esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Rock Damage Mechanism for Lateral Blasting under High In Situ Stresses
## 2 Numerical Modeling
### Numerical Model for Lateral Blasting under Static Load  
A numerical model with dimensions of 400 \(\times\) 400 \(\times\) 200 mm was built to simulate the dynamic response and damage evolution of lateral blasting, as shown in Figure 2. It is commonly seen that rectangular cavern is usually generated by production boreholes with an excavation method of lateral caving with large-diameter long-hole blasting or by tunneling excavation for its advantages of simple procedures, high excavation efficiency and convenient support measures [22; 34]. Therefore, the cavern prototype was set as a rectangle in this study, which also has important enlightening significances for other shapes. In the center of the model, a rectangular cavern with a size of span X \(\times\) span Y \(\times\) 200 mm is placed to form the lateral free surface and a borehole with a diameter of 8.0 mm is placed near the cavity with a distance of \(W\). It should be noted that the span X and span Y are no more than \(1/3\) of the model size of 400 mm to decrease the influence of boundary on the stress distribution. The explosive with a diameter of 6.3 mm and a length of 20 mm is charged in the hole centrally. The ends of the borehole are filled with stemming. Static stresses, P1 and P2, are applied to the four external boundaries of the model in X and Y directions respectively using a dynamic relaxation scheme, and the four sides inside the model are specified as free surfaces. After the stress initialization, the charge is loaded in the model and detonated. The numerical model is meshed by hexahedral elements, with a size of 4 mm, which is small enough to avoid any wave distortion [22]. The total number of meshed elements is 0.5 million. In this study, in order to monitor the damage distribution in the rock mass, cut Y1 and cut Z1 are selected, as shown in Figure 2. The evolution process of blast-induced damage is completed before 100 \(\upmu\)s, which is set to the calculation termination time.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Density** & **Poisson’s Ratio** & **Shear Modulus** & **Cohesive Force** & **Friction Coefficient** & **Internal Friction Angle** \\ \(\rho^{\prime}\) & \(\
u\) & \(E_{T}\) & \(c\) & \(\mu\) & \(\varphi\) \\ \hline
2600 kg/m\({}^{3}\) & 0.19 & 16 GPa & 0.018 MPa & 0.7 & 35\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters for stemming.
# Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics
## 3 Summary of ROS-Based RL Applications
### Notable Works Using On-Policy
#### 3.1.3 Multitask Learning  
In [61], the authors introduced Multi-ROS, a framework designed to simplify training, testing, and validation algorithms in simulated environments. Its objective is to overcome the limitations of existing simulation tools, which mainly focus on single-task learning scenarios. Multi-ROS solves these problems by allowing vectorized environments and parallel simulations. This makes it easier to try out more advanced RL methods like meta-learning and multitask learning.  
The framework employs the PPO algorithm to train agents in simulated environments, enabling agents to interact with the environment, collect observations, perform actions, and receive rewards. The ROS facilitates the framework's interaction with the Gazebo simulator, oversees sensor data collection, regulates robot actuators, and manages inter-module communication, as shown in Figure 4. The experimental simulation shows that vectorized environments speed up training, proving that this approach solves time problems in multirobot training scenarios.  
The above-mentioned work of the Multi-ROS framework, with separate Gazebo, robot, and task environment classes, offers flexibility and extensibility for users to develop robot models. The framework is ideal for testing various algorithms and intricate learning scenarios allowing preconfigured settings and parallel simulation. Enhancing the framework will add value to the robotics field by incorporating additional simulation tools and robotic platforms, advanced algorithms, and transfer learning pipelines for seamless transitions between simulated and real-world scenarios.
## 2 Sensor Principle  
### Principle of a Multiple Optical Loss Based Fiber Optic Sensor Using OTDR  
A multiplexed optical loss based fiber optic sensor (MOL-FOS) using OTDR with new sensor probes for sensing reflected signals and measuring a position as shown in Figure 1. The sensor probehas a pair of highly reflected connectors: the first one is a reference connector and the second one is a sensing connector as shown in Figure 1(a). If the pulsed light meets the reference connector and the sensing connector, then some portion of the pulsed light shall be reflected at the connectors. The rest of the pulsed light can travel through the optical fiber line to operate other sensor probes. Figure 1(b) shows the reflected lights from two connectors. If water leakage induces the bending of the optical fiber between the reference connector and the sensing connector, then the bending causes some optical loss between the two connectors (blue line). This optical loss can be detected by the reflected light decrease of the sensing connector. Also, when the light is decreased at the front of the reference connector, then the reflected light signal can indicate the decrease of the effective light intensity. Therefore, if the relation between the water leakage and the change of the sensing signal can be found, then the water leakage can be detected from this fiber optic sensor. It is noteworthy that our sensor probes are a kind of leak detector. Thus, we deploy the sensors in the field and check whether the event occurs or not. If the event happens, the sensors should be exchanged for new ones.  
