esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Related Work
The simplicity of the routing protocol mechanisms that provides self-healing capacities to the network circumventing the attacked zone benefits the jamming detection in energy consumption. Our work aims at detection algorithms with low processing and energy requirements compared to others commonly used for the same purpose. Table 1 describes some techniques for jamming detection and mitigation and their respective processing complexity and power consumption. We have compiled information about the typical techniques that mitigate the effects of jamming attacks in wireless networks. In addition to these techniques, we have observed that, when the routing protocol has self-configuring mechanisms, it is easier to distinguish changes in the nodes' performance variables. This is why the algorithm presented here exhibits processing simplicity and low energy impact; from the analysis results presented in this work, such properties will be displayed. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Technique** & **Description** & **Impact on Energy** & **Complexity** \\ \hline Appropriate power transmission [5] & The high transmission power prevents the attacker from being eavesdropped on and interfering with the communications channel. & Low & Low \\ \hline Frequency hopping spread spectrum [30] & It is a highly efficient technique due to its pseudo-random nature of carrier frequencies to achieve fast switching. It requires a wide bandwidth. & Medium & Medium \\ \hline Direct sequence spread spectrum [31] & It is a more efficient technique than DSSS where the signal is similar to white noise, making it difficult to find the transmission source. It is used for spread spectrum modulation that helps reduce interference in the original signal. & High & Medium \\ \hline \end{tabular}
\end{table} 
Table 1: Techniques to mitigate jamming attacks.
## 2 Materials and Methods  
### Characteristics of Volunteers and Groups  
After obtaining the approval of the Bioethical Committee of the Medical University of Silesia in Katowice, 200 feet of 100 women over 50 (68 \(\pm\) 6.5) were examined. The research started in 2015. The project is still being continued and also includes the evaluation of other procedures.  
The research was conducted after obtaining the written consent of the volunteers. They were informed about the purpose of the research and its course. The criteria for inclusion in the study were: voluntary participation, reporting discomfort or pain in the feet while standing, and prophylaxis of lower limb diseases. The criteria for exclusion from the study included: the occurrence of injuries, unhaeled wounds and post-operative conditions in the feet, the presence of infectious lesions, pain resulting from rheumatic diseases, untreated [PERSON]'s neuralgia or unhaeled heel spurs, and the lack/withdrawal of consent to participate in the study. On the basis of an interview about the state of health, those people who stood and moved independently without the need to support themselves were qualified for the study.  
Then, in order to assess the physiological effect of podological treatments, the following groups were distinguished in the AV group (all volunteers): group T (treatment)--76 feet of 38 women who had undergone callus and/or corns removal procedures; group C (control)--80 feet of 40 women with keratosis but without calluses and corns, thus they had no treatments performed; group NT (no treatment)--44 feet of 22 women who, despite the presence of calluses and/or corns, did not agree to treatment, thus they were excluded from further examinations (Table 1).  
In groups T and C subgroups were distinguished according to the following criteria:  
* based on foot geometry parameters--transverse flat foot according to the Wejsflog index (W):
* transverse flat foot (T\({}_{\text{F}}\)),
* normal transverse arch (T\({}_{\text{NA}}\)).
* based on the force of pressure on the ground (P) in the forefoot area:
* insufficient pressure in the forefoot (T\({}_{\text{IP}}\)),
* normal pressure in the forefoot (T\({}_{\text{NP}}\)),
* excessive pressure in the front area (T\({}_{\text{EP}}\)).  
Each volunteer has undergone a study involving: a podiatry interview, visual assessment and palpation of the feet, as well as objective measurement methods.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Group/ Subgroup**} & \multirow{2}{*}{**No. of Volunteers**} & \multirow{2}{*}{**No. of Feet**} & \multicolumn{6}{c}{**Age (Years)**} & \multicolumn{6}{c}{**BMI (kg/m2)**} \\ \cline{3-11}  & & & **Avg.** & **SD** & **Min** & **Max** & **Avg.** & **SD** & **Min** & **Max** \\ \hline
**AV** & 100 & 200 & 68.0 & 6.5 & 50.0 & 85.0 & 33.1 & 5.3 & 21.8 & 46.4 \\
**T** & 38 & 76 & 66.5 & 7.6 & 50.0 & 83.0 & 32.1 & 5.6 & 23.5 & 46.4 \\
**T\({}_{\text{F}}\)**
# Detecting Change at Archaeological Sites in North Africa Using Open-Source Satellite Imagery
## 3 Results
### Aswan and Kom Ombo
#### 3.1.4 False Negatives  
The producer's accuracy of 78% reveals that changes at 12 sites were missed by the algorithm. Inevitably, instances of change which are too small to be visible in Sentinel-2 images were not detected, including graffiti and very small pits and spoil heaps.  
