esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
This study introduced a novel gradient-oriented prioritization meta-learning (GOPML) algorithm, designed to enhance the efficiency and effectiveness of few-shot learning in fault diagnosis tasks. The methodological focus was on leveraging the intrinsic patterns embedded in industrial process data to enable robust fault diagnosis, using datasets such as TEP and SKAB. The GOPML algorithm demonstrated significant improvements over traditional methods in fault classification accuracy, precision, and F1 score, particularly in few-shot scenarios. The use of gradient-based task prioritization within GOPML has proven to be a key factor in its success, indicating that the magnitude and alignment of gradients are critical indicators of a task's learning potential. This finding aligns with previous studies in meta-learning that have emphasized the importance of task selection and prioritization in improving learning outcomes. Another notable aspect of GOPML is its adaptability to different operational conditions and fault scenarios. The algorithm's ability to maintain a balanced performance across various dataset divisions is indicative of its robustness and reliability in practical industrial contexts. This adaptability is crucial in fault diagnosis applications, where operational conditions can vary significantly.  
The results also underscore the potential of GOPML in addressing the challenges posed by limited data availability in industrial settings. By optimizing the learning process through task sequencing and prioritization, GOPML has shown that it is possible to achieve high levels of diagnostic accuracy with a relatively small number of samples.
# Influence of Earthquakes on Landslide Susceptibility in a Seismic Prone Catchment in Central Asia
## 2 Materials and Methods  
### Study Area  
The study area of the Mailuu-Suu catchment lies in the north-eastern part of the Ferghana Valley in Central Asia. The Mailuu-Suu river feeds the Syr Daria river, which is the major source of irrigation water in the Ferghana Valley (Figure 1) [50]. The catchment is located at 600-4400 m.a.s.l. with a catchment area of 530 km\({}^{2}\), and is characterized by semi-arid climatic conditions [48]. This study area belongs to the tectonically and seismically  
Figure 1: Maps of **(a)** the locality of Central Asia, **(b)** the important toponyms in the study area, **(c)** the distributions of tailings ponds and the elevation pattern of the Mailuu-Suu catchment, the localities of the recorded earthquake events in **(d)** Central Asia and **(e)** the Mailuu-Suu catchment between 1900 and 2020, and **(f,g)** the past landslide events in the Mailuu-Suu catchment.  
active Tien Shan Mountains [48]. These mountains are an earthquake hotspot originating from the continent movements of India and Asia [48]. In total, 4933 earthquake events with a magnitude greater than 3.0 were observed in the Central Asian part of the Tien Shan Mountains during 1900 and 2020 ([[https://earthquake.usgs.gov](https://earthquake.usgs.gov)]([https://earthquake.usgs.gov](https://earthquake.usgs.gov)); accessed on 22 March 2021 Figure 1d). The maximum magnitude of 7.7 (or M 7.7) was recorded in the eastern part of Kazakhstan (latitude = 43.01\({}^{\circ}\) and longitude = 78.53\({}^{\circ}\)) on 3 January 1911. Five earthquake events were recently recorded in the Mailuu-Suu catchment, namely in 1983 (M 4.7), 2003 (M 4.6), 2006 (M 4.8), 2011 (M 4.6), and 2018 (M 4.6) (Figure 1e). During the last 50 years, the catchment has experienced severe landslide disasters in the vicinity of numerous radioactive waste tailings ponds [46; 48]. The probability of the damages of earthquake and landslide in this catchment is expected to increase in the future as a result of anthropogenic activities, such as deforestation, overgrazing, agricultural activities and development of infrastructure. Furthermore, the population growth will also provide more pressure on the fragile landscape and make the study area more vulnerable to landslide [46; 48].  
As a former uranium mining area [52; 57], in and around the Mailuu-Suu town, uranium mining and milling activities started in 1946 and lasted until 1968 [48; 54]. Most of the waste dumps and tailing ponds (Figure 1c) from mining were deposited in the moderate mountainous terrain and gently sloping alluvial areas [54; 58]. Under the effect of earthquakes, the release of radioactive soils from the tailing ponds is highly likely to run into the river networks and further transport into the downstream areas, where agricultural activities and dense populations can be largely affected.