Figure 1: Schematic diagram of MOL-FOS using OTDR. (**a**) Configuration of MOL-FOS, WCS: Water combination soil; (**b**) The intensity of the received light.
## 3 Incremental Conductance with Integral Compensator (IC-INC)
### Using the IC-INC Method to Train a Neural Network  
It can be seen that MPPT ANNs trained using irradiance and temperature datasets may present severe drawbacks in practice. To use the voltage and current from PVs, a dataset for training a new neural network is obtained through the IC-INC method applied to the PV.  
The new neural network, denoted IC-INC, uses the voltage and current of the PV and their last values (\(t-1\)) as inputs. The ANN IC-INC outputs the variation in the reference current, an external integrator in the output of the ANN that mimics the integrator in the IC-INC, and additional multiplication by \(V_{PV}\) is required (11).  
The IC-INC method includes divisions (11) of \(I_{PV}\) by \(V_{PV}\) and \(dI_{PV}\) by \(dV_{PV}\). This is a more sophisticated process than learning a surface, as performed by ANNs (G, T), because of the known division operation issues that occur in ANNs. As such, two different ANNs were considered in this paper. The first uses three hidden layers and five neurons in the first and second layers, and three neurons in the third layer, while the second uses four hidden layers and six neurons in the first and second layers, four neurons in the third layer, and two neurons in the fourth layer.  
When the results of the two trained ANNs were compared, it was determined that the neural network with four hidden layers provided the best answer, because no overfitting was discovered during its training phase and its performance was higher. The ANN's performance was measured using two parameters: the mean square error (MSE) and R-squared (R). The first implementation with three hidden layers obtained an MSE of 3.72 \(\times\) 10\({}^{-7}\) and an R of 0.85. The implementation with four hidden layers achieved an MSE of 2.48 \(\times\) 10\({}^{-7}\) and an R of 0.92.
## 5 Deployment and Integrability  
The healthcare integration of technological advancements, and, specifically, deep-learning systems, retain its own challenges due to the presence of inherent PHI/PII (personal health information/personal identification information), both in the headers of the DICOM files (that holds up radiological information), and, sometimes, also burned into the image itself. That implies the removal of PHI/PII for all data that leaves the hospital/clinic network, a fact that partially limits the available deployment options.  
That was what motivated the decision to use a model from the EfficientNet family, which not only is already compatible with popular frameworks (_Tensorflow_ and _PyTorch_), but also allows a decent inference speed using local resources while retaining high metrics. Below are shown the different integration options that could be implemented:
_Algorithms_**2010**, \(3\), 44-62; doi:10.3390/a3010044  
_Article_  
**Breat Cancer Detection with Gabor Features from Digital Mammograms**  
**[PERSON]**  
Alcorn State University, Alcorn State, MS 39096, USA; E-Mail: [EMAIL_ADDRESS];  
Tel.: +601-877-6490; Fax: +601-877-3941.  
_Received: 28 October 2009; in revised form: 14 January 2010 / Accepted: 14 January 2010 / Published: 19 January 2010_  
**Abstract:** A new breast cancer detection algorithm, named the \"Gabor Cancer Detection\" (GCD) algorithm, utilizing Gabor features is proposed. Three major steps are involved in the GCD algorithm, preprocessing, segmentation (generating alarm segments), and classification (reducing false alarms). In preprocessing, a digital mammogram is down-sampled, quantized, denoised and enhanced. Nonlinear diffusion is used for noise suppression. In segmentation, a band-pass filter is formed by rotating a 1-D Gaussian filter (off center) in frequency space, termed as \"Circular Gaussian Filter\" (CGF). A CGF can be uniquely characterized by specifying a central frequency and a frequency band. A mass or calcification is a space-occupying lesion and usually appears as a bright region on a mammogram. The alarm segments (suspicious to be masses/calcifications) can be extracted out using a threshold that is adaptively decided upon the histogram analysis of the CGF-filtered mammogram. In classification, a Gabor filter bank is formed with five bands by four orientations (horizontal, vertical, 45 and 135 degree) in Fourier frequency domain. For each mammographic image, twenty Gabor-filtered images are produced. A set of edge histogram descriptors (EHD) are then extracted from 20 Gabor images for classification. An EHD signature is computed with four orientations of Gabor images along each band and five EHD signatures are then joined together to form an EHD feature vector of 20 dimensions. With the EHD features, the fuzzy C-means clustering technique and _k_-nearest neighbor (KNN) classifier are used to reduce the number of false alarms. The experimental results tested on the DDSM database (University of South Florida) show the promises of GCD algorithm in breast cancer detection, which achieved TP (true positive rate) = 90% at FPI (false positives per image) = 1.21 in mass detection; and TP = 93% at FPI = 1.19 in calcification detection.  
