esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Satellite Interferometry for Landslide Detection in Italy
## 6 How InSAR Products Have Been Used for Landslide Studies in Italy
### Landslide Inventory Update and the State of Activity Definition  
Thanks to the wide-area coverage, InSAR is the perfect tool to update and modify the boundaries and the state of activity of known landslides. Considering the impact of these phenomena in the Italian Peninsula, regional and national catalogs already exist; the IFFI database is an example [2]. However, a constant update is needed to improve risk management activities. The update of landslide inventories is a demanding task, especially where databases of events or high-resolution optical images are lacking. For this reason, InSAR was chosen by some authors as a support for landslide inventory updates and, most of all, for the definition of landslides' state of activity.  
One of the first examples comes from the Arno River Basin (Tuscany region), the testing ground of the SLAM (Service for LAndslides Monitoring) project, funded by the European Space Agency [32; 33]. In the following years, many applications were proposed in different Italian regions, such as Molise [103], Calabria [131; 133], Sicily [51; 163], Tuscany [164], Emilia Romagna [165], and Lombardia [166], and at the basin scale, as in the case of the Liri-Garigliano and Vulturno basins [112; 167].  
The update of landslide inventories is a complex activity that requires both ancillary information, i.e., geomorphological maps, orthophotos or preexisting inventories, and expertise in the field. This activity cannot be automatized, and it always requires the support of InSAR experts. All the intrinsic uncertainties of the interferometric technique and the distribution of measurement points within a landslide area must be taken into account. Since InSAR is an opportunistic technique, i.e., able to derive information only from well-defined objects (the PS/DS), the update of landslide inventories will be more precise in urban or peri-urban areas. The expert judgment is relevant, especially where the point density is low, and where the support of ancillary information is required.  
Landslide activity matrices are a useful tool to estimate the state of activity of landslides. The activity matrix approach exploits multi-temporal InSAR datasets as indicators of activity and intensity of landslide processes [126]. Different versions of activity matrices were developed in recent years [130; 131; 132; 133; 126; 130; 133]. All these approaches are aimed to the evaluation of the temporal evolution of landslides, highlighting reactivation and stabilization cycles.
# Numerical Study of the Toughness of Complex Metal Matrix Composite Topologies
## 2 Topological Structures of Reinforcement  
Three topological reinforcement structures are studied, namely a gyroid, an octet-truss and an FBCCZ. The gyroid structure (Figure 1a), discovered by [PERSON] [19] in 1970 and initially meets in nature (mitochondria, butterfly wings, etc.), has a topology similar to open-celled foam. This shape is a triply periodic minimal surface with mean curvature described by the formula presented in (1).  
\[\sin\left(\frac{2\pi}{L}x\right)\cos\left(\frac{2\pi}{L}y\right)+\sin\left( \frac{2\pi}{L}y\right)\cos\left(\frac{2\pi}{L}z\right)+\sin\left(\frac{2\pi}{ L}z\right)\cos\left(\frac{2\pi}{L}x\right)=0 \tag{1}\]  
This formula is expressed with the cartesian coordinates \((x,y,z)\), the cell length \(L\). The gyroid has a complete shape and represents 50% of the volume fraction of a cube in which it is enclose in [20].  
[PERSON] et al. [8] showed in their study that the gyroid can be used as scaffold for human bones thanks to its good compressive and torsional strengths and stiffnesses. [PERSON] et al. [21] performed a study on 3D graphene gyroids with varying cell size and wall thickness. They concluded that a gyroid with a high density gives a better mechanical performance of the assembly, especially a high strength.  
The octet-truss (Figure 1b) and the FBCCZ (Figure 1c) are micro-lattices. They are composed of trusses. The octet-truss has a Face-Centered Cubic (FCC) configuration. A unit cell of octet-truss corresponds to eight identical tetrahedron elements. The octet-truss has very good mechanical performance compared to other reinforcement structures [22], especially metallic foam [23]. It has higher crack resistance for a high relative density and large cell size [24].  
