esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Influence of Laser Treatment on Mechanical Properties
#### 3.2.2 Fatigue Test Analyses  
Figure 6 shows the S-N or Wohler curve, i.e., the maximum stress (S) versus the number of cycles to fracture (N) for both untreated (circles) and laser-textured specimens (triangles). It is worth noting that laser-textured and untreated specimens showed the same fatigue resistance, reporting the same fatigue limit at 516 \(\pm\) 6 MPa, fatigue strength coefficient \(\sigma^{\prime}_{f}\) and fatigue strength exponent \(b\) of the Basquin stress-life relation as reported in Table 2.  
To understand this result, one should consider the generally accepted correlation between fatigue resistance and surface state. A rough surface with deep defects could decrease the fatigue resistance of a material, especially at a cycle regime as high as around 10\({}^{6}\) cycles [27]. In fact, defects or surface valleys act as stress raisers, leading to the initiation of fatigue cracks and compromising the material's resistance to fatigue [28]. The larger the size of the defects or the depth of surface valleys, the higher the impact of the mentioned mechanism. In our case, we can consider that LIPSS have a period of 800-900 nm and typical height of 150-250 nm [29]. These values are significantly lower than the critical crack length (\(\sqrt{area_{critical}}\)) of 8 \(\upmu\)m, calculated according to [PERSON] et al. [30].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Surface Condition** & \(\sigma^{\prime}_{f}\) **(MPa)** & \(b\) \\ \hline Untreated & 730 & 0.024 \\ Laser-treated & 734 & 0.025 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Basquin stress–life parameters for each surface condition.  
Figure 5: Results of tensile stress tests carried out on laser-treated (T) and untreated (NT) specimens in different cutting directions. (**a**) Yield strength (YS) and ultimate tensile strength (UTS), (**b**) uniform elongation (UE) and total plastic deformation (EL), and (**c**) reduction of area (RA). Error bars represent standard deviation values.  
Figure 6: Results of fatigue tests carried out on laser-treated and untreated specimens. The arrow indicates the number of specimens that reached the runout.
## 5 Further Application Examples
### Independent Sets in Graphs  
Consider a graph with vertex set \(V\) and maximum degree \(\Delta\). Assume \(V\) is partitioned as \(V=V_{1}\cup\ldots\cup V_{r}\), where each \(V_{i}\) satisfies \(|V_{i}|\geq\ell\Delta\), for some positive integer \(\ell\). We ask: what value of \(\ell\) guarantees that one can always select a vertex from each \(V_{i}\); such that the selected vertices form an independent set, i.e., a set in which no two vertices are connected.  
First, observe that we can assume \(|V_{i}|=\ell\Delta\), since otherwise we can just remove vertices from \(V_{i}\) to achieve the equality. The reason is that if the claim holds with fewer vertices in \(V_{i}\), then it certainly must hold in the original graph. Set \(k=\ell\Delta\); this will be the size of each \(V_{i}\), after possibly removing some vertices.  
Now, pick a random vertex from each \(V_{i}\). For each edge \(e\), let \(A_{e}\) denote the event that \(e\) connects two of the randomly selected vertices. This can only happen if \(e\) connects vertices in two different sets \(V_{i},V_{j}\). Since \(|V_{i}|=|V_{j}|=k\), \(\Pr(A_{e})=1/k^{2}\) holds for any \(e\) due to the random vertex selection.  
Observe now that if \(e\) has its endpoints in the sets \(A_{i},A_{j}\), then for another edge \(f\), the event \(A_{f}\) can only depend on \(A_{e}\) if \(f\) has an endpoint in \(A_{i}\cup A_{j}\). How many such edges \(f\) can exist? The size of \(A_{i}\cup A_{j}\) is \(2k\) and each vertex is adjacent to at most \(\Delta\) edges. Therefore, the maximum degree in the dependency graph of the \(A_{e}\) events is at most \(d=2k\Delta\).  