**Keywords:** breast cancer; computer-aided detection (CAD); Gabor filter; edge histogram descriptor (EHD); mammography screening; mass detection; calcification detection
## 2 Materials and Methods  
### Study Area  
The investigated area (Figure 1) falls within the so-called area of the Production Settlement Plan (PIP) of the municipality of Statte (Taranto province, Apulia Region,southern Italy; 40\({}^{\circ}\)32\({}^{\prime}\)25\({}^{\prime\prime}\) N, 17\({}^{\circ}\)12\({}^{\prime}\)51\({}^{\prime\prime}\) E). Specifically, it regards an area outside the ex-MATRA, within 100 m of the industry.  
The climate is typically Mediterranean, with precipitation concentrated in winter and hot/dry summers, usually followed by warm and humid falls. Mean annual temperatures are of 16.0 \({}^{\circ}\)C, and mean annual precipitations are of 520 mm, with reference evapotranspiration of 1035 mm. Limestone, dolstone, and sandstone represent the dominant lithology of the area surrounding the study site.  
The reference soil profile of the soil-landscape unit where the site under investigation falls is classified, according to the USDA Soil Taxonomy [30], as _Calcic Haploxeralf_ fine loamy, mixed, thermic [31]. The basic properties of that soil profile are shown in Table 1.  
For the purpose of the present study, twenty-eight soil samples were analysed. The samples had previously been collected (March 2005) by CNR-IRSA at fifteen sites inside the study area, at one (0-15 cm) or two different depths (0-5 and 5-20 cm). The collected soil samples were subjected to chemical analyses [32] for the determination of the sum of the twelve dioxin-like PCBs and six non-dioxin-like \"indicator\" PCBs (hereafter indicated as PCBs\({}_{18}\)) and the percentage of different isomeric classes of PCBs (from Tetra-CB to 376 Hepta-CB) in the PCBs\({}_{18}\). In addition, the extractable organic halogen (EOX) contents were also determined by [32]. EOX represent the total content of halogens (Cl, Br, I) in organo-halogenated compounds, which can be extracted by organic solvents from soil and  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{**Horizon**} & \multirow{3}{*}{**Depth** **(cm)**} & \multicolumn{6}{c}{**Texture**} & \multirow{3}{*}{**pH**} & \multirow{3}{*}{**CaCO\({}_{3}\)**} & \multirow{3}{*}{**OC** **(\%)**} \\  & & & **Class** & **Sand** & & & **Sillt** & \\  & & & **(\%)** & & & **(\%)** & & \\ \hline Ap1 & 0–20 & 29 & 43 & 24 & 4 & 7.84 & 2.5 & 0.58 \\ Ap2 & 20–40 & 30 & 43 & 23 & 4 & 7.71 & 2.25 & 0.311 \\ Bt & 40–100 & 43 & 35 & 19 & 3 & 7.3 & 4.0 & 0.185 \\ Bk & 100–140 & 26 & 38 & 32 & 4 & 8.22 & 26.0 & 0.101 \\ \hline \hline \end{tabular} After [31], mod.  
\end{table}
Table 1: Basic properties of the reference soil profile.  
Figure 1: Location of the study site.  
other sediments. Therefore, it can be considered a useful overall measure of soil pollution by organochlorine compounds, which is why the EOX parameter was also considered in this study.  
For additional information on the soil sampling strategies and the methodologies of chemical analysis, see [26].
# Progress and Summary of Photodarkening in Rare Earth Doped Fiber
###### Abstract  
In this paper, we summarize the research on photodarkening in optical fibers. The causes of photodarkening in fiber, the influence of photodarkening on fiber laser, the experimental device of photodarkening, and the mathematical model used to study the phenomenon of photodarkening are described in detail. At the end of the paper, we summarize the means and methods to suppress photodarkening.  