The FBCCZ (Face- and Body-Centered Cubic with Z-truss) is composed of 28 trusses including Z-truss. This lattice structure has higher strength and stiffness thanks to Z-trusses. It has a high compressive strength [25; 26], a high-impact resistance [26] and a good energy absorption [25]. As the gyroid, the FBCCZ lattice can be used as titanium-based alloy scaffold in human body thanks to a good biocompatibility and a good corrosion behavior [9]. [PERSON] et al. [26] showed that the FBCCZ lattice performs better than the gyroid regarding the compressive strength despite the same stiffness in all loading directions of the gyroid.
## 3 Space Time Block Code (STBC)  
STBC spreads data symbols across multiple transmit antennas and transmits them over a specific duration. The STBC rate (\(R\)) is determined by the ratio of the number of  
Figure 1: IRS-assisted MISO System Model.  
Figure 2: Fully Utilized STBC transceiver with IRS.  
modulated symbols manipulated by the encoder (\(K\)) and the number of transmission time slots (\(T_{s}\)) required to transmit these symbols from multiple antennas [34].  
\[R=\frac{K}{T_{s}} \tag{1}\]  
Consider a scenario where the signal constellation contains \(2^{m}\) points. During each encoding operation, a block of \(Km\) information bits is mapped onto the signal constellation to select \(K\) modulated symbols, represented as \(x_{1}x_{2}\),...\(x_{\rm k}\). In this situation, each group of \(m\) bits corresponds to the selection of a specific signal from the constellation [35]. These k modulated symbols are subsequently encoded using a space-time block encoder, resulting in the generation of parallel signal sequences. The \(N_{t}\) transmitting antennas simultaneously transmit these sequences over \(T_{s}\) discrete time slots. In [36], it is mathematically proven that complex OSTBC with full rate did not exist for more than two transmission antennas. So, the presented work will be inconsistent with this constraint.
# Using Modeling to Select the Type of Microwave Field Emitter for Dense-Layer Grain Dryers
## 1 Introduction
The experimental studies of the grain drying and disinfection processes [25,26] have shown that there is substantial nonuniformity in the electromagnetic microwave field distribution over the grain layer, reducing the efficiency of grain processing. Therefore, there is a need for the development of new design solutions for microwave field introduction into dense grain layers. The application of rectangular and semicircular waveguides with slot radiators is also regarded as an alternative to horn-type antennas. The use of microwave radiation in grain-processing technology (drying, disinfection, and pre-seeding treatment) has its fundamental peculiarities. These installations cannot be compared to microwave ovens. Each design of microwave grain processing installations typically uses its own radiation-emitting configurations. In the construction of such installations, it is necessary to consider not only the characteristics of the microwave field distribution but also the peculiarities of drying agent delivery to the grain processing zone. For instance, the air can first cool the power blocks and magnetrons before being supplied to the grain layer. In this case, the design of the emitters is of great importance. The choice of emitter configurations can  
Figure 1: Outlay of convective-microwave zone for grain processing: (1) casing of convective-microwave unit; (2) waveguides; (3) magnetrons; and (4) waveguide output gate. significantly alter and complicate the construction of the entire installation. This paper considers waveguide configurations that are better suited for convective-microwave grain processing installations with a dense layer. They do not have voids unfilled with grains. The application of the considered waveguide configurations entails specific features and designs of air ducts that deliver hot air to the grain layer in each case. Therefore, the proposed waveguide options are intended for convective-microwave grain-drying installations. Mathematical simulations for the microwave field distribution in the output of such waveguides have been carried out with the use of CST Microwave Studio software [27; 28] in order to compare the efficiency of such design types.
# Tamper and Clone-Resistant Authentication Scheme for Medical Image Systems
## 5 Watermarking Analysis and Security Evaluation
### Security Analysis of the Proposed Protocols
#### 5.1.1 Adversary Model  
The adversary' assumptions are as follows [37]:  
* \(\Psi\) can run any medical device with an integrated SUC.
* \(\Psi\) can listen to the transmitted and exchanged messages between the TA server and the medical devices.
* \(\Psi\) can exchange messages with the medical devices and the TA server.  
Such an adversary model can be used in the security evaluation of the proposed protocols. The adversary tries to take advantage of vulnerabilities and drawbacks of the proposed watermarking system. In the following, two attack scenarios are defined and analyzed based on the proposed adversary model: First, Man-in-the-middle Attack (MIM), and second, tampering or faking a medical device with an integrated SUC.
