esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods  
### Study Area and In-Situ Measurements  
Dahra is a town of commune status located in the Louga region of Senegal at roughly 264 km from Dakar, with a center latitude of 15 deg21'N and a center longitude of 15 deg36'W. The region has a tropical climate with pleasant heat throughout the year, with well-defined dry and humid seasons that result from the northeast winter winds and the southwest summer winds. The dry season is from December to April and the wet season is from roughly May to October. Located in the northwest part of the Ferlo Basin, our study area, as shown in Figure 1, is a sandy pastoral region, where the predominant soil types are red-brown sandy soils and ferruginous tropical sandy soils, which is covered by open shrub steppes and grasslands. According to [PERSON] et al. and [PERSON] et al. [34; 35], tree and shrub canopy cover does not exceed 5% of the total area on average, and the pseudo-steppe consists of a discontinuous herbaceous cover of annual grasses.  
Figure 1: Information of the study area: (**a**) map of Senegal; (**b**) Google Earth image of the Dahra study site; and (**c**) International Geosphere Biosphere Programme (IGBP) land-cover types of the study area.  
The in situ measurements of surface soil moisture were provided by the International Soil Moisture Net (ISMN [36]), and measured by the ThetaProbe ML2x sensor. The precision of the volumetric soil moisture is approximately \(\pm\)1%. Together with precipitation and soil temperature, the values were recorded with a 1-h interval at the study site from 4 July 2002 to 1 January 2016. Both the soil temperature and the volumetric soil moisture from a depth of 5 cm at 19:00 UTC were used in the experiment, which coincidence with the acquisition time of Sentinel-1A.
## 2 Issues in Topology Design
### Resource Oriented Considerations  
Designing topology for resource-constrained sensor network requires careful consideration regarding the consumption of resources such as energy, time, processing capability, and memory requirements.  
Obviously, the first consideration should be energy. [27, 28] have shown that chain oriented topology greatly reduces energy consumption, and thus this section will not attempt to compare the consumption of energy and other resources within different topologies that has already been reviewed. This section, however, will focus on areas of resource utilization within chain oriented topology that can be further improved.  
The aim of the resource oriented consideration is not only to save energy but also to ensure that energy dissipation is evenly distributed. As the chain leaders undertake more tasks and long distant communications, they deplete energy more rapidly compared with other nodes in the network. Thus, it is important to change the role of the leader regularly, so that the load of the leader is distributed among many nodes. Conversely, very frequent changing of the role of chain leaders can diminish the performance of chain oriented sensor networks. At the same time, energy consumption also increases, due to an increase in the number of control messages regarding the new leaders, and their selection procedures. Thus, determining the time to change the role of the leader is crucial.  
Another important resource related issue is the _time_ required by the network to perform an operational round. If the sensors of the network construct only one chain, latency becomes very high. This is because each sensor node needs to wait for the data from its predecessor node. This latency can be reduced by constructing multiple chains using the sensor nodes. Multiple chains in the network introduce parallelism, which directly recommends the use of multiple chains instead of a single chain. Multiple chains are advantageous compared with a single chain not only for decreasing the latency, but also for receiving other facilities, such as scalability, flexibility, and ease for management. For these reasons, we propose a topology that uses multiple chains instead of a single chain.
# MAC Protocols for mmWave Communication: A Comparative Survey +
## 6 Challenges & Future Research Directions
### Mobility Management  
Mobility can rapidly alter the quality of established mmWave connections. Nodes in vehicle-to-everything (V2X) networks change positions at varying speeds. A management module should be created at the MAC layer to monitor node location and speed to maintain network connectivity. A mobility model for each situation must be developed for such an application. The mobility management module will determine how to operate the handover and keep the connection stable until the transmission is completed without any interruption. In mobile environments, incorporating quick switching capabilities can also help to minimize beam switching time and latency.
# Free Space Optical Communication: An Enabling Backhaul Technology for 6G Non-Terrestrial Networks
## 4 Challenges and the Road Ahead
### Scalability  
To provide wide coverage, the deployment of multiple HAPSs and multiple UAVs in NTNs is necessary [103]. However, as the number of HAPS platforms grows, managing and coordinating them becomes increasingly complex. Ensuring seamless handovers between platforms and maintaining overall system reliability become difficult tasks, particularly given the dynamic nature of these platforms in the stratosphere. Additionally, aligning optical beams from multiple moving HAPS platforms with ground-based receivers becomes increasingly challenging, due to fluctuating atmospheric conditions and other environmental factors, necessitating continuous beam adjustments. These complexities of beam alignment can lead to potential errors and a deterioration in system performance, which becomes more pronounced as the network is scaled up.  