Keywords:photodarkening (PD); excited-state absorption (ESA); energy transfer upconversion (ETU); avalanche upconversion; cooperative luminescence; photobleaching (PB); thermal-bleaching (TB); refractive index change (RIC); color center +
Footnote †: journal: Applied
## 3 Definitions of the Generalized Operations  
We now give a formal description of the operations involved: _insert_, _delete_ and _move_, all of which are with respect to strings of characters over a finite alphabet.  
Let \(S=S_{1}\cdots S_{n}\) :  
_Insert:_ For a string \(S\) and a position \(1\leq p\leq n\), the operation \(insert\big{(}\sigma,p\big{)}\) inserts the character \(\sigma\) to the \(p\)th position of \(S\). After performing this operation, \(S\) becomes \(S_{1}\cdots S_{p-1}\sigma S_{p}\cdots S_{n}\).  
_Delete:_ The operation \(delete\big{(}\,p,\ell\big{)}\) deletes \(\ell\), \(\big{(}1\leq\ell\leq n-p+1\big{)}\), consecutive characters which occur at positions \(p,...,p+\ell-1\) of \(S\), _i.e._, \(S\) becomes \(S_{1}\cdots S_{p-1}S_{p+\ell}\cdots S_{n}\).  
_Move:_ Given two distinct positions \(1\leq p_{1}\
eq p_{2}\leq n\) and a length \(\ell\), \(move\big{(}\ell,p_{1},p_{2}\big{)}\) moves the string at position \(p_{1}\) of length \(\ell\) to position \(p_{2}\). After performing the move operation, if \(p_{1}<p_{2}\) and \(\ell\leq p_{2}-p_{1}\) then \(S\) becomes \(S_{1}\cdots S_{p_{n}-1}S_{p_{1}+\ell}\cdots S_{p_{2}-1}S_{p_{1}}\cdots S_{p_{n}+ \ell-1}S_{p_{2}}\cdots S_{n}\) ; and if \(p_{2}<p_{1}\) and \(1\leq\ell\leq n-p_{1}+1\), \(S\) becomes: \(S_{1}\cdots S_{p_{2}-1}S_{p_{n}}\cdots S_{p_{n}+\ell-1}S_{p_{2}}\cdots S_{p_{n} -1}S_{p_{n}+\ell}\cdots S_{n}\).  
When \(\ell=1\) we refer to the operations as _delete-character_, and _move-character_, and when \(\ell>1\) we refer to the operations as _block-deletion_, and _block-move_ Alternatively, we sometimes write \(delete\big{(}str,p\big{)}/\,move\big{(}str,p_{1},p_{2}\big{)}\), where \(str\) specifies the string which is deleted or moved, rather than its length. We even use \(delete/\,move\big{(}str\big{)}\) when the indices are clear.
# A Simple Neural-Network-Based Decoder for Short Binary Linear Block Codes
## 6 Experimental Results of Decoding by Proposed Multi-class NN-Based Decoder  
### Simulation Environment and Procedure  
Simulations are conducted on the computer with Intel i9-10900 processor and RTX 3080 graphics card. The traditional soft decision decoding methods, including BP, Chase-II, OSD [11; 12], and the recent DNN-based decoding approaches [13; 14] are evaluated. The BCH and QR codes act as examples for investigation. The simulation procedure for a random message is shown in Figure 10. For each SNR value, 1,000,000 messages are simulated. We consider the decoding performance when the SNR is from 0 to 7 dB. The codeword error rate (CER) and bit error rate (BER) for various decoding methods are assessed.
## 4 Security Attacks
### Network Layer
#### 4.2.5 Routing Attack  
Routing attacks [29; 42] can occur in two primary forms: network partitioning and message delay. In a partitioning attack, adversaries split the network into disjoint segments, which may lead to the creation of parallel blockchains. Once the attack ceases, the smaller chain is typically discarded, resulting in significant loss of mining power and revenue. In delay attacks, adversaries tamper with the propagation of messages, causing blocks to reach victim nodes later than expected. This delay can facilitate double-spending or lead to wasted mining resources. Due to their stealthy nature, these attacks are challenging to detect. Preventative measures include using secure communication protocols and network segmentation to minimize the risk of such routing attacks.