# Reconstruction of Missing Data in Weather Radar Image Sequences Using Deep Neuron Networks
## 3 Data and Experimental Design
### Evaluation Metrics  
We evaluated the performances of all models with the mean absolute error (MAE), critical success index (CSI), and Heidke skill score (HSS) [51]. Mathematically, the CSI score was computed as  
\[\text{CSI}=\frac{\text{hits}}{\text{hits}+\text{misses}+\text{false alarms}}, \tag{4}\]  
where hits correspond to true positives, misses correspond to false positives, and false alarms correspond to false negatives in the confusion matrix. The critical threshold was chosen to be 30 dBZ (CSI-30) or 40 dBZ (CSI-40). A higher CSI score denotes a better prediction with respect to a reflectivity factor larger than the threshold. The HSS score was computed as  
\[\text{HSS}=\frac{\frac{1}{N}\sum_{j=1}^{K}n\big{(}F_{j},O_{j}\big{)}-\frac{1}{N ^{2}}\sum_{j=1}^{K}N\big{(}F_{j}\big{)}N\big{(}O_{j}\big{)}}{1-\frac{1}{N^{2}} \sum_{j=1}^{K}N\big{(}F_{j}\big{)}N\big{(}O_{j}\big{)}}, \tag{5}\]  
where \(N\) is the number of predictions (i.e., pixels in a predicted image), \(K\) denotes forecasts categories, \(n\big{(}F_{j},O_{j}\big{)}\) denotes the number of forecasts and observations both in category \(j\) and \(N\big{(}F_{j}\big{)}\) and \(N\big{(}O_{j}\big{)}\) denote the total number of forecasts and observations in category \(j\), respectively. The predictions are classified into 5 classes according to the intervals (0, 20), (20, 30), (30, 40), (40, 50), and (50, 80). The HSS score reflects the accuracy of a prediction relative to that obtained by random chance. A higher HSS score denotes a better model.
# Detecting and Solving Tube Entanglement in Bin Picking Operations
###### Abstract  
Manufacturing and production industries are increasingly turning to robots to carry out repetitive picking operations in an efficient manner. This paper focuses on tackling the novel challenge of automating the bin picking process for entangled objects, for which there is very little research. The chosen case study are sets of freely curved tubes, which are prone to occlusions and entanglement. The proposed algorithm builds a representation of the tubes as an ordered list of cylinders and joints using a point cloud acquired by a 3D scanner. This representation enables the detection of occlusions in the tubes. The solution also performs grasp planning and motion planning, by evaluating post-grasp trajectories via simulation using Gazebo and the ODE physics engine. A force/torque sensor is used to determine how many items were picked by a robot gripper and in which direction it should rotate to solve cases of entanglement. Real-life experiments with sets of PVC tubes and rubber radiator hoses showed that the robot was able to pick a single tube on the first try with success rates of 99% and 93%, respectively. This study indicates that using simulation for motion planning is a promising solution to deal with entangled objects.  
bin picking; force sensing; grasp planning; industrial robots; motion planning simulation; 3D perception +
Footnote †: journal: Applied sciences
# Amharic _Adhoc_ Information Retrieval System Based on Morphological Features
## 4 Amharic IR System Design and Text Representation
### Word Embedding  
Similar to other languages, in Amharic language semantically related words co-occur many times in a text document. User information need might be incomplete or contain all co-occurrence words. As a result of this, relevant documents could not be retrieved. Therefore, user information need should be completed by query expansion in order to retrieve more relevant documents. To overcome the problem of vocabulary mismatch between index term and query term, we used word embedding vector representation for each word in the vocabulary. Each query terms are expanded by semantically and syntactically related terms using word2 vec developed by [PERSON] et al. [54]. To verify the impact of query expansion on Amharic IR using word embedding algorithms, we created CBOW and Skip-gram models on stem-based and root-based corpora. CBOW and skip-gram models capture order relationships between words and they are effective for NLP tasks involving the use of word similarity and word analogy. We used them to capture semantic relations between words in queries and relevant documents. Four neural network models: stem-based with CBOW, stem-based with Skip-gram, root-based with CBOW and root-based with Skip-gram models are created offline and used for expanding stem-based and root-based query terms using stem-based and root-based corpora, respectively. The original query terms are matched with vocabulary words in the models to find their vector values. Then, the similarity between the features of each original query word and vocabulary words is computed to identify similar terms for each query term. The similarity _sim_ between a query term \(q\) and a corpus word \(d\) is computed using cosine similarity as shown in Equation (9).  
