esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
These constitute a major drawback for integration of fiber optics systems. However, downsizing this measurement equipment seems feasible to some extent. A third class of transducers is contact-less. Air-coupled, contactless UT has been noted above [49; 50; 51]. Other contactless methods comprise, e.g., laser-based systems, such as laser interferometry [43], or vibrometers [63]. One disadvantage of contactless laser methods is that they often require a sufficient and fairly uniform surface reflectivity of the test object. This may sometimes be difficult to achieve in field testing, and in many cases a thin layer of paint has to be applied even under laboratory conditions. However, there are also advantages, as stated in [63] (cite): \"_Laser vibrometry used for fatigue crack detection has many advantages if compared with contact techniques. This approach not only eliminates frequency shifting due to additional mass of Transducers and avoids problems associated with local stiffening due to transducer surface-bonding but also eliminates possible measurement-related to nonlinear sources_\". Lasers are also capable of exciting elastic waves in materials [64]; hence, they can act as both emitter and sensor for GUW. A comparison between different SHM systems for composite structures has recently been published by [65], and the latest developments of NDT and SHM, for aerospace composites, are discussed in [66]. Depending on the type of transducer or sensor as well as test object and its service environment, the mounting device and couplant have to be appropriately chosen. Piezofiber composites such as AFC or MFC and piezopolymer films often are flexible to some extent and easily conform to curved surfaces without the need for a so-called waveguide [67]. AFC have been adhesively bonded on pipe surfaces with diameters of 60 mm [68] and 50 mm [69] without any waveguide. The flexibility of AFC and MFC is somewhat higher in the direction transverse to the fiber direction; hence, fibers were oriented along the pipe length in these applications on comparatively small diameter pipes. Another application of waveguides is in AE or GUW monitoring at high temperatures [70]. Waveguides are often made from metals (steel or Aluminum) and, hence, are fairly robust and durable. A disadvantage may be that they introduce two additional contact interfaces (one more than for direct coupling of sensors) yielding higher signal attenuation due to reflections. However, more importantly, waveguides act as a wave propagation filter by limiting or changing the types of waves that can propagate through them. The exact type of this AE wave signal filtering depends on the material, the shape and the size of the waveguide [65; 71]. For high-temperature applications, special sensor design with fiber optics [72] or development of piezo-materials with higher Curie temperature T\({}_{\text{C}}\)[36; 37] provide alternatives that yield lesser effects on the recorded signals than waveguides.
# Protecting Data at Risk of Unintentional Electromagnetic Emanation: TEMPEST Profiling
## 3 Sources of Electromagnetic Leakage  
Electronic devices and gadgets are getting smaller, lighter, faster, have longer battery life, and continue to increase computing power. Such devices require smaller electronic components, which reduce electromagnetic interference. A compact design, on the other hand, also means smaller distances between components, traces, and packages, leading to increased field interactions, current loops, ground loops, and other potential sources of EM leakage [45]. An electrical device characteristic that allows it to function properly in the presence of other devices without affecting them is called electromagnetic compatibility (EMC) [4]. The main focus of EMC is to analyze unwanted EM emissions, which are categorized into two groups: conductive coupling and radiative coupling. Conductive coupling requires physical support due to transmission interference within the system. Although the radiation is usually low, the circuits are often connected with long conductors or wires, which act as antennas [46]. Radiative coupling occurs when internal circuit components act as antennas and emit EM waves [47]. Radiation emissions usually originate from electronic circuits acting as electric dipoles produced by the voltage between signal conductors or as magnetic dipoles produced by a current flowing in a loop [46]. EM emissions from computer monitors, wireless keyboards and mice, printers, scanners, conductors, PES, and RFID devices are discussed below.
# Framework for Fast Experimental Testing of Autonomous Navigation Algorithms
## 4 Initial Framework Implementation
### Execution Phase
#### 4.2.1 Localization Module: AMCL  
In the basic configuration of our system, once we have a grid map obtained using GMapping (Figure 2), we need an algorithm to locate the robot inside it. We rely on the well-known AMCL. This algorithm is based on the method described in [40] and is part of the ROS navigation stack [24]. AMCL is based on the Monte Carlo approximation applied sequentially, which in the literature is called a particle filter. This method uses the sensor model described in the section beam models of range finders in [40]. It also uses the action model described in the odometry motion model section in [40]. In order to adjust the AMCL parameters, we must follow the processes described in the given references. In Section 6.2 we show the parameters obtained through these processes. This module  
Figure 3: ROS nodes scheme that shows the system architecture in the execution phase for our first framework implementation. Notice that each node is placed at its corresponding abstraction level. The execution is now online because it is a closed loop application that acts on the environment through the control node. For both localization and planning, we will use the files generated in the previous phase shown in Figure 2.  