## Abbreviations  
The following abbreviations are used in this manuscript:  
AAA  Authentication, Authorization and Accounting  
AD  Active Directory  
API  Application Program Interface  
Apps  apps for smartphones  
CapEx  Capital Expenditure  
CB  Content Broker  
CCO or OCC  Operational Control Center  
CDN  Content Delivery Network  
CoAP  Constrained Application Protocol  
CPE  Customer Premissed equipment on customer site  
DB  Data Base  
DL  Deep Learning  
DNS  Domain Name System  
DoS  Denial-of-Service  
E2E  End-to-End  
EVs  Electric vehicles  
Fiware  European organization that proposes solutions for the IoT world  
GPU  Graphic Processing Unit  
Hetnets  heterozygous networks  
IEEE 802.11 ax  next-generation wireless networking protocol or Wi-Fi 6  
IETF  Internet Engineering Task Force  
IoT  Internet of Things  
IT  Information Technology  
LAN  Local Area Network  
LoRa  Long Range  
LWM2M  Lightweight Machine-to-Machine, OMA Alliance protocol  
MCC  Mobile Cloud Computing  
MEC  Mobile Edge Computing  
Mist  services based on cloud  
ML  Machine Learning  
MQTT  Message Queue Telemetry Transport  
M2M  Machine-to-Machine  
NB-IoT  Narrow Band-IoT  
PaaS  Plataform as a Service  
QoS  Quality of Service  
QoE  Quality of Experience  
RESTful  Representational State Transfer  
RFC  Request for Comments, Internet standard  
RFID  RadioFrequency IDentification  
RTCP  Real Time Control Protocol  
RTP  Real-Time Protocol
## 3 Results and Discussion
### Post-Treatment Using CW  
Influent pH values of the filter-treated WW presented a mean average of 7.01 and increased slightly to 7.30 during the post-treatment process in both CW units after the gravel layer (2/3 of the unit's length). This increase suggests further biological degradation and the adaptation of specific microorganisms, as described above. The pH values were observed to remain stable at 7.30 after the zeolite layer. Influent EC values presented a mean average of 433 \(\upmu\)S/cm, and were observed to increase during treatment in the CW units to 814 \(\upmu\)S/cm after the gravel layer, and to 930 \(\upmu\)S/cm after the zeolite layer, most likely due to wastewater concentration caused by high evapotranspiration (ET) rates [65].  
Figure 5a,b present time series charts for the removal of d-COD as recorded in the CW-P and CW-U units, respectively. Concentrations at the 2/3 point of unit length and effluent points were appropriately corrected to take into account the precipitation and evapotranspiration volumes. Influent d-COD concentrations varied from 788 to 2985 mg/L as the quality of the pre-treated WW was not constant, meaning that the CWs were tested under real operating conditions. Mean d-COD removal efficiencies for the two-year operation period were 91.7% and 81.0% for the CW-P and CW-U units, respectively. As shown in Figure 5, d-COD removal efficiency predictably fluctuated during the commissioning phase, after plant biomass harvesting (end of January), and after HRT alterations [65; 66; 67]. As reported in previous studies [67; 68; 69; 70], most of the organic load was removed within the first two thirds of the length of the pilot-units (mean removal efficiencies measured at the 2/3 rd length point were 75.5% and 64.7% in CW-P and CW-U, respectively), indicating that organic matter is mainly removed in the first sector of a CW. On the other hand, additional organic matter removal was observed to occur in the zeolite layer, as final COD removal rates averaged 92% in CW-P and 82% in CW-U, thus confirming the ability of zeolite to remove organic substances [66; 67; 71].  
Figure 5: Time series charts for d-COD concentrations in the: (**a**) CW-P, and (**b**) CW-U unit.  
In contrast to d-COD, NH4+-N removal from the pre-treated WW was significant, as removal rates of 78% and 75% were recorded from the zeolite layers of CW-P and CW-U, respectively. In CW-P, 57% of the initial ammonium concentration was removed in the gravel layer, while in the CW-U unit this value was limited to 30% (Figure 6).  
The relatively low removal rates observed in the gravel layer, especially in CW-U, could be attributed to the low HRT values, which were not sufficient to remove NH4+-N [68], and to the inability of the experimental units to remove nitrogen when treating effluents with high organic matter loads [67]. On the other hand, NH4+-N was sufficiently removed on the zeolite layer, mainly through adsorption onto the zeolite grains, which are known to be very efficient at ammonium removal [66; 71; 72; 73; 74; 75; 76].