\[sim(q,d)=\frac{\sum_{i}qi.di}{\sqrt{\sum_{i}qi^{2\sum_{i}di^{2}}}} \tag{9}\]  
where \(qi\) is vector representation of the \(i\)th query term and \(di\) is the vector representation of the \(i\)th word in the corpus. The top 5 most related terms are used to expand query terms. Nearest neighbors or syntactically related words are used to expand query. Words that are used in similar contexts with respect to a given word frequently are used as expanded terms.
# Aerostat-Based Observation of Space Objects in the Stratosphere
## 4 Observation Ability Analysis
### Observation Ability in Infrared Band  
#### 4.2.1 Observation Ability Analysis  
As described above, the intensity of the daytime sky background of the infrared band in the stratosphere is one mag darker than that of the visible band. It is favorable for the daytime observation of space debris and NEAs using SOFOS. When space debris is around the sun or NEA, coming from the orientation of the sun, the phase angle of the object is too large to detect in the visible band, but there is a chance to detect it in the infrared band: the NEA could be regarded as a stable thermal object and could be detected in the infrared band by means of the SOFOS in a long observation period during the daytime. However, the infrared characteristics of space debris change with the phase angle, so the detection opportunity during the daytime should be selected carefully.  
Taking the same telescope with the 500 mm aperture mentioned above, a near-infrared detector of 900 nm\(\sim\)1700 nm bands is used to observe space objects in the stratosphere. The detector has a pixel size of 20 \(\upmu\)m with a resolution of 640 \(\times\) 512 and an average quantum efficiency of 80%. A short exposure time should be set to avoid saturation in the daytime due to the sky background and the well capacity. Setting the daytime sky background of 7.5 mag and the SNR of five as calculating conditions, the infrared detection ability is about 7.0 mag at the exposure time of 20 ms, as shown in Figure 9. If the optical design of the lens is changed to increase the focal length and reduce the FOV from 1\({}^{\circ}\) to 0.5\({}^{\circ}\), then the detection ability can reach 7.7 mag at the same exposure time.  
It should be pointed out that this is the detection ability of the single frame with a short exposure time. In order to increase the observation ability, multiple frames can be obtained in a short time continuously to obtain a higher SNR as follows:  
\[SNR=\frac{MS_{\text{obj}}}{\sqrt{MS_{\text{obj}}+MS_{\text{sky}}}}=\sqrt{M} \frac{S_{\text{obj}}}{\sqrt{S_{\text{obj}}+S_{\text{sky}}}} \tag{2}\]  
where \(M\) is the number of frames obtained by the telescope, \(S_{obj}\) is the object flux, and \(S_{\text{sky}}\) is the sky background flux.  
This means that, with \(M\) frames co-added, the \(SNR\) is \(\sqrt{M}\) times than that of the single frame, which corresponds to an enhancement of 2.5 IgM mag for the observation ability. Generally, the image is within a second, and no further shifting/rotating procedure is needed. As an example, when 10 frames are taken within a second, the ultimate observation ability can be enhanced by 2.5 mag to about 10.2 mag in the infrared band after the superposition of these images.  
Figure 9: The detection ability of telescopes with different focal lengths in the infrared band.
# Integrating IP Mobility Management Protocols and MANET: A Survey
## 3 MANET Routing Protocols
### Proactive MANET Routing  
Proactive routing schemes build routing entries for each existing node in the network in advance of their usage. Each MANET node maintains its routing table by exchanging routing information with other nodes. When a node wants to communicate with another node, it immediately consults  
Figure 7: Taxonomy of Mobile ad hoc Network (MANET) routing protocols.  
its own routing table to get a route towards the destination. This is possible since each node maintains a complete knowledge of the network. Examples of proactive routing protocols are Destination-Sequenced Distance-Vector (DSDV) [30], Optimized Link State Routing (OLSR) [31], Wireless Routing Protocol (WRP) [32], BATMAN [33], and Source Tree Routing (STAR) [34].  