accepts as inputs the odometry of the robot, and laser scans from a light detection and ranging (LiDAR) sensor, in our case a Velodyne VLP-16. These inputs are standard ROS messages, which eases the process of using different LiDAR sensors or even robots with different odometers. The output of this module is also a standard ROS pose message containing pose and covariance information. These covariances will play an important role in the new localization module described in Section 5, where the AMCL estimation will be fused with GNSS information by using a Kalman filter. Thanks to this structure of inputs and outputs we can replace this localization node without affecting the rest of the architecture.
# Road Extraction Based on Improved Convolutional Neural Networks with Satellite Images
## 2 Methods
### Architecture of Semantic Segmentation Network  
Here, the improved CNN with D-Dense block construction is named D-DenseNet (Figure 7). We introduce D-Block, the center part of D-LinkNet, into our improved network. Further, the proposed network uses a DenseNet backbone and took stem as the initial block.  
Figure 6: Relation between initial and stem blocks: (**a**) the initial block; (**b**) the stem block.  
Figure 7: The proposed network construction with the D-Dense block.  
The symbol \(\begin{bmatrix}k\times k,s\\ m,n\end{bmatrix}\) in Figure 7 indicates that convolution kernel size is \(k\times k\), stride is \(s\), the input channel is \(m\) and the output channel is \(n\). The _Conv_ in Figure 7 indicates BN-ReLU--Conv except the final convolution layer. In addition, _Deconv_ is BN-ReLU-Transposed-Conv.  
D-Block contains four steps (Figure 8): the dilated convolution of 2 cascade modes and 2 parallel modes. Meanwhile, different dilated convolutions have different expanding rates. Further, each path presents different receptive fields, and the multi-scale context information can be obtained.
## Appendix A
### Derivation Procedure of PD Material Parameters
\begin{array}{ll}W_{(k)}&=a\theta_{(k)}^{2}+b\sum\limits_{j=1}^{\infty}\delta|x _{(j)}-x_{(k)}|s_{(j)(j)}^{2}V_{(j)}+b_{F}\sum\limits_{j=1}^{l}\delta|x_{(j)}-x _{(k)}|s_{(k)(j)}^{2}V_{(j)}+b_{Z}\sum\limits_{j=1}^{l}\delta|x_{(j)}-x_{(k)}|s _{(k)(j)}^{2}V_{(j)}\\ &=a(2\zeta)^{2}+b\int\limits_{H}\delta\xi\zeta^{2}dH+b_{F}\sum\limits_{j=1}^{l }\delta|x_{(j)}-x_{(k)}|\zeta^{2}V_{(j)}b_{Z}\sum\limits_{j=1}^{l}\delta|x_{(j )}-x_{(k)}|\zeta^{2}V_{(j)}\\ &=4a\zeta^{2}+b\int_{0}^{s}\int_{0}^{2\pi}\delta\xi\zeta^{2}\xi d\theta\xi+ \xi^{2}b_{F}\delta\sum\limits_{j=1}^{l}|x_{(j)}-x_{(k)}|V_{(j)}\xi^{2}b_{Z} \delta\sum\limits_{j=1}^{l}|x_{(j)}-x_{(k)}|V_{(j)}\\ &=4a\zeta^{2}+\frac{2}{3}\pi kb^{4}\zeta^{2}+\zeta^{2}b_{F}\delta\sum\limits_ {j=1}^{l}|x_{(j)}-x_{(k)}|V_{(j)}+\zeta^{2}b_{Z}\delta\sum\limits_{j=1}^{l}|x_ {(j)}-x_{(k)}|V_{(j)}=\frac{1}{2}Q_{11}+2Q_{13}+Q_{23})\zeta^{2}\end{array} . \tag{100}\]  
Comparing Equation (100) with (101) and (102),  
\[d=\frac{2}{\pi\delta^{3}}, \tag{101}\]  
\[4a+b_{F}\delta\sum\limits_{j=1}^{l}\Big{|}x_{(j)}-x_{(k)}\Big{|}V_{(j)}+b_{Z} \delta\sum\limits_{j=1}^{l}\Big{|}x_{(j)}-x_{(k)}\Big{|}V_{(j)}=\frac{1}{2}(Q _{11}+2Q_{13}+Q_{33}-8Q_{55}). \tag{102}\]  
Solving Equations (100), (101), and (102),  
\[a=\frac{1}{2}(Q_{13}-Q_{55}), \tag{103}\]  
\[b_{F}=\frac{Q_{11}-Q_{13}-2Q_{55}}{2\delta\sum\limits_{j=1}^{l}\Big{|}x_{(j)} -x_{(k)}\Big{|}V_{(j)}},\;b_{Z}=\frac{Q_{33}-Q_{13}-2Q_{55}}{2\delta\sum\limits _{j=1}^{l}\Big{|}x_{(j)}-x_{(k)}\Big{|}V_{(j)}}. \tag{104}\]
# Diversity in Selected Grain Mineral and Protein among Pigeonpea Landraces
## 1 Introduction  
Pigeonpea (_Cajanus cajan_) is a drought-tolerant food legume which is cultivated widely in tropical countries. The grain of pigenopea sustains the livelihoods of rural communities as a source of affordable protein in many parts of Africa and Asia [1, 2]. Moreover, it plays a vital role in food security and income generation through trading in both formal and informal markets. In southern Africa, it is often consumed as fresh green bean or boiled dry grain [3]. The main pigenopea-producing countries in southern Africa include Malawi, Mozambique and Tanzania. In South Africa, it is cultivated as a minor crop largely in the eastern coastal region of the country.  