## 2 Materials and Methods
### Manual Assessment of EPVS  
This study defines EPVS as small, sharply delineated structures of less than 3 mm following the movement of the perforating vessel [23] and containing CSF intensity (or near-CSF intensity). Visual assessment of EPVS rating was carried out manually by two neuroradiologists (one with 22 years of experience and the other with 8 years of experience) who were blinded to the clinical diagnosis one month prior to the visual assessment session. The two raters conducted their own analyses and reached a consensus on the final decision. All images (T1-weighted, T2-weighted MRI, and FLAIR) were considered for manual visual assessment. To begin, EPVS is evaluated using T2-weighted MRI. In FLAIR images, even if surrounding hyperintensity is present, it is regarded as EPVS if it exists inside white matter [23]. EPVS is evaluated based on the three slices that contain the basal ganglia, which is above anterior commissure area. As suggested in [20,22], EPVS evaluation also includes sub-insular white matter originating from perforating artery of the insular cortex as the basal ganglia.  
Both the size and shapes are critical in assessing the EPVS. When the exact count is difficult to obtain, general impression of imaging features can aid the EPVS rating [20,22]. Thus, the general impression of each rater is also recorded to grade EPVS. Finally, we recorded the counts of EPVS on both brains (three slices) of basal ganglia area independently and selected the highest count for rating. In cases of asymmetrical distribution in each anatomical region, the higher count from the two regions is selected for rating the EPVS. The rating (five point scale [20]) was made based on the EPVS counts (Table 2). Along with the rating score of the EPVS, the manual ratings of the dataset for all 576 images (96 subjects) are also presented in Table 2. Agreement between the two raters (evaluated  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Characteristics**} & \multirow{2}{*}{**AD**} & \multicolumn{2}{c}{**Other**} & \multirow{2}{*}{**MCI**} & \multirow{2}{*}{**SMI**} & \multirow{2}{*}{**Healthy**} \\  & & **Dementia** & & & \\ \hline Age & 76.35 \(\pm\) 1.98 & 70.00 \(\pm\) 12.72 & 70.21 \(\pm\) 5.86 & 67.71 \(\pm\) 9.18 & 31.05 \(\pm\) 7.37 \\ Male:Female & 26 & 1.3 & 16.31 & 6.11 & 13.7 \\ CDR & 0.625 \(\pm\) 0.35 & 0.5 \(\pm\) 0.0 & 0.46 \(\pm\) 0.14 & 0.29 \(\pm\) 0.25 & - \\ CDR (SB) & 3.5 \(\pm\) 1.96 & 1.375 \(\pm\) 1.11 & 1.27 \(\pm\) 0.87 & 0.44 \(\pm\) 0.35 & - \\ MMSE & 22 \(\pm\) 4.21 & 24.5 \(\pm\) 3.70 & 26.26 \(\pm\) 2.58 & 28.85 \(\pm\) 1.69 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: The demographic information of patients (age, clinical dementia rating (CDR), CDR (SB), and MMSE-mini-mental status examination shown as mean \(\pm\) standard deviation).  
via intraclass correlation coefficient (ICC) using the healthy controls' T2 images) were 88.0% (95% confidence interval: 0.761-0.942) and 90.1% (95% confidence interval: 0.800-0.952).
# Smart Cities Big Data Algorithms for Sensors Location
## 3 Process Implementation
a function of partial elimination of records in the cloud. For Phases 3 and 4, the R language, kernlab, e1071, rpart, and doParallel libraries were applied to support the process. Three prediction methods were tested: multiple linear regression (MLR), support vector machine (SVM), and decision trees implementing a classification and regression tree (CART). The first one (MLR) was chosen to explore the linear model. The second and third methods were used for testing because of the advantages they present, such as parallelization and reliability. For the MLR test, six variables were involved--the quality label as a dependent variable, and CO, NO\({}_{2}\), O\({}_{3}\), PM\({}_{10}\), and SO\({}_{2}\) as independent variables. Once the files were cleaned and filtered, 66,880 observations formed the data sample. The data-training group contained 70% of these observations, with a total of 46,816 observations. The MLR test presented an R-squared of 58.13%, not reaching the expected 80%, indicating that the data did not fit a linear function. For the SVM, two groups were formed: the training and testing groups. The first group contained 46,816 records, and the testing group 20,064 records, the values for the kernel parameter were rbfdot, radial, and hyperparameter sigma = 0.5. For both, the training error of 20% was obtained. The equivalent algorithm for both cases is represented by a mathematical equation in which the outputs are the Quality level; this virtually eliminates comparisons and decreases execution time. CART showed a root node error of 16974/66881 = 0.25379, reducing the comparison variables PM\({}_{10}\) and O\({}_{3}\). However, the model does not classify for level 5 because there are few entries with that label, so that specific output is not explained, as shown in Figure 4.