Let us now apply the LLL, in its simplest form, presented in Lemma 1. We need to satisfy the condition \(\Pr(A_{e})\leq 1/(4d)\). Due to \(\Pr(A_{e})=1/k^{2}\), it is satisfied if  
\[\frac{1}{k^{2}}\,\leq\,\frac{1}{4d}\,=\,\frac{1}{8k\Delta}\]  
holds. From this, and from \(k=\ell\Delta\), we obtain  
\[\frac{1}{\ell^{2}\Delta^{2}}\,\leq\,\frac{1}{8\ell\Delta^{2}}.\]  
After simplification, this yields \(\ell\geq 8\). Consequently, the Lovasz Local Lemma implies that if \(\ell\geq 8\), then  
\[\Pr(\overline{A}_{e_{1}}\ldots\overline{A}_{e_{m}})>0\]  
holds, where \(e_{1},\ldots,e_{m}\) are the edges of the graph. This means, there is a positive probability that no edge connects two of the randomly selected vertices, so the special selection we are looking for must indeed exist. Thus, by means of the LLL, we have proved the following result:  
**Theorem 4**.: _Let \(G\) be a graph with vertex set \(V\) and maximum degree \(\Delta\). Assume \(V\) is partitioned as \(V=V_{1}\cup\ldots\cup V_{r}\), where each \(V_{i}\) satisfies \(|V_{i}|\geq 8\Delta\). Then, it is always possible to select a vertex from each \(V_{i}\) such that the selected vertices form an independent set._  
**Remark 3**.: The constant \(8\) can be reduced to \(2\mathrm{e}\approx 5.43656\) via the stronger versions of the symmetric LLL (see Section 4.1), but this does not change the principle of the proof.
# Multi-Classification of Chest X-rays for COVID-19 Diagnosis Using Deep Learning Algorithms
###### Abstract  
Accurate detection of COVID-19 is of immense importance to help physicians intervene with appropriate treatments. Although RT-PCR is routinely used for COVID-19 detection, it is expensive, takes a long time, and is prone to inaccurate results. Currently, medical imaging-based detection systems have been explored as an alternative for more accurate diagnosis. In this work, we propose a multi-level diagnostic framework for the accurate detection of COVID-19 using X-ray scans based on transfer learning. The developed framework consists of three stages, beginning with a pre-processing step to remove noise effects and image resizing followed by a deep learning architecture utilizing an Xception pre-trained model for feature extraction from the pre-processed image. Our design utilizes a global average pooling (GAP) layer for avoiding over-fitting, and an activation layer is added in order to reduce the losses. Final classification is achieved using a softmax layer. The system is evaluated using different activation functions and thresholds with different optimizes. We used a benchmark dataset from the kaggle website. The proposed model has been evaluated on 7395 images that consist of 3 classes (COVID-19, normal and pneumonia). Additionally, we compared our framework with the traditional pre-trained deep learning models and with other literature studies. Our evaluation using various metrics showed that our framework achieved a high test accuracy of 99.3% with a minimum loss of 0.02 using the LeakyReLU activation function at a threshold equal to 0.1 with the RMSprop optimizer. Additionally, we achieved a sensitivity and specificity of 99 and F1-Score of 99.3% with only 10 epochs and a \(10^{-4}\) learning rate.  
COVID-19; transfer learning (TL); chest X-ray (CXR); deep learning (DL); artificial intelligence (AI) +
Footnote †: journal: Applied Sciences
## 3 Communication Protocols in IoMT
### Application Layer  
The application layer is dedicated towards managing the smart medical platform. This includes customized interfaces and role-based control panels for diagnostic decision making. After collecting information from other layers, the application layer can transform the information into a suitable form that can be processed by the end-devices and medical servers [20]. The most commonly used application layer protocols are medical-data-encoding protocols such as HL7 (Health Level Seven) and XML (Extensible Markup Language) encode [21], CoAP (Constrained Application Protocol), MQTT (MQ Telemetry  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Protocol** & **ZigBee** & **Bluetooth** & **IEEE802.11 ac** & **Z-Wave** & **LoRaWAN** & **Sigfox** \\ \hline Debits & 250 kbps & 2 Mbps & 433–1300 Mbps & 100 kbps & 0.3–50 kbps & 1 Mbps \\  & & & 35 m (inside) & 30 m (inside) & 20 km (rural area) & 50 km (rural area) \\ Range & 10–100 m & 10–100 m & and 300 m & and 100 m & and 8 km & and 10 km \\  & & & (outside) & outside) & (urban area) & (urban area) \\ Frequency & 2.4 GHz & 2.4 GHz & 5 GHz & 868 MHz & 868 MHz & 868 MHz \\  & & & & (EU) 908 & (EU) 915 & (EU) 902 \\  & & & & MHz (USA) & MHz (USA) & MHz (USA) \\ Security & AES 128 bits & AES 128 bits & \begin{tabular}{l} WEP-WPA \\ (AES 128 bits) \\ \end{tabular} & AES 128 bits & AES 128 bits & \
\begin{tabular}{l} AES 128 bits \\ \end{tabular} &
\begin{tabular}{l} Partially \\ addressed \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Differences between different communication protocols in IoMT.  
Transport), and HTTP (Hypertext Transfer Protocol) secured with Transport Layer Security (TLS) [22].