Achieving scalability in HAPS-based FSO communication systems also necessitates the development of robust network management and control mechanisms specific to the unique challenges posed by HAPS. These include efficient resource allocation and routing algorithms, congestion control, fault tolerance, and traffic management across numerous HAPS platforms. Energy efficiency remains a vital concern, requiring a balance between power requirements and endurance. Ensuring data protection and interference management is crucial for network security as the HAPS network is scaled up. Additionally, as more HAPS platforms are deployed, regulatory considerations specific to HAPS operations become important, demanding adherence to aviation and airspace regulations.  
UAVs that operate at lower altitudes (up to a few kilometers above the Earth's surface) serve as vital components of NTNs. As the number of these UAVs increases to meet increasing demand, scalability concerns emerge. Managing a network of UAVs involves addressing issues such as efficient deployment, route planning, and maintaining stable and interference-free connections. These challenges differ from those of HAPS, due to the closer proximity to the ground, which introduces additional environmental and operational considerations.  
Cost is another key factor that needs to be taken into account. The costs involved are multifaceted and encompass various aspects, including the acquisition of HAPSs, optical communication equipment, ground station infrastructure, and ongoing maintenance expenses. Additionally, factors such as spectrum licensing fees and regulatory compliance costs can influence the overall financial outlook if optical signal licensing is required in the future. As UAV deployment increases alongside HAPS, it is imperative to consider the cost implications of integrating both types of platform into the NTN architecture. Balancing cost-effectiveness with performance and scalability will remain a central challenge in the development of NTNs.
## 2 Fundamentals  
### Basic EIT Equations  
The EIT handles the problem of reconstructing the conductivities inside a domain of interest, for example, the human body, through boundary voltage measurements. Accordingly, N electrodes are placed equidistantly across a slice of a body. A current pattern is injected through these electrodes, and the resulting voltages are measured. There are different current injection and voltage measurement patterns, which have distinct advantages and disadvantages. For more information on this, see [26]. The voltage measurement patterns also have different advantages and disadvantages. The problem of EIT can be modeled as follows:  
\[\
abla(\sigma(z)\
abla u(z))=0,\quad z\,\in\,\Omega \tag{1}\]  
where \(u\) represents the electrical potential, \(\Omega\) is the 2D domain (the slice through the body) of interest, \(\sigma\) is the location-dependent conductivity of the domain, and \(z\) denotes the position expressed as a complex number \(z=x+j\cdot y\) (with \(j=\sqrt{-1}\)). The position is denoted as a complex number \(z\) due to mathematical considerations with the usage of complex geometrical optics solutions. Only real-valued conductivities are considered for the sake of simplicity. The system from Equation (1) is excited by an electric current to reconstruct an estimate of \(\sigma\) inside \(\Omega\). Mathematically speaking, this is the Neumann boundary condition that constrains the partial differential equation of Equation (1). This can be expressed in the notation of Equation (1) as  
\[\sigma(z)\frac{\partial u}{\partial\vec{n}}(z)=J(z),\quad z\,\in\,\partial\Omega \tag{2}\]  
where \(\vec{n}\) is the outward normal vector of the boundary of \(\Omega\), \(J\) is the current density, and \(\partial\Omega\) denotes the boundary of \(\Omega\). In theory, it is also possible to apply known voltage distributions on the boundary of \(\Omega\) and then measure the resulting currents. The known voltage distribution is called the Dirichlet boundary condition and can be expressed in the notation of Equation (1) as  
\[u(z)=f(z),\quad z\,\in\,\partial\Omega \tag{3}\]  
where \(f(z)\) is an arbitrary function in the sense that, depending on the injected current, it can lead to different forms. It is possible to map the Dirichlet boundary condition to the Neumann boundary condition. This map is called the \"Dirichlet-to-Neumann map\" (DN-map). It can more formally be stated as  
\[\Lambda_{\sigma}\,:\,u\mapsto\sigma\frac{\partial u}{\partial\vec{n}} \tag{4}\]  
[PERSON] and [PERSON] showed that knowledge of the DN-map dictates the conductivity inside \(\Omega\) uniquely [27]. Our take on the explanation of its fundamentals can be found in the Appendix A.1 or, for a more detailed look, in Chapters 14 and 15 of [PERSON]'s and [PERSON]'s book [28].