Significantly, some changes near the standing remain of an important Coptic monastery were missed (SM12, Figure 14). The monastery, built on top of a Ptolemaic temple, is located on the riverside of the New Aswan city. It was excavated a hundred years ago, and since then, has been refilled by Aeolian sand. Since the 2011 revolution, and with the ongoing construction work for the new urban centre, the site has been repeatedly looted, and previously buried brick walls are now visible on the ground. These are also detectable from satellite imagery.  
Figure 13: Sand dune and cliffs that cause false positives (by shadows/moving sand) at WT12, 18 and 25, where remains of desert walls, stone structures and rock art were recorded by AKAP. Photograph November 2018.  
Between the two analysis periods, a tree adjacent to the structure was removed and an area of burning appeared. However, because there was another, surviving tree registered within the same Sentinel-2 pixels, the resolution of the imagery was too low for this change to be detected. This is an issue because even these relatively small changes could signify plans for extensive destruction. In this case, the site is in the path of planned construction along the New Aswan riverbank, and AKAP is working with the local authorities to deliver mitigating measures.  
In one case (NT01-03, some tombs and a cluster of small stone structures) change was registered due to relatively minor changes, such as a new roof on an existing building and vehicle tracks, but within the buffered area a more significant change was missed (Figure 15). This was because the change occurred between the end of the period encompassed by the later composite (1 January 2020) and the day of the field visit (26 January 2020), showing how fast damage to sites can occur. The changes were  
Figure 14: Cut-down tree and area of burning in the buffered zone at SM12 and direction of the photograph. Google image 2020 imported as an xyz tile to QGIS. Photograph January 2020.  
visible as a few brighter pixels in the PlanetScope imagery and represented the ongoing construction of new buildings, confirmed during the field visit. AKAP has managed to produce basic documentation with photogrammetric reconstruction of the visible archaeological features.
## 10 Challenges and Open Research Problems
### Complexity
Path planning involves motion planning (to control the path length and turning angles), trajectory planning (involving the speed and kinematics of the vehicle), and navigation (involving localization and obstacle/collision avoidance) [332]. For UAV swarms used as aerial BSs, path planning in such cases requires high-rate exchange of positional sensor data, which involves multi-dimensional channel characterization, tracking and communication, interference management, transmit power allocation, resource block assignment, etc. [333]. Path planning for other UAV applications such as monitoring or target tracking are complicated by issues such as target location and identification, which arise due to the high mobility of the UAVs. Some of the techniques used in path planning can be categorized under representative, cooperative, and non-cooperative techniques [332]. Increasingly, ML approaches are employed to address path planning problems [334]. To save on energy, time, computational and communications costs, path planning is a complex task whose complexity grows rapidly as the number of UAVs in the swarm increases. Other key challenges that arise due to the complexity of multi-UAV systems include security, equitable allocation of workload and coverage area. The greater the number of UAVs in a swarm, the larger the attack surface [335]. Therefore, modeling potential threats and designing a robust security framework to thwart cyber attacks is a highly complex and challenging task.
## Waveform Generator  
#### 2.3.2 Development of the MDDS Waveform Generator  
The developed waveform generator based on the improved MDDS is depicted in Figure 6. The width, length, and height of the waveform generator are 25, 17, and 4 cm, respectively. The weight of the entire system containing the RF modulator is less than 4 kg. The waveform generator and the RF modulator are separated into two layers to prevent signal inflow to each other. The developed system improves the gain of spectral regrowth by more than 20 dB compared to the previous RF module manufactured without shielding. The ripple decreases from more than 3 dB before to less than 1 dB after shielding.  
Figure 5: Proposed hardware architecture. The waveform generator consisted of one field programmable gate array (FPGA), two digital analog convertors (DACs), a filter, etc. Xilinx ISE was used to code very-high-speed-integrated-circuits hardware description language (VHDL), and the program was uploaded to the FPGA using joint test action group (JTAG). A radio frequency (RF) modulator converted to intermediate frequency (IF). We used an additional local oscillator (LO) to up-convert the X-band.  
The four signals (I+, I-, Q+, Q-) output from the two DACs are mixed with a local oscillator (LO) in the quadrature modulator. The signal with an intermediate frequency is amplified once more to the X-band (9.75 GHz). Frequency error, spectrum quality degradation, and high-power amplifier (HPA) distortion are generated in this process. We compensated for the errors using pre-distortion in the digital components.