## 1 Introduction
Many methods, such as physicochemical adsorption [12], bioremediation [13], chemical reduction [14; 15; 16], membrane separation [17] and ion exchange [18] have been developed to enable remediation of Cr(VI) contamination from waters. Among them, the most widely used treatment method applied to industrial wastewaters or groundwaters containing hexavalent chromium is its chemical reduction and precipitation by using it as reducing agent, such as ferrous iron [14]. In this technology hexavalent chromium is reduced to its trivalent form and then precipitates, mainly in the form of hydroxide [Cr(OH)\({}_{3}\)] (Log Ksp = \(-32.3\)) or complexes with iron oxides [4; 15; 19]. The method is based on following reactions:  
\[\mathrm{H_{x}CrO_{4}}^{\mathrm{x-2}(aq)+3 Fe^{2+}(aq)+jH_{2}O\to Cr(OH)_{y}}^{ 3-y}\mathrm{\ (aq)+3 Fe(OH)_{z}}^{3-z(aq)+jH^{+}} \tag{1}\]  
\[\mathrm{xCr(III)+(1-x)Fe(III)+3H_{2}O\to(Cr_{x}Fe_{1-x})(OH)_{3}(s)+3H^{+}} \tag{2}\]  
Fe (II) reacts with Cr (VI) and promotes its reduction to Cr (III), while Fe (II) is oxidized to Fe (III), creating insoluble ferric hydroxide particles [Fe(OH)\({}_{3}\)]. Cr (III) is precipitated either as hydroxide or as mixed oxides of Cr(III)-Fe(III) [20]. The resulting precipitates can be separated from the water by conventional sand or membrane filtration [20]. The main objective of this research is to study the removal of hexavalent chromium from water by chemical reduction and precipitation using ferrous sulfate as the reducing agent followed by sand filtration, using an innovative treatment apparatus, which runs in full-scale mode and uses pipe reactors to promote the reaction of Fe(II) with Cr(VI)  
To the best of our knowledge, until to date, this is the first time, that hexavalent chromium removal is investigated using a continuous treatment unit, based in pipe flocculation reactors followed by sand filtration. In addition, a further aim of this study was to propose a treatment plant, which will be simple in use, with low capital and operational costs, but very efficient in the removal of hexavalent chromium, in order to contribute to the fulfillment of the sustainable goal 6, which is aiming at providing clean water for all [21].
# Secure Delivery Method for Preserving Data Integrity of a Video Frame with Sensitive Objects
## 2 Related Works  
To verify the integrity of video data, there are methods such as a hash function, hash tree, digital signature, and blockchain [12; 13; 14; 15]. In this section, we discuss the methods applied in this paper.
# Microwave Heat Treatment Induced Changes in Forage Hay Digestibility and Cell Microstructure
## 3 Results
### In Vitro Pepsin-Cellulose Digestibility  
Both the dry matter digestibility (DMD) and digestibility of organic matter in the DM (DOMD) contents of lucerne, canola, and wheat hay increased when treated with MW for 60, 20, and 40 s (\(p\) < 0.001), respectively, compared with their controls (Table 1). On the other hand, DMD of pastureand oat did not show any effect up to 60 s treatment. When MW treatment for was 80 s, both hay digestibility measures started to reduce dramatically.
###### Abstract  
The importance of the usage of renewable energy sources in powering wireless sensor nodes in IoT and sensor networks grows together with the increasing number of utilized sensor nodes. Considering the other types of renewable energy sources, solar power differs as the most suitable one and emerges as the major source for powering sensor nodes. Thus, the consideration of using sensor nodes and collected sensor data for estimating solar panel performances and therefore solar power potential can improve the efforts in this direction. This paper presents the methodology for implementing edge intelligence on wireless sensor nodes for solar panel output voltage estimation and forecasting. The methodology covers the usage of the Python Scikit-learn package and micromlgen library for the implementation of edge intelligence on Arduino clone-based sensor nodes, particularly the development boards based on the ESP8266 chips. Scikit-learn is used for analyzing the efficiency of various regressors on collected solar data. The micromlgen library is then used for implementing those regressors on Arduino and clone nodes. The prediction of solar panel voltage generation is based on a single-sensor reading--UV or BH1750 light sensor. The Random Forest and Decision Tree regressors are implemented on the ESP8266-based development board--Wemos D1 R2. The estimation accuracy of the RF model is an MSE of approximately 0.10, MAE of 0.07 for UV and 0.04 for BH1750, and an R\({}^{2}\) of approximately 0.93 for both UV and BH1750 light sensors. The Decision Tree model has a lower accuracy with an MSE between 0.13 and 0.14, MAE of 0.07 for UV and 0.04 for BH1750, and R\({}^{2}\) of 0.90 and 0.89 for the UV and BH1750 sensors, respectively. The methodology and its efficiency are presented and discussed in this paper.  