# UWB Localization with Battery-Powered Wireless Backbone for Drone-Based Inventory Management
## 4 Implementation and Evaluation Setup
### Radio Settings  
Both the sub-GHz radio (CC1200) and the UWB radio (DW1000) support multiple modulation types, which influence several properties of the communication performance such as packet duration, range, scalability, reliability and energy consumption. To implement the MAC protocol, multiple radio settings were evaluated as shown in Table 3.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**System on Chip** & **ARM Cortex-M3** \\ \hline Clock speed & 32 MHz \\ \hline Memory & 32 KB RAM \\  & 512 KB FLASH \\ \hline \multirow{3}{*}{Radios} & CC2538: IEEE 802.15.4 (2.4 GHz) \\  & CC1200: sub-GHz IEEE 802.15.4 g (868 MHz) \\  & DW1000: UWB IEEE 802.15.4a (3.5–6 GHz) \\ \hline \multirow{3}{*}{Antenna} & On-board (CC1200 and CC3538) \\  & SMA Connector (DW1000) \\ \hline Protocol stack & Contiki OS \\ \hline \multirow{3}{*}{MAC Design Framework} & Time Annotated Instruction \\  & Set Computer (TAISC) [33] \\ \hline \hline \end{tabular}
\end{table}
Table 2: UWB localization system specifications.  
* For the _UIWB radio_, two configuration options will be evaluated in the following section. The first setting UWB\({}_{slow}\) corresponds to the slowest bitrate and largest preamble. This settings has the highest accuracy ranging, but negatively impacts the update rate due to the longer packet durations. In contrast, the UWB\({}_{fast}\) settings result in lowest energy consumption and larger update rates, but result in reduced ranging accuracy. The impact of these settings will be evaluated in the next section.
* For the _sub-GHz radio_, two different configurations are used during each MAC superframe. (i) The sub-GHz\({}_{slow}\) is used by the anchor nodes to report the ranging results to a remote server. This setting uses a low datarate and large preamble settings to maximize the range. (ii) In contrast, the sub-GHz\({}_{fast}\) setting uses a higher bitrate and lower preamble to maximize the datarate. This last setting is used by the mobile tag to wake up and synchronize nearby anchor nodes. In this case, high datarates are preferable since the higher datarate reduces the length of the beacon slot of the TDMA, and the reduced range has no impact since the tag only needs to wake up anchor nodes which are nearby.
## 1 Introduction  
The word \"smart\" used before the names of devices such as in smartphone, smarttv and smartwatch means that these devices are connected to the Internet and can send and receive information. These devices represent IoT. IoT is \"An open and comprehensive network of intelligent objects that have the capacity to auto-organize, share information, data and resources, reacting and acting in face of situations and changes in the environment\" [1]. IoT revolutionized many domains such as environmental monitoring, medical treatment, public health, Intelligent Transportation System (ITS), smart grid and other areas [2]. Communication in IoT is challenging since smart objects are mobile and heterogeneous. Furthermore, smart objects are limited in computing, storage and energy capacities. Conventional TCP/IP protocols are not suitable to achieve reliable communication in IoT since they assume end-to-end connectivity. DTN is an alternative that was designed for challenged networks characterized by lack of contemporaneous end-to-end connectivity [3].  
DTN's first application was the Inter-Planetary Network (IPN) [4]. [PERSON] and his team developed IPN in the 1990s. IPN is characterized by long delays and intermittent connectivity since it comprises both terrestrial and interplanetary links. In 2003, [PERSON] [5] adapted the IPN architecture for terrestrial networks. DTN is a network architecture designed to address challenges in disrupted networks that suffer long latency. The principle of DTN is the Store-Carry-Forward approach, where nodes can store and transport messages called \"bundles\" to their destination. To assure interoperability, DTN architecture introduces a message oriented overlay, called the \"bundle layer\", on top of the transport layer [6]. The Bundle Protocol (BP) is used to exchange data across a DTN [7].  
Although many DTN solutions for IoT are proposed in the literature, standardization remains unclear. In this paper, we survey existing DTN solutions for IoT and give a comparison based on IoT constraints. We introduce two classifications, the first is for DTN routing while the second is for DTN solutions for IoT. We mainly focus on a new category called X-DTN, which aims to combine delay tolerant schemes with other technologies (M2M, ICN, Fog Computing, etc.). We intend to help new readers to have better understanding of this research area with most recent solutions.  
The remainder of this paper is organized as follows. Section 2 covers important surveys on DTN routing protocols together with a classification we propose. In Section 3, we classify DTN solutions for IoT and we review existing solutions, which we compare and discuss in Section 4. Some concluding remarks and perspectives are available in Section 5.