## 3 Drone Routing Algorithms
### Security Algorithms Used in Drone Routing  
Drone security is currently one of the major research issues in the industry and academia. Due to the widespread use of drones, problems related to security, privacy, regulation, and ownership are simultaneously rising. Numerous applications are highly critical in terms of security, failing to provide which may considerably harm society [54]. Due to the increasing development of e-commerce, high manpower is required to cover the last-mile delivery of products. In such cases, drones can have a huge potential for delivering the product in shorter time. Although drones have huge potential in e-commerce and logistics, when hacked, they can pose a huge threat to the people as well as to the society. For example, hackers might utilize radars to detect and capture sensitive information such as user and product information. The hackers may even modify the delivery address and send the package to another location or to their location. Therefore, certain security guidelines need to be ensured to guarantee secure, effective, and reliable package delivery using drones. To handle major security issues, blockchain-enabled techniques have been widely investigated in the present literature. Blockchain enables storing the previous blocks as hashes in an immutable ledger. All the stakeholders in the system have access to the ledger as a chain. In order to guarantee the consensus between the stakeholders, a new block is added to the existing chain and is copied to all the other nodes [55]. Thus, this structure cannot be manipulated by anonymous attackers. In this section we discuss several techniques to ensure security issues in drone-based delivery systems.
# Dynamic Bending Behaviour of Sandwich Structures for Marine Applications
## 3 Bending Properties
### Impact Bending Stiffness  
In an analysis of the impact bending, the facesheet, web (adhesive layers), and core were assumed to be perfectly bonded and, thus, considered as a single solid section [25]. It is noteworthy that in calculating the core's contribution to the overall bending stiffness of the structure, the contribution of the adhesive layers needs to be accounted for where applicable (GSH and GSV samples). This is because the alignment of the adhesive layers to the neutral axis determines the extent of their contribution to the core resistance tobending [26]. During impacts, the effect of the loads cumulatively increases until the ultimate strength of the material is reached. Therefore, the assessment of the impact loads at each stage and their corresponding deflection could provide a better prediction of the impact bending stiffness of the exposed and unexposed specimens and indicate the role of the core variants. The sequence of deformation in response to the multiple low-velocity impacts was idealised using a two-degree-of-freedom spring-mass model [27]. The expression for the bending stiffness with respect to impact loads is obtained as follows:  
\[K_{bs}\ =\ \frac{P_{\max}}{\delta}, \tag{6}\]  
where \(K_{bs}\), \(P_{\max}\), and \(\delta\) are the impact bending stiffness, maximum impact force, and the corresponding displacement, respectively.
## 3 Experimental Results and Discussion
### Stokes Lasers Generation
When the pumping laser reaches 120 mJ, the total Stokes energy reaches 44 mJ, corresponding to an optical efficiency of 36.7%. Figure 2: The calculated Stokes wavelengths (numerical value) and the detected spectra (figure) of the Stokes lasers at the pumping energy of 120 mJ. The calculated Stokes wavelengths of 1409.9 and 1598.8 nm are not detected in the experiments.