Pigeonpea grain contains abundant levels of carbohydrates, minerals and proteins [2]. It possesses about 20-26% protein, 65% carbohydrates and 2% fats [4, 5]. It can be used as a supplement to cereal-based diets that are deficient in protein, vitamin B and beta-carotenes [6]. The range of minerals in the grain of improved pigeonpea genotypes includes calcium (Ca) (130.00 mg/100 g), potassium (K) (1329.00 mg/100 g), iron (Fe) (5.23 mg/100 g) and zinc (Zn) (2.76 mg/100 g) as well as thiamine (0.64 mg/100 g) and niacin (2.96 mg/100 g) [7]. Nedicinally, it is used to treat measles, hepatitis, diabetes and liver dysfunction [8, 9]. In addition, pigeonpea possesses considerable amounts of natural antimicrobial compounds such as tannins, flavonoids and alkaloids and a broad spectrum of phytochemical, anticarcinogenic, anti-inflammatory and antidiabetic properties [10, 11, 12, 13].  
Grain extracts appeared to reduce red blood cell sickling, suggesting the potential to benefit people with sickle cell anaemia [13; 14].  
In many parts of Africa where poor rural communities typically depend on starch-based diets leading to nutrient deficiencies such as anaemia, hypocalcaemia as well as kwashiorkor, pigeopnea is an affordable source of good-quality protein [15; 16]. Biofortification and dietary diversification with nutrient-rich legumes such as pigeonpea can reduce nutrient deficiencies. However, previous research efforts placed little emphasis on the nutrient profiles of the pigeopnea [17; 18; 19]. Likely, this was due to inadequate funding. Therefore, the objective of this study was to determine the diversity in grain mineral and protein content among pigeonpea landraces that are available in the pool maintained at the University of Venda with a view to exploit their potential in human diet in future.
###### Abstract  
There is an urgent need for clean recycling strategies to address the increase in food waste (FW) and the harvesting of microalgae (MA). In this study, biogas production potential and operational stability were evaluated by testing combinations of FW and MA mixed at five different ratios. Co-digestion of FW and MA improved substrate biodegradability, achieving a decomposition rate of 0.45/d (FW/MA = 1:1), which is 1.25 to 1.55 times higher than that of MA or FW alone. Co-digestion of FW and MA resulted in a synergistic effect, improving biogas yield by 2.04-26.86%. Four mathematical models were applied to estimate biological degradation and biogas production kinetics, and the Cone model performed better than the other models in terms of reliability and accuracy. The abundance of Bacteroidetes, _Frimicutes_, and _Synergistetes_ peaked at FW/MA = 1:1. At the same ratio, the genera _Methanospirillum_, _Methanocarppuscularum_, and _Methanomethyl hydrocarbons_ were also found to have increased in abundance. The optimal ratio was found to be 1:1 for co-digestion of FW and MA, which is a feasible approach for simultaneous bioenergy production and biomass waste co-disposal.  