## 5 Discussion and Limitations
In future work, we would like to study user behavior by considering a more sophisticated driver profiling and contextual model, possibly based on semantic ontologies. Determining users' tastes and needs (profiling and classifying them) may help in identifying the products that users need before designing the sale of that product--in other words, by first stimulating demand and then by designing supply. It would, therefore, not be a question of selling products, but of satisfying the needs of individuals. Before finishing, it is important to indicate the limitations of the research work presented. Age and gender can be two factors that can condition the results and those variables have not been considered; nor has the driving style (the more aggressive it is, the higher the fuel consumption, for example). The socio-economic contexts of the survey participants have not been considered either. The number of people who participated in the survey samples, although significant, could be considered too small to generalize the findings. Furthermore, we assume that the normalization process with the expense data may lead to an average bias, as we translated continuous values into concrete scalars for all the respondents. All these limitations must be considered and included in a subsequent analysis process to delve deeper into the study that is now being presented. Still, we believe that the depth of this study has been sufficient to be able to assert that instrumenting cars with technology can result in reductions of car maintenance and usage expenses, with important side-effects toward reducing accidents or pollution.
## 2 Mechanisms Specific to QoS and Multicast in Ad Hoc Networks
### Mechanisms for Resource Estimation
#### 2.5.1 Mechanisms for Estimation of Bandwidth in Contention-free MAC Ad Hoc Networks  
The protocols in the survey use two types of contention-free MAC sub-layer, TDMA (Time Division Multiple Access) and CDMA-over TDMA. The CDMA (Code Division Multiple Access) system employs spread-spectrum technology and a special coding scheme to allow multiple users to be multiplexed over the same physical channel. CDMA can be overlaid on top of the TDMA infrastructure. In other words, multiple sessions can share the same TDMA slot via CDMA. In CDMA-over TDMA, the use of a time slot on a link only depends on the status of its one-hop neighboring links.  
Four protocols in [21, 22, 38, 50] use TDMA while two protocols in [23, 24] use CDMA-over TDMA for their sublayer. Protocols in [21, 38] assume that the available bandwidth of a link is available from the underlying layers. QoS-MEM [22] uses bandwidth-constrained multicast tree to formulate the QoS-aware Minimum Energy Multicast problem as a Mixed Integer Linear Programming model in a TDMA-based ad hoc network. A suitable scheduling of free slots for each link of the multicast tree can be obtained from this model. In [50], both the hidden-terminal and exposed-terminal problems are taken into consideration in order to possibly exploit the time-slot reuse capability. The hexagonal-based scheme offers a higher success rate for constructing a QoS multicast tree due to the use of the hexagonal-tree structure. A hexagonal-tree is a tree whose sub-path is a hexagonal-path, which is a special two-path structure.  
In [23], timeslot reservation is used as a primitive operation for constructing the lantern-path and lantern-tree. The lantern is identified after collecting the local link-state information for all nodes. The lantern-tree then is constructed by the lantern-path search operation and the lantern-tree construction operation. The timeslot assignment algorithm on a path in [24] follows the method proposed in [48]. In this protocol, the available bandwidth of a link is the number of free timeslots over the link. The available bandwidth of a path is the minimum bandwidth of the links along the path. The available bandwidth of a tree is the minimum bandwidth of the paths on the tree.
## 5 Experimental Results
### Geometric Attacks
#### 5.3.3 Scaling Attack  
In an image scaling attack, the intruders try to manipulate the image without knowing by downscaling and upscaling. The image is downsampled twice in the proposed system to test the robustness.