## 3 The Field Testing on Concrete Pavement
### The Field Measurement of SASW Method  
In this research, the SASW device is based on the NDE-360 platform manufactured by Olson Instruments, consisting of a dynamic signal analyzer with anti-aliasing filtering, several hammers, two receivers, and a cable. The SASW field testing is shown in Figure 2. Considering the thickness of the measured concrete pavement, the minimum distance between the two receivers was set at 0.15 m and then increased to 0.3 m, 0.6 m, and 0.9 m. Such multiples increase the receiver's distance in order to obtain a signal covering a different wavelength range, which can help generate a more robust and accurate dispersion curve. At each receiver distance, the ground will be struck four times with the excitation, and the average value will be calculated as the initial in situ data.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Pavement** & **Measurement Location** & **SASW Method** & **FWD Method** & **Core Taking** \\ \hline \multirow{4}{*}{Section I} & X1/X2/X3 & \(+\) & \(+\) & \(+\) \\  & Y1/Y2/Y3 & \(+\) & \(+\) & \(+\) \\  & Z1/Z2/Z3 & \(+\) & \(-\) & \(+\) \\ \cline{2-5}  & M & \(+\) & \(-\) & \(+\) \\  & L & \(+\) & \(-\) & \(+\) \\  & N & \(+\) & \(-\) & \(+\) \\ \hline \multirow{4}{*}{Section II} & AA & \(+\) & \(-\) & \(+\) \\  & BA & \(+\) & \(-\) & \(+\) \\ \cline{1-1}  & CA & \(+\) & \(-\) & \(+\) \\ \cline{1-1}  & DA & \(+\) & \(-\) & \(+\) \\ \hline \multirow{4}{*}{Section III} & JX1-JX10 & \(+\) & \(-\) & \(-\) \\ \cline{1-1}  & A & \(+\) & \(+\) & \(-\) \\ \cline{1-1}  & B & \(+\) & \(+\) & \(-\) \\ \cline{1-1}  & C & \(+\) & \(+\) & \(-\) \\ \cline{1-1}  & D & \(+\) & \(+\) & \(-\) \\ \cline{1-1}  & E & \(+\) & \(+\) & \(-\) \\ \hline \end{tabular}
\end{table}
Table 1: The description of NDT methods and coring tests on three concrete pavements (the “+” represents an experiment conducted, and the “\(-\)” symbol means testing is not conducted).  
Figure 2: The SASW field testing: (**a**) on Section I; (**b**) on Section III.  
In addition, the forward and reverse measurements were performed for each receiver array, which can eliminate the effects of any unexpected internal phase shifts related to the receiver or signal analyzer [35]. When the field Rayleigh wave signal was organized, the dispersion curve could be constructed, and then the shear wave velocity profile, which varies along the depth, could also be calculated. As the material properties of pavements are evaluated at minor shear strains (less than 0.001%) in the SASW method, the assumption of linear elastic behavior is practical in these materials [19]. Therefore, the elastic modulus of each layer would be determined using the shear wave velocity based on the linear elastic theory.
## 2 Development of Quadruped Robots
### System Description and Robot Model
#### 2.1.1 Forward Kinematics of Robot Legs  
The forward kinematics of robot legs was analyzed using Denavit-Hartenberg parameters (also called DH parameters) [14], which are used widely for describing robotic systems and other mechanical problems [15]. In the set of DH parameters, four parameters are defined as transformation parameters, namely, \(d_{i}\), \(\theta_{i}\), \(a_{i}\), and \(\alpha_{i}\). The notation of each of the parameter can be described as follows [16]:  
* \(d_{i}\) (joint displacement):length between the two joints.
* \(\theta_{i}\) (joint angle): angle measured between the orthogonal of the common normals. This parameter is variable for revolute joint while the other parameters remain constant.
* \(a_{i}\) (link length): mathematical link length (distance between common normals).
* \(\alpha_{i}\) (link twist): angle measured between the orthogonal of the joint axes. For a prismatic joint, the other parameters are fixed, but this parameter is variable.  
The top and side views of the geometrical model of the robot's leg are shown in Figure 3. Moreover, the link coordinate frame is illustrated herein. As aforementioned, each leg comprises three links, namely, link1, link2, and link3. Joint 1 rotates in the horizontal direction, and joints 2 and 3 move the leg upward and downward along the vertical direction. The joint angles \(\theta_{1}\), \(\theta_{2}\), and \(\theta_{3}\) are the angles of link1, link2, and link3, respectively. According to the parameters shown in Figure 3, we can define the DH parameters of the systems as summarized in Table 1.  
Position of the robot leg end-effector can be written as follows:  
\[x=a_{3}\cdot\cos(\theta_{1})\cos(\theta_{2}+\theta_{3})+a_{2}\cdot\cos(\theta _{1})\cos(\theta_{2})+a_{1}\cdot\cos(\theta_{1}), \tag{1}\]  
\[y=a_{3}\cdot\sin(\theta_{1})\cos(\theta_{2}+\theta_{3})+a_{2}\cdot\sin(\theta _{1})\cos(\theta_{2})+a_{1}\cdot\sin(\theta_{1}), \tag{2}\]  
\[z=a_{3}\cdot\sin(\theta_{2}+\theta_{3})+a_{2}\cdot\sin(\theta_{3}). \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Link** & \(\mathbf{d}_{i}\) & \(\mathbf{\theta_{i}}\) & \(\mathbf{a_{i}}\) & \(\mathbf{\alpha_{i}}\) \\ \hline
1 & 0 & \(\theta_{1}\) & \(a_{1}\) & 90 \\
2 & 0 & \(\theta_{2}\) & \(a_{2}\) & 0 \\
3 & 0 & \(\theta_{3}\) & \(a_{3}\) & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: DH parameter for robot legs.  