food waste; microalgae; anaerobic co-digestion; microbial community Article
## 7 Experimental Results
### Realtime Implementation
#### 7.1.2. Evacuation Path  
This section describes the building evacuation decision-making model implemented under the 2005 Indian National Building Standards Act and the 2005 Fire Safety Act. This decision model is based on the [PERSON] algorithm [51], which is an improved algorithm that is applied for the shortest route in a building (Figure 25). This approach assists in finding the shortest path between the specified source node and all other nodes in the graph. It can also be used to find the shortest path from a single node to a single target node. The process will stop as soon as the shortest path to the target node is established. [PERSON]'s algorithm uses fully ordered labels that are positive integers or real numbers. The steps of [PERSON]'s algorithm to find the shortest path from a single source node on the graph to all other nodes are show below. Steps of [PERSON]'s Algorithm to Find the Shortest Path. 1. Make a set called spStet (shortest path tree set) to keep track of the vertices in the shortest-path tree, i.e., those whose minimal distance from the source is calculated and completed. This set is initially empty. 2. Assign a distance value to each of the input graph's vertices. All distance values should be set to INFINITE. Assign the source vertex a distance value of 0 to ensure that it is chosen first. 3. Because spStet does not contain all vertices. 1. Choose a vertex u that is not in spStet and has a minimum distance value. 2. Add u to the spStet. 3. Update the distance between all of u's adjacent vertices. Iterate through all nearby vertices to update the distance values. If the total distance value of u (from source) and the weight of edge u-v is less than the distance value of v for each neighboring vertex v, then update the distance value of v.  
The equivalent diagram of a building is also shown in Figure 26. Here, the rooms are replaced with bubbles, the stairs are replaced with jagged lines, and the straight lines are replaced with straight lines. Each entity acts as a node called {E0, E1,..., E19}. In this case, {E0 and E19} are the target nodes, {E1, E2,..., E18} are the source nodes. Equivalent lengths of canals are not the same, due to different factors in the ease of evacuation routes under different road traffic conditions. As the equivalent distance increases, the weight of the route in the weighted graph increases. In the absence of a fire hazard, after using the improved Dijkstra algorithm the shortest path to exit E0 from relay node E18 is {E18, E12, E1, E0}. The middle node is E1, E12, the  
Figure 25. 3D diagram of Hostel. length is 14 m. The shortest path to leave E19 from transit node E18 is {E18, E17, E16, E15, E14, E13, E7, E6, E19} and the intermediate nodes are E6, E7, E13, E14, E15, E16, E17, the length is 51.5 m. In this case, since the distance to the destination node, E0 from node E18 is relatively less, it has to choose output E0 as its output, and the final path output is {E18, E12, E1, E0}. Based on this analysis, the shortest paths from each source node to the destination node are shown as shown in Table 2.
## 3 Methodology
### Satellite Image Processing
#### 3.1.4 Log Ratio Algorithm for Satellite Derived Relative Depth  
The method to derive the relative radiance for the satellite data was based on the methodology adopted by [PERSON] and [PERSON] [21]. The Relative Water Depth tool enabled in ENVITM 5 suite was used to extract the log ratio between the green band (520-590 nm) and the blue band (440-510 nm). The algorithm uses a ratio of observed reflectance and two constants to calculate satellite derived relative depth (SDRD), as follows:\[SDRD=m_{1}\frac{ln\big{(}nR_{w}(\lambda_{i})\big{)}}{ln\ \Big{(}nR_{w}(\lambda_{j})\Big{)}}\ -\ m_{0} \tag{1}\]  
R\({}_{w}\) is the observed reflectance of the wavelength (\(\hat{\oplus}\) for bands \(i\) and \(j\), the ratio of which forms the basis for depth extraction. The blue (\(\lambda_{i}\)) and green (\(\lambda_{j}\)) bands were used for the ratio input as they have the greatest penetration though the water column. The constant, \(n\), is a fixed value, chosen to keep the ratio positive given any reflectance value input. The output must still be calibrated to field data to estimate actual depth. m\({}_{1}\) is a tunable constant to scale the ratio to depth, while m\({}_{0}\) is the offset correction when the output should be zero (_i.e._, SDRD = 0), used most recently by [PERSON] _et al._, [51] and [PERSON] [52] for Indian Ocean and Antarctic Ocean coastal surveys. The application of this band ratio algorithm has led to improvements in the water depth empirical models, particularly dealing with seafloor reflectance issues or water column issues [53]. However, limitations in the implementation are still hampered by its complexity, particularly on the physics of light transmission within turbid waters [17,54] and in biota covered bottoms as details in [PERSON] _et al._, [30]. The output SDRD data was exported to vector point data using the pixel centroid as the geo-reference locator.
# A Network Device Identification Method Based on Packet Temporal Features and Machine Learning
## 2 Data Analysis and Feature Engineering  
### Data Collection and Feature Extraction  
The collection of data within this method mainly utilizes passive probing to collect network data from the network environment, usually by setting up a traffic collection device in the network environment to listen to the communication behavior of the device, and then labeling the raw traffic with the device type. To ensure the independence of the training data and test data, the data are usually divided into the training set and test set from the source.  