solar panel performance; solar power potential estimation; Arduino edge intelligence; Scikit-learn library; micrgenml library +
Footnote †: journal: Applied Sciences
# Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights
## 2 Technical Background of NB-IoT  
The evolution of the Internet of Things (IoT) has occurred in recent years, with a forecast on IoT device connectivity running into billions in 2021 [2]. The standardization and careful research into technology has also led to meaningful progress. This section, therefore, briefly describes some of the features of WSN, IoT, Industrial IoT (IIoT), and NB-IoT, as well as their technical challenges.
# PRNet: 3D Object Detection Network-Based on Point-Region Fusion
## 3 PRNet Framework
### IF-Fusion  
To optimally combine image features and augment the capability to represent small objects, the study introduces the Image Feature Fusion (IF-Fusion) module. This module is specifically designed to address the challenge of information loss encountered in the fusion of features across multiple scales and enhance the network's ability to detect and accurately represent small objects. The overall structure of IF-Fusion is shown in Figure 5.  
Initially, the process involves extracting features. For this purpose, a convolutional neural network \(f_{CNN}\) is employed to analyze the input image I. This results in the generation of four distinct feature maps at varying scales, specifically maps \(F_{1}\), \(F_{2}\), \(F_{3}\) and \(F_{4}\). The scale factor between each layer in the IF-Fusion module is set to 2, meaning that each subsequent feature map is downsampled by a factor of 2 compared to the previous layer. Here, \(F_{i}\) is used to represent the feature map corresponding to the \(i\)th layer.  
\[F_{i}=f_{CNN}(I),\;i\in\{1,2,3,4\} \tag{6}\]  
Among them, \(F_{i}\) denotes the feature map of the \(i\)th layer. This represents the outputs for the neural network \(f_{CNN}\) to input image \(I\) at varying depths, facilitating the capture of a spectrum of visual features from basic to advanced levels.  
Subsequently, the process entails performing pairwise fusion. This is achieved by executing a straightforward concatenation operation on each pair of feature maps \((i,j)\). This method results in the creation of six intermediate fusion feature maps \(F^{\prime}_{ij}\), thereby efficiently integrating the feature information across different scales:  
\[F^{\prime}_{ij}=Concat\big{(}F^{\prime}_{i},F^{\prime}_{j}\big{)},\;i<j,\;i,j \in\{1,2,3,4\} \tag{7}\]  
Figure 5: IF-Fusion module structure.  
In the final step, all six intermediate fusion feature maps \(F^{\prime}_{ij}\) undergo a cascade fusion process via another fusion function \(G\). This results in the production of the ultimate fused feature map \(F^{\prime\prime}\):  
\[F^{\prime\prime}=G\left(F^{\prime}_{12},F^{\prime}_{13},F^{\prime}_{14},F^{ \prime}_{23},F^{\prime}_{24},F^{\prime}_{34}\right) \tag{8}\]  
The optimization of feature representation is further enhanced through additional convolution and pooling operations, aiming to minimize information loss.  
A significant strength of IF-Fusion is its ability to fully exploit feature information across various scales via a multi-stage fusion approach, notably improving the network's sensitivity to small objects. The cascaded fusion structure employed in IF-Fusion notably mitigates information loss while simultaneously bolstering the network's capacity to resist interference. This enhancement contributes significantly to the precision and robustness of 3D object detection. The incorporation of the IF-Fusion module plays a crucial role in elevating the overall performance of the network.
## 4 Measurement Setup
### Simulation Setup  
The algorithm was developed and tested using an iterative approach. Simulations were the first step to verify the software implementation of the measurement algorithm. The simulations were developed in C++ using GNURadio libraries. The setup consists of a transmitter and a receiver implemented in software. The transmitter sends simulated interference (test signals) and the receiver runs the measurement algorithm.  
The test signals used to validate the algorithm were based on the interference environment that can be encountered by a satellite in LEO using the UHF band (430-440 MHz). The four types of signals that were used in testing and validation are: _AWGN_, _Continuous Wave (CW)_, _pulse wave_, and a _chirp_ signal. When there are many independent random processes, an AWGN model can be used. A CW is a stationary signal; thus, it can be used to measure full stationarity. Pulsed signals and chirps are chosen because radiolocation is a primary service in the 420-450 MHz band and are expected to be found.
