-Rank Decomposition
## 4 Methods
### Color  
In this section, we introduce the theoretical model for color modeling in our design. Given a 3D spatial coordinate \(d=(x,y,z)\) and a feature grid \(G_{rgb}\) with a feature dimension of \(R_{rgb}\), our method queries the vector feature \(F_{rgb,r_{1}}^{v}\) of each dimension \(r_{i}\) using the coordinates \(x\), \(y\), and \(z\) in the vectors \(v_{x}\), \(v_{y}\), and \(v_{z}\), respectively. Similarly, it queries the plane feature \(F_{rgb,r_{1}}^{m}\) of each dimension using the coordinates \(yz\), \(xz\), and \(xy\) in the matrices \(M_{yz}\), \(M_{xz}\), and \(M_{xy}\), respectively. Up to this point, the process is basically similar to density modeling.  
The extracted features are represented as a set of features, each of which can be regarded as corresponding to one of the axes (\(x\), \(y\), or \(z\)) in the \(xyz\) coordinate system. Specifically, \(F_{rgb,r_{1}}(V_{x},M_{yz})\) represents the features corresponding to the \(x\)-axis, where \(V_{x}\) denotes the vector component along the x-axis, and \(M_{yz}\) represents the feature plane spanned by the \(y\) and \(z\) axes. Similarly, \(F_{rgb,r_{1}}(V_{y},M_{xz})\) and \(F_{rgb,r_{1}}(V_{z},M_{xy})\) correspond to the \(y\)-axis and \(z\)-axis, respectively, capturing the interactions between vector components and their associated feature planes. We use a low-rank factor \(B\) to reduce the rank of the extracted information, as demonstrated in [13]. We concatenate (Concat) the three groups of features to facilitate decoding.  
To better represent the three groups of features, we define  
\[C_{rgb,r_{1}}^{i}=F_{rgb,r_{1}}(V_{r},M_{xyz-i})\otimes B. \tag{5}\]  
The overall process is described by Equation (6):  
\[\begin{split} G_{rgb}(d)&=\sum_{r=1}^{R_{rgb}}(F_ {rgb,r_{1}}^{p,x}\otimes F_{rgb,r_{1}}^{m,yz}\otimes b)\oplus(F_{rgb,r_{1} }^{p,y}\otimes F_{rgb,r_{1}}^{m,xz}\otimes b)\oplus(F_{rgb,r_{1}}^{p,z} \otimes F_{rgb,r}^{m,xy}\otimes b)\\ &=\sum_{r=1}^{R_{rgb}}(F_{rgb,r_{1}}(V_{x},M_{yz})\otimes b) \oplus(F_{rgb,r_{1}}(V_{y},M_{xz})\otimes b)\oplus(F_{rgb,r_{1}}(V_{z},M_{ xy})\otimes b)\\ &=\sum_{r=1}^{R_{rgb}}\oplus_{m\in\{XYZ\}}C_{rgb,r}^{m}(x) \otimes B.\end{split} \tag{6}\]  
Subsequently, similar to the steps in density modeling, we use multi-hash encoding to compress the plane features, obtaining the final feature \(C^{\prime}rgb,r=Frgb,r(V,M)\). The concatenated features are then fed into the decoder \(D\). Unlike density modeling, predicting the color also requires the additional input of camera pose \(\phi\). As in NeRF, we apply positional encoding (PE) to \(\phi\), concatenate it with the features retrieved from the grid, and finally feed them into the decoder to obtain the final RGB values. The overall process is described by Equation (7):  
\[\begin{split} rgb&=D(G_{rgb}(d))\\ &=D((\sum_{r=1}^{R_{rgb}}\oplus_{m\in\{XYZ\}}C_{rgb,r}^{m}(x) \otimes B)\oplus PE(\phi)).\end{split} \tag{7}\]  
In out implementation, we use an MLP as \(D\) to decode the color feature.
## 1 Introduction
The major contributions of this work may be listed as follows:  
* We introduce the concept of minimum expected delay (MED), which is employed to denote the expected earliest time that messages can be successfully delivered to the sink node. * We propose the MEDR routing protocol for data gathering in DTMSNs with high data delivery ratio and low transmission overhead and delay. * We compare the performance of the proposed protocol with several existing approaches and show that MEDR outperforms the existing approaches. The rest of the paper is organized as follows: we review the related work in Section 2 and identify the problems in the existing works. We present the MEDR protocol in Section 3. The simulation is carried out, and the performance is evaluated in Section 4. Finally, we conclude this work in Section 5.
# Zeroes: Robust Derivative-Based Demodulation Method for Optical Camera Communication
## 4 Experimental Setup
### Reception  
The reception of LED brightness variations is operated by the Sony IMX477R 12 MP image sensor of the Raspberry Pi HQ camera [26], equipped with a 6 mm wide-angle lens to cover a large view of the transmitter and its nearby environment. The camera is positioned at a height of 120 cm in each scenario, and it is positioned at a lateral distance of 105 cm from the LED panel in the case of NLOS transmission. Image capture is performed by a Raspberry Pi 4 through a Python program configuring the camera's parameters based on the Picamera2 library. Image resolution is set to the maximum of 4056 \(\times\) 3040, the frame rate is fixed to 40 fps, and the minimum exposure time of 1/16667 s is selected in order to achieve better contrast between the bright and dark strips. Since the ISO value mainly depends on ambient light, its parametrization must therefore be carried out in each environment; here, it is set at 5769. Considering that the image sensor is oriented in landscape mode, 90\({}^{\circ}\) clockwise rotation is applied on images to recover the usual vertical strips. Through the \(P_{b}\) parameter, which is equal to 8.25 here, the bit period of the transmitted signal is known by the receiver. In order to minimize the whole processing time, images are decimated in both columns and rows (\(\Delta_{col}\) = 4, \(\Delta_{row}\) = 100) to reduce their resolution (from 3040 \(\times\) 4056 to 41 \(\times\) 760). The different parameters of the receiver (RX) are summarized in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**TX Parameters** & **LOS** & **NLOS** \\ \hline LED power & 19.91 W & 19.91 W \\ Panel dimension & 30 \(\times\) 30 cm & 30 \(\times\) 30 cm \\ Panel height & 120 cm & 130 cm \\ Panel/wall distance & / & 110 cm \\ Panel/wall orientation & / & 30\({}^{\circ}\) \\ Header & 10 bits & 10 bits \\ Payload & 40 bits & 40 bits \\ Data rate & 4 kbps & 4 kbps \\ Modulation & OOK & OOK \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of TX parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**RX Parameters** & **LOS** & **NLOS** \\ \hline Camera & Raspberry Pi HQ & Raspberry Pi HQ \\ Camera height & 120 cm & 120 cm \\ Camera/LED lateral dist. & / & 105 cm \\ Resolution & 4056 \(\times\) 3040 & 4056 \(\times\) 3040 \\ Focal length & 6 mm & 6 mm \\ Frame rate & 40 fps & 40 fps \\ Exposure time & 1/16667 s & 1/16667s \\ \(T_{shutter}\) & 23.3 ms & 23.3 ms \\ ISO & 5769 & 5769 \\ Average ambient light & 1173 lx & 3724 lx \\ \(P_{b}\) & 8.25 & 8.25 \\ \(\Delta_{col}\), \(\Delta_{row}\) & 4, 100 & 4, 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of RX parameters.