## 3 KA-DDPG-Based Direct Torque Distribution Method
### KA-DDPG-Based Driving Torque Distribution
#### 3.2.3 Reward Function
```
1:Initialize critic network \(Q\big{(}s,a\mid\theta Q\big{)}\) and actor network \(u(s\mid\theta^{u})\);
2:Initialize target network \(Q^{\prime}\) and \(u^{\prime}\) with same weights;
3:Initialize replay buffer \(RB\);
4:for\(episode=1,\cdots,M\)do
5: Receive initial observation state \(s_{0}\);
6:for\(t=1,\cdots,T\)do
7: Select agent action \(a_{a,t}=u(s_{t}\mid\theta^{u})+N_{t}\);
8: Get criteria action \(a_{c,t}\);
9: Calculate execution action: \(a_{e,t}=\left(1-\gamma^{episode}\right)\cdot a_{a,t}+\gamma^{episode}\cdot a_{c,t}\);
10: Get observed reward \(R_{o,t}\);
11: Get guiding reward \(R_{g,t}\);
12: Calculate updating reward: \(R_{u,t}=\left(1-\gamma^{episode}\right)\cdot R_{o,t}+\gamma^{episode}\cdot R_{g,t}\);
13: Store transition \(\langle s_{t},a_{e,t},R_{u,t},s_{t+1}\rangle\) in \(RB\);
14: Sample a random mini-batch of N transitions from \(RB\);
15: Set \(y_{i}=r_{i}+\gamma Q^{\prime}\Big{(}s_{i+1},u^{\prime}\Big{(}s_{i+1}\theta^{u ^{\prime}}\Big{)}\mid\theta Q^{\prime}\Big{)}\);
16: Update critic by minimizing the loss: \(L\big{(}\theta^{Q}\big{)}=\frac{1}{N}\sum_{t}^{N}\big{(}y_{i}-Q\big{(}s_{i},a_ {i}\mid\theta Q\big{)}\big{)}^{2}\);
17: Update the actor policy, using the sampled policy gradient: \(\
abla_{\theta^{u}}u\mid_{s_{t}}\approx\frac{1}{N}\sum_{t}^{N}\big{[}\
abla_{ a_{t}}Q\big{(}s_{t},a_{t}\mid\theta^{Q}\big{)}\
abla_{\theta^{u}}u(s_{t}\mid \theta^{u})\big{]}\);
18: Update the target networks \(Q^{\prime}\) and \(u^{\prime}\). ```  
**Algorithm 1** KA-DDPG algorithm
# A Novel Deep Learning Approach for Machinery Prognostics Based on Time Windows
## 3 Experiment and Analysis
### Experimental Setup
#### 3.2.2 Time Window Processing  
Many multivariate temporal data-based prediction models take as an input a multivariate data point sampled at a single time stamp, which neglects some useful temporal information that may  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sub-Dataset** & **FD001** & **FD002** & **FD003** & **FD004** \\ \hline Engine models for training & 100 & 260 & 100 & 249 \\ Engine models for testing & 100 & 259 & 100 & 248 \\ Simulation conditions & 1 & 6 & 1 & 6 \\ Fault modes & 1 & 1 & 2 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Information of NASA C-MAPSS dataset.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**The Selected Time-Varying Variables** & **Units** \\ \hline Total temperature at LPC outlet & \({}^{\circ}\)R \\ Total temperature at HPC outlet & \({}^{\circ}\)R \\ Total temperature at LPT outlet & \({}^{\circ}\)R \\ Total pressure at HPC outlet & psia \\ Physical fan speed & rpm \\ Physical core speed & rpm \\ Static pressure at HPC outlet & psia \\ Ratio of fuel flow to Ps30 & pps/psi \\ Corrected fan speed & rpm \\ Corrected core speed & rpm \\ Bypass ratio & – \\ Bleed enthalpy & – \\ HPT coolant bleed & 1 bm/s \\ LPT coolant bleed & 1 bm/s \\ \hline \hline \end{tabular}
\end{table}
Table 3: The selected time-varying variables.  
much improve prediction performance [23]. Therefore, the TW is introduced to solve this problem. Specifically, at any particular timestamp, TW covers the data of the current timestamp and its several preceding timestamps, which is then fed as an input into the prediction model [23]. In this study, the effect of the TW size on the prediction results was considered.