## 3 Results  
The demographic and clinical data of the patients evaluated are presented in Table 1. The six patients analyzed had the right limb as the dominant one, according to the median laterality quotient score (mean \(\pm\) standard deviation: 83.7 \(\pm\) 21.9%). The mean stimulation intensities across subjects, corresponding to the _motor threshold_ and _maximal muscle response_ for biceps brachii were, respectively, 21.7 \(\pm\) 6.4 mA and 79.3 \(\pm\) 21.3 mA for the non-paretic limb, and 30.7 \(\pm\) 15.9 mA and 86.3 \(\pm\) 19.7 mA for the paretic limb.  
Differences between Muscle Responses in Paretic and Non-Paretic limbsWhen comparing _increment amplitudes_ between paretic (3.4 \(\pm\) 1.1%) and non-paretic (2.7 \(\pm\) 1.0%) limbs, the Student's \(t\)-test revealed greater values in paretic muscles (\(p\)= 0.016; \(n=12\); 6 subjects \(\times\) 2 limbs; Figure 3A). More specifically, _increment amplitude_ values differed between limbs by up to 1.3%. The greater increment amplitudes indicate more muscle fibers were activated in paretic than non-paretic limbs for similar, relative increases in the stimulation intensity. This finding suggests that the analysis proposed in this study detected limb differences in the organization of the biceps brachii motor units, likely associated with a muscle reinnervation process due to stroke. Additionally, the statistical analysis also revealed no significant difference in the current range between sides (\(p\) = 0.72;  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**ID**} & \multirow{2}{*}{**Sex**} & **Age** & **Stroke** & **Paretic** & **LQ Score** & **Time since** & **FMA-UE** & **Inc Amp** & **Inc Amp** \\  & & **(yo)** & **Type** & **Limb** & **(\%)** & **Stroke** & **Score** & **Non-Paretic** & **Paretic** \\  & & & & & **(Months)** & **Score** & **Limb (\%)** & **Limb (\%)** \\ \hline
1 & M & 48 & hemorrhagic & right & 81.0 & 19 & 5/12 & 3.5 & 4.8 \\
2 & F & 68 & ischemic & right & 90.5 & 10 & 4/12 & 4.0 & 4.1 \\
3 & M & 61 & hemorrhagic & left & 90.5 & 39 & 10/12 & 1.4 & 1.6 \\
4 & F & 71 & ischemic & right & 41.7 & 1 & 3/12 & 3.0 & 3.7 \\
5 & M & 59 & ischemic & right & 100.0 & 2 & 2/12 & 2.3 & 3.1 \\
6 & M & 70 & ischemic & left & 100.0 & 43 & 6/12 & 2.0 & 3.2 \\ \hline \hline \end{tabular}  
* M: male. F: female. yo: years old. FMA-UE: Fugl-Meyer Assessment for Upper Extremity considered only items related to biceps brachii function. LQ: laterality quotient. Inc amp: increment amplitude.  
\end{table}
Table 1: Demographic, clinical data, and increment amplitude values of the evaluated stroke survivors.  
\(n=12\); 6 subjects \(\times\) 2 limbs; Figure 3B). Half of the patients showed greater ranges in the non-paretic than paretic limb, differing from 4 to 16 mA (i.e., from two to eight stimulation levels), and for the other half the current range was 2 to 18 mA (i.e., one to nine stimulation levels) greater in the paretic limb.
## 3 Experiments
### Number of Preceding Features Input  
The FAW module adaptively propagates contextual cues from previous features for object feature enhancement. In this section, we investigate the impact of different numbers of input features (T frames) on the MOT performance. It is worth emphasizing that we used 50% of videos from the training set for training and the remaining 50% for performance validation in the experiments.  
We experimented with different numbers of input features selected from the preceding T frames as the inputs of our FAW module, where T was set to 1, 2, 3, and 4, respectively. The results are summarized in Table 2.  
As shown in Table 2, using only one feature of the previous frame as the input results in the highest IDF1 performance. When using the features of the previous two frames as the input, the model achieves the highest MOTA performance, albeit with a slight decrease in the IDF1. This phenomenon may be attributed to the FAW module significantly reducing the false detection rate while introducing a small number of missed detections, resulting in a certain number of identity switches in the tracking algorithm. Furthermore, as the number of input features (T frames) increases, there is a noticeable performance decline in both the MOTA and IDF1. To ensure the accuracy of tiny object detection, we selected T = 2 as our FAW module input. For the MFACNet, aggregating two input features in the FAW module indicates utilizing three consecutive frames (including the current frame) as the input.