Figure 2: Quadruped robot model used in this study.
## 2 State of the Art--Underground Mining Localization Systems
The system is capable of obtaining in real-time the accurate location of miners within the tunnels of the underground mine. Through the RSSI, the location node takes advantage of its built-in location engine to calculate its coordinates and location. The maximum location estimation error is approximately 6 m.  
Whereas in [23], the authors proposed a new positioning algorithm for underground coal mines based on Zigbee technology, in which the nearest neighbor in signal space (NNSS) algorithm was adopted for the preliminary location and the dynamic triangular measurement (DTM) localization method was used for precise positioning. The algorithm provides personnel tracking, monitoring, in addition to other related functions. No results are displayed related to the location estimation error. In [24], software and hardware for the location system of underground mines personnel were designed. The system consists of a gateway node, reference node, location node, and the connection of the three devices to a monitor. The system is capable of transmitting the location of the personnel in real-time to the software graphical monitor, and achieve a real-time tracking function for the location node. An algorithm based on RSSI was used as the localization algorithm. The results show a minimum location estimation error of less than 3 m.  
As it has been shown, most of the underground localization systems use RF technologies to fulfill the purpose of localization in underground mining. As previously indicated, there are a number of problems with RF systems applied to indoor localization, which include: (i) long response time and low accuracy; (ii) traditional short range RF technologies operated in the 2.4 GHz band do not provide large coverage in underground environments [25; 26]; (iii) typical RF-based systems are not permitted in environments such as hospitals and airplanes, due to security reasons; (iv) technologies such as Wi-Fi, Zigbee, and RFID incur additional costs associated with the development and particular configuration when applied to large spaces; and (v) lower precision (typically between a few tens and several hundred of meters).
# Computer Vision Algorithms on a Raspberry Pi 4 for Automated Depalletizing
## 3 Methodology
### Pattern Matching  
Pattern matching is a common image-processing operation which detects and locates predetermined templates inside an image. With a special emphasis on the integration of template matching with non-maximum suppression, a widely used technique in this field, this Section provides some technical detail.  
Pattern matching involves comparing a predefined template against different regions of the target image. In this research, the implementation of pattern matching followed these key steps:  
1. **Template Generation**:Templates images are selected and augmented by applying rotations at various angles.
2. **Image Processing**: The system captured a grayscale image to simplify subsequent computations and facilitate robust feature extraction.
3. **Template Matching**: Image patches are compared to templates by using an appropriate similarity metric, discussed in the following.
4. **Non-maximum suppression**: Of a set of overlapping candidate regions, only the top scoring is retained.
5. **Result Visualization** Image-template similarity is computed by normalized cross-correlation (NCC). This is the Pearson correlation coefficient [37], used in its 2D form and computed as follows:  
\[R(x,y)=\frac{\sum_{i,j}(T(i,j)-\bar{T})(I(x+i,y+j)-\bar{I}_{x,y})}{\sqrt{\sum_ {i,j}(T(i,j)-\bar{T})^{2}\sum_{i,j}(I(x+i,y+j)-\bar{I}_{x,y})^{2}}}\]  
where \(T\) is the template, \(I\) is the image, \((x,y)\) is the origin of the image patch being compared, \(\bar{T}\) and \(\bar{I}_{x,y}\) denote mean pixel value in the template and in the image patch, respectively.  
Figure 4: Experimental methodology.  
The matchTemplate function from OpenCV [30] was employed, and a threshold was applied to filter out false positives.  
Non-maximum suppression (NMS) removes redundant bounding boxes, ensuring only the most relevant boxes are retained. Given a similarity score matrix \(R\) obtained after template matching, NMS retains the score at any pixel location that is a local maximum in its \(3\times 3\) neighborhood; otherwise, it suppresses it by setting it to 0.  
As a final step, the end result is then displayed, indicating the detected objects' centers and their corresponding identification numbers (Figure 5).