Destination-Sequenced Distance-Vector (DSDV)This is a table-driven protocol based on the distributed Bellman-Ford algorithm. In a table-driven routing, each node maintains the next hop node on the shortest path to each existing node in the network. A DSDV node stores a number of hops, the next hop node, and the most current sequence number for each entry in its routing table. An increasing sequence number is used to distinguish the stale routing entries from the new ones and to avoid loops. In order to maintain up-to-date routing tables, DSDV applies time-driven and event-driven update mechanisms. In a time-driven update, each node sends its routing table frequently to its immediate neighbors. The sent routing table is then forwarded throughout the network. In the event-driven update, a node sends its routing table whenever a significant change has taken place in its table since the last transmitted update.
# PrECast: An Efficient Crypto-Free Solution for Broadcast-Based Attacks in IPv4 Networks
## 6 Performance Analysis of PrECast
### Communication Message Complexity  
Communication message complexity in our context determines the amount of network traffic due to the traditional ARP scheme and the proposed PrECast.  
As we mentioned before, ARP broadcast is applicable only to a single IP broadcast domain. We assume the underlying network topology in which PrECast is deployed follows Cisco's recommended three-tier architecture. We assume that there are \(n\) active hosts in the network. These include PCs, mobile hosts, printers, fax machines, etc. Our LAN has \(m\) access layer switches and a distribution layer switch. The hosts are connected to the access layer switches, and the access layer switches are connected to the distribution layer switch.  
Even though access layer switches and the distribution layer switch can be connected in any fashion to offer redundancy and fault tolerance, they eventually form a tree architecture to avoid any loop in the network. Thus, the entire topology can be viewed as a rooted-tree with \((n+m+1)\) vertices and \((n+m)\) edges. The root of the tree is the distribution layer switch. The \(m\) access layer switches are the children of the distribution layer switch, and the hosts form leaf nodes.  
Whenever a host broadcast a new ARP request, the request needs to be forwarded through every active port of the access and the distribution layer switches. Thus, a new ARP request traverses through every edge of the tree. Since there are \(n\) active hosts in the network, the communication message complexity for the traditional ARP is \(O(n\times(n+m))=O(n^{2}+nm)\).  
In PrECast, let a host issue a new ARP-request for a destination. If the destination's <MAC, IP> binding is not available in the MCAM table of the host's switch, the switch will multicast a request to the remaining \(m\) switches. Thus, in this case, the multicast request traverses only through \(m\)-edges connecting \((m+1)\) switches. Thus, the worst case message complexity is \(O(nm)\).  
Even though, the worst case complexity of PrECast is \(O(nm)\), in reality, PrECast issues a smaller number of multicast requests. This is because, at the startup, a switch has more <MAC, IP> bindings stored in its MCAM table than an end-host, which knows nothing in a traditional ARP scheme.  
As we can observe, PrECast has a savings of \(O(n^{2})\) messages, even in the worst case scenario. The savings is significant in terms of bandwidth, host CPU and other performance resources in a medium to a large-scale network.
## 2 Materials and Methods
### Physiological Variables
#### 2.5.2 Cardiovascular Measures  
The cardiovascular measures were recorded using three adhesive foam electrodes with conductive hydrogel. The signal was acquired and digitalized at 1000 Hz using PowerLab/16 SP hardware (Castle Hill, ADInstruments, Bella Vista, Australia) and LabChart software version 5.2. Data were filtered using a 1 Hz low-pass digital filter; after that, the ECG was visually inspected, and abnormal data were edited. Finally, the ECG was analyzed using Kubios Analysis software 2.2 (Biomedical Signal Analysis Group, University of Kuopio, Kuopio, Finland; [PERSON] 2014 [61]). In accordance with Task Force (1996) recommendations for HRV [62], we used the first 5 min of recorded data as a habituation period and analyzed cardiovascular parameters using the last 5 min. Power spectral analyses of HRV were calculated by means of Fast Fourier Transformation (FFT) to extract frequency domain measures. Spectral power density was expressed in absolute units (ms2/Hz). This study computed the LF band (between 0.04 and 0.15 Hz), which is an index of the baroreflex function and could be interpreted as both sympathetic and parasympathetic control [63]; and the HF band (between 0.15 and 0.40 Hz), which reflects the RSA and can be taken as an index of parasympathetic control, and the ratio of the low-frequency and high-frequency powers (LF/HF) were measured.