Network devices use different protocols at different stages of their operation, such as ARP, SSL, LLC, HTTP, etc. The list of protocols used by a network device is a good static characterization of the device's behavior, so we extracted protocol information such as the data link layer, network layer, transport layer, etc., from the packets. In addition, the dynamic characteristics of the net device are also important features for the identifiability of the device. Here, the dynamic characteristics focused on three indicators: payload entropy, TCP payload length, and TCP window size. The payload entropy indicates the content of  
Figure 1: The overall process framework of the methodology.  
the message. To calculate the Shannon entropy of a sequence of m-bytes with a symbol length of 8 bits or 1 byte, the following formula can be used:  
\[h_{m}=-\sum_{i=1}^{256}p_{i}\ \log_{256}\ p_{i} \tag{1}\]  
where \(p_{i}\) is the probability of the occurrence of byte value \(i\) in the \(m\) bytes, i.e., \(p_{i}=\frac{counter(i)}{m}\). [PERSON] et al. (2013) concluded that the entropy of the payload is small if the packet carries plaintext, and will be high if the packet carries audio data. The TCP payload length indicates the length of the message sent by the given device and is a device-specific characteristic. The TCP window size depends on the memory and processing speed of the device and was used by [PERSON] et al. (2014) in a fingerprinting study of devices. Additionally, we extracted the source and destination ports and classified them, as shown in Table 1. Finally, we extracted 96 features from the network packets to characterize the communication behavior of the device.
## 3 Results and Data Analysis  
### Calculation Formula for Wind Pressure Coefficient  
The wind pressure coefficient \(C_{p}\) on the surface of the solar greenhouse is defined in Equation (6):  
\[C_{p}=\frac{2(p_{i}-p_{0})}{\rho v_{b}^{2}} \tag{6}\]  
where \(p_{i}\) is the pressure value on the greenhouse surface; \(p_{0}\) expresses the static pressure; \(\rho\) is air density; and \(v_{b}\) is the average wind speed at a height of 10 m, and is taken as 25 m\(\cdot\)s\({}^{-1}\).
# Gaze-Dependent Image Re-Ranking Technique for Enhancing Content-Based Image Retrieval
## 4 Experiment
### Evaluation Metrics  
To accurately evaluate the proposed method, we adopt two extensively used evaluation metrics, namely Recall@k and NDCG@\(k\). Specifically, we set \(k\) to 1, 5, 10, 50, and 100 to measure the performance at different retrieval depths. Higher values of Recall@k and NDCG@k indicate the superior performance of the method. Recall@\(k\) is an extensively used evaluation metric for machine learning and information retrieval. It quantifies the capacity of a model to recognize all pertinent instances in a given dataset. The definition of Recall@k is shown as follows:  
\[\text{Recall@}k=\frac{n_{\text{q}}}{k}, \tag{9}\]  
where \(n_{\text{q}}\) represents the number of desired images that appeared in the top \(k\) retrieval results. The desired images refer to the retrieval results obtained using the human-generated caption corresponding to the query images in our experiment. NDCG@\(k\) (normalized discounted cumulative gain) is also an evaluation metric commonly used in image retrieval to measure the effectiveness of a ranked list of items. It measures the quality of the recommendations by assigning a score between 0 and 1 based on how well the recommended items match the user's preferences. NDCG considers each recommended item's relevance and position in the list of recommendations [57]. The relevance score of each item is usually determined by the user's feedback (e.g., ratings, clicks, purchases) on the items. NDCG is calculated by taking the discounted cumulative gain (DCG) of the ranked list and normalizing it by the ideal DCG(IDCG). Specifically, NDCG@\(k\) is defined as follows:  
\[\text{NDCG@}k=\frac{\text{DCG@}k}{\text{IDCG@}k}, \tag{10}\]  
where DCG is calculated by summing the relevance scores of the ranked items, discounted by their position in the list. IDCG is the maximum possible DCG for the list, calculated by assuming that the items are ranked in order of decreasing relevance. Specifically, DCG and IDCG are defined as follows:  
\[\text{DCG@}k=\sum_{i=1}^{k}\frac{2^{rel_{i}}-1}{\log_{2}(i+1)}, \tag{11}\]  
\[\text{IDCG@}k=\sum_{i=1}^{\min(k,|R|)}\frac{2^{rel_{i}}-1}{\log_{2}(i+1)}, \tag{12}\]  
where \(rel_{i}\) denotes the relevance score of \(i\)th item in the desired image list.