## 3 Results  
### Cytotoxicity  
There was a significant (\(p\leq 0.01\)) concentration-dependent reduction in the frequency of viable cells after exposure as well as the recovery period (Table 1). The decline at 6.3, 12.6, 25.2 and 60 ug/L was 4.9, 6.3, 14.43 and 17.82% after exposure and 1.42, 6.96, 8.69 and 12.65% after the recovery period, respectively. The necrotic and apoptotic cell frequencies, on the other hand, increased significantly (\(p\leq 0.01\)) in a concentration-dependent manner after exposure and recovery. After 14 days of exposure, the increase at 6.3, 12.6, 25.2 and 60 ug/L was 34.28, 45.71, 116.14 and 151.43% for the necrotic cells and 83.40, 102.43, 199.59 and 223.89% for the apoptotic cells, respectively. Percent increase over control after recovery was 9.61, 28.09, 47.45 and 91.12% for necrotic cells and 14.74, 104.67, 109.58 and 122.85% for the apoptotic cells at 6.3, 12.6, 25.2 and 60 ug/L, respectively. After recovery, percentage of viable and apoptotic cells increased significantly (\(p\leq 0.01\)) over exposure values only at 60 and 12.6 ug/L, respectively, while that of necrotic cells declined significantly only at the highest concentration.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Treatment** & & **Viable Cells (\%)** & **Nexrotic Cells (\%)** & **Apoptotic Cells (\%)** \\ \hline \multirow{3}{*}{Control} & E & 90.53 \(\pm\) 0.11  & 2.47 \(\pm\) 0.42  \\ \cline{2-5}  & R & 89.07 \(\pm\) 0.64  & 6.87 \(\pm\) 0.42  \\ \hline \multirow{3}{*}{Acetone} & E & 90.67 \(\pm\) 0.90  \\ \cline{2-5}  & R & 89.00 \(\pm\) 0.40  & 7.07 \(\pm\) 0.30  \\ \hline \multirow{3}{*}{6.3 ug/L} & E & 86.07 \(\pm\) 1.51  \\ \cline{2-5}  & R & 87.80 \(\pm\) 0.35  & 7.53 \(\pm\) 0.50  \\ \hline \multirow{3}{*}{12.6 ug/L} & E & 84.80 \(\pm\) 0.60  & 10.20 \(\pm\) 0.40  \\ \cline{2-5}  & R & 82.87 \(\pm\) 1.94  & 8.80 \(\pm\) 1.44  \\ \hline \multirow{3}{*}{25.2 ug/L} & E & 77.47 \(\pm\) 1.92  & 15.13 \(\pm\) 1.40  & 7.40 \(\pm\) 0.53  \\ \cline{2-5}  & R & 81.33 \(\pm\) 2.05  & 8.53 \(\pm\) 0.50  \\ \hline \multirow{3}{*}{6.3 ug/L} & E & 74.40 \(\pm\) 1.83  & 17.60 \(\pm\) 1.44 [FOOTNOTE:4]Footnote 4: Values are mean \(\pm\) SD, n = 3, and different alphabets represent significant
# Moving Towards Body-to-Body Sensor Networks for Ubiquitous Applications: A Survey
## 3 BBN Design Considerations
### Security Policies for BBN
Intended to scale to high-density networks, connecting a wide range of heterogeneous devices, CodeBlue places the security issue on the spotlight of interest and implements a flexible and decentralized security model that allows the emergency medical technicians to be authenticated by the network before receiving patient information. A handoff mechanism is also performed to ensure transparent authentication when the patient is transferred to the hospital. The Biosec project [81] also addresses the security constraints of the communications among biosensors operating in extremely stringent environments. The idea is to derive biometric inputs from the human body for the security module. Therefore, keying material is computed using biometrics from the body, which would drop the security overhead for the benefit of reliable sensing and energy saving. Another interesting proposal could be found in [82], which consists of a lightweight authentication scheme based on zero-knowledge proof (ZKP) [83] for WBANs, implemented on TinyOS-based sensors, referred to as TinyZKP. Originally, ZKP is a cryptographic protocol with low computational requirements and simple key management. TinyZKP mechanism relies on the cryptographic strength of ZKP. Thus, with such authentication protocol, it is impossible to inject malicious data within the communication between the base station and the authenticated WBAN. Another recent study, BANZKP [84], also implemented ZKP authentication for WBANs, together with a commitment scheme to deal with replay attacks. BANZKP has been proved to be more efficient than TinyZKP in terms of memory requirement, execution time and energy consumption.
# A Model and Quantitative Framework for Evaluating Iterative Steganography
## 2 A Model for Iterative Steganography
### Encoding and Decoding Iterations  
From the set of all iterations in the cover and the set of all iterations in the steganogram, we select subsets using coding indices, referred to as the _encoding_ and _decoding iterations_ (refer to Equations (9) and (10)).  
\[c^{I}=\{c_{i}:i\in I\} \tag{9}\]  
\[s^{I}=\{s_{i}:i\in I\} \tag{10}\]  
where  
\(c^{I}\)--the encoding iterations, represented as a subset of all cover iterations and selected using the coding indices, \(I\);  
\(s^{I}\)--the decoding iterations, represented as a subset of all steganogram iterations and selected using the coding indices, \(I\).