## 4 The Anatomy of Obesity
### Surface Disposition of Somatic Adipose Tissue  
One of the first aspects that becomes apparent when observing an individual with excess adipose tissue is the superficial distribution of body fat, with a particular predisposition to certain anatomical areas. The simplest form of categorizing superficial fat distribution is to discern between android and gynecoid obesity patterns. Developmentally, the difference between the two becomes apparent under the influence of sex hormones, typically during adolescence. Android obesity is characteristic for males and implies the distribution of fat around the central areas of the body, particularly the abdomen, whilst in gynoid adipose distribution, usually seen in overweight women, the hips and thighs are the most prominently interested areas [25]. Sexual dimorphism of body fat distribution can be explained in part by the particular sensitivity of adipose depots to the influence of sex hormones. The femoral-gluteal region, for example, is more prone to the inhibitory influence of testosterone on lipoprotein activity [26] as well as the estradiol-dependent increase in the expression of lipolytic a2-adrenergic receptors [27]. In addition, sex hormones also act upon adipocyte maturation. Testosterone suppresses adipocyte formation [28] while estrogen promotes the proliferation of preadipocytes while progestins initiate their differentiation [29].  
Although visually objectifiable due to the distribution of somatic fat, android obesity is typically associated with a higher accumulation of visceral adipose tissue and has been shown to be responsible for a greater increase in cardiovascular increase when compared to gynoid obesity [30]. The relevance of discerning between these two types of deposits is outlined in the following section.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Location** & **Year** & **Type of Weight Excess** & **Age Group (Years)** & **Prevalence** & **References** \\ \hline \multirow{4}{*}{United States of America} & 2012 & & 2–5 & 22.8\% & \\  & & Overweight or obesity & 6–11 & 34.2\% & \\  & & & 12–19 & 34.5\% & \\  & & & 2–5 & 8.4\% & [19] \\  & & Obesity & 6–11 & 17.7\% & \\  & & & 12–19 & 20.5\% & \\ Latin America & & Overweight & 0–5 & 7.1\% & \\  & 2008–2013 & Overweight or obesity & 6–11 & 18.9–36.9\% & [20] \\  & & Overweight & 12–19 & 16.6–35.8\% & \\  & & Overweight & 6–11 & 11.2\% & \\ Asia & 1999–2017 & Obesity & 12–19 & 14.6\% & \\  & & Obesity & 6–11 & 5.8\% & [21] \\  & & & 12–19 & 8.6\% & \\ Africa & 2017 & Overweight and obesity & 0–5 & 8–16\% & [22] \\ Australia & 2014–2015 & Overweight & 2–4 & 11\% & [23] \\  & & & 5–17 & 20\% & \\  & & Obesity & 2–4 & 9\% & \\  & & & 5–17 & 7\% & \\ Europe & 2011–2016 & Overweight and obesity & 2–13 & 21.3\% & [24] \\  & & Obesity & & 5.7\% & \\ \hline \end{tabular}
\end{table}
Table 1: Childhood obesity prevalence in relation to geographical location.
## 2 Related Work
 & **Strengths** & **Limitations** \\ \hline Socialite & [35] & 2015 & ontology-based modeling & semantic interoperability; new relationships & lack of mechanisms for relationship \\ Virtual objects & [36] & 2018 & ontology; semantic web; virtual objects & semantic interoperability & maxagenent, security trust and privacy, and service discovery; \\ Cognitive & [37] & 2017 & ontology-based modeling & semantic interoperability; relationship management & absence of practical implementation \\ SIoT for & [38] & 2019 & ontology-based modeling; semantic similarity trust and privacy; service discovery & semantic interoperability; relationship management; security trust and privacy, and service discovery; \\ Semantic service & [39] & 2014 & ontology-based modeling & semantic interoperability & limited evaluation; lack of scalability testing \\ for SIoT & & & & & \\ Semantic-based & [40] & 2019 & ontology-based modeling; semantic interoperability; service discovery & & implementation and evaluation \\  & &  
Additionally, privacy management and security issues remain a challenge that needs to be addressed. S2 NetM stands out as a proposed solution that addresses some of the limitations of related works. S2 NetM utilizes ontology-based modeling and semantic reasoning techniques, which improve interoperability between heterogeneous systems. One of the strengths of S2 NetM is its ability to handle social relationships between users and devices through ontology-based modeling. In addition, S2 NetM addresses the limitations of other studies by incorporating privacy preservation techniques and spatiotemporal criteria in its proposed architecture. This can improve the accuracy of recommendations and personalized services provided to users. The S2 NetM design, with components distributed across different nodes, enables faster processing and improved scalability. Furthermore, S2 NetM has the potential to integrate with edge computing, which can address the limitations of centralized systems by reducing network latency and improving response time.
# A Transfer Learning Method for Pneumonia Classification and Visualization
## 7 Conclusions  
In this paper, we present a transfer learning method that automatically classifies between chest X-ray images of people that do not have a pulmonary disease, labeled as NORMAL, and people that have Pneumonia disease, labeled as PNEUMONIA. The proposed method uses a Transfer Learning technique to use the Xception Convolutional Neural Network pre-trained weights on ImageNet as an initialization for the new model. This network has 36 convolutional layers, and for the output, flow contains a Global Average Pooling layer, a dropout of 0.25 keep rate, and a logistic layer of two neurons with Sigmoid as activation function. Images are preprocessed to eliminate black segments, weights are assigned to penalize the cost function, and Adam optimizer is used to minimize the binary cross-entropy function. With the transfer learning, we overcome the problem of having a huge dataset of images. Our method outperforms baseline models and state-of-the-art previous results work in some performance measures. Moreover, we achieve a precision score of 0.843; a recall score of 0.992; an F1-Score of 0.912; an AUC score of 0.962 for the ROC curve; and an AUC score of 0.973 for precision-recall curves. In addition, our proposal is up to 5.4 to 6.7 times faster compared with the baseline models used in this research.  
As future work, we would want to consider invariance of results on image shifts, and both preprocessing and data augmentation techniques that involve brightness and contrast changes, as well as image warping. In addition, we would also try using an automatic parameter-tuning method and apply the proposed algorithm to several medical image dataset, in order to make a statistical analysis of its performance.  
Conceptualization, [PERSON]-G., [PERSON]-R., and [PERSON]-[PERSON]; validation, O.C.-N.; formal analysis, J.E.L.-G., [PERSON]-R., and [PERSON]-[PERSON]; investigation, O.C.-N.; writing--original draft preparation, [PERSON]-G.; writing--review and editing, [PERSON]-R., and [PERSON]; visualization, [PERSON]; supervision, [PERSON], and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors gratefully acknowledge the Instituto Politecnico Nacional (Secretaria Academica, Comision de Operacion y Fomento de Actividades Academicas, Secretaria de Investigacion y Posgrado, Centro de Investigacion en Computacion, and Centro de Innovacion y Desarrollo Tecnologico en Computo), the Consejo Nacional de Ciencia y Tecnologia (CONACyT), and Sistema Nacional de Investigadores for their economic support to develop this work.  
The authors declare no conflict of interest.  
Figure 13: An example of LZnet2019 performance. (**a**) is an original CXRAY and (**b**) its corresponding Grad-CAM from the LZNet2019 article.
# Data Acquisition System Based on the Bluesky Suite in the Shanghai Synchrotron Radiation Facility
## 2 Materials and Methods
### Control and Data Acquisition Architecture  
The architecture, shown in Figure 2, of the control and data acquisition software developed for the BL07U line is based on Bluesky suite and EPICS. The design concepts of these packages allow for building a versatile data acquisition system. From the bottom to the top are the hardware layer, the control layer, the data acquisition layer and the operator interface layer.  
Specifically, the hardware layer consists of all the setups, including one toroidal mirror, one double crystal monochromator, five SR570 amplifiers, three Volt-Frequency converters, one scaler SIS3820, one embedded CPU MVME5500 and one Virtual Machine Environment (VME) machine.  
Figure 1: The hardware of BL07U measurement system.  
The control layer is developed based on EPICS. EPICS consists of two major components: Input/Output controller (IOC) and Operator Interface (OPI) [5,31]. The IOC uses Process Variables (PVs) to control spectroscopy setups. For example, via corresponding PVs, the monochromator can change its energy, angle and status; and the scaler SIS3820 can set its dwell time, status and 32-channel scaler value. IOC runs on the embedded CPU MVME5500 in the vxWorks operating system. OPI runs on the experimental computer in the CentOS operating system. OPI and IOC generally exchange PVs data by EPICS Channel Access (CA) protocol via Ethernet.  
The data acquisition layer is developed based on Bluesky suite, which is a suite of Python code. EPICS provides _CA_ application interface libraries with various high-level languages, and the interface with Python is _PyErics_[5]. The control layer and the data acquisition layer communicate through the _PyErics_ mechanism.  
The operator interface layer offers a graphical user interface (GUI), and the GUI is developed based on Control System Studio (CSS) [32]. CSS is an OPI application software supported by EPICS. CSS communicates with the control layer in real time through the CA mechanism and the data acquisition layer in real time through _PyErics_, respectively.