esa-sceva/satcom-chunk-collection huggingface dataset:
# An Investigation of Optically Very Thin Ice Clouds from Ground-Based ARM Raman Lidars
## 4 Results  
### Ice Cloudy Optical Depth Distribution  
Figure 3 shows the cumulative distribution functions (CDFs) of ice cloud column optical depths for transparent profiles when ice clouds are detected for the RL and CALIPSO. The dashed cyan line for \(\tau_{i,c}\) = 0.01 highlights the focus of this study on the distribution of transparent ice cloud column optical depths below this threshold. It is seen that at both sites, more than 20% of transparent ice cloud profiles from the RLs have \(\tau_{i,c}\) < 0.01, but only a few percent of those from CALIPSO have \(\tau_{i,c}\) < 0.01. The RL observes more transparent ice cloud column optical depths below 0.01 than CALIPSO at both SGP and TWP, indicating the higher sensitivity of the RL in comparison to CALIPSO. In Figure 3, the percentages of the transparent profiles from the RLs and CALIPSO are not forced to be the same. By using consistent resolutions between the RL and CALIPSO and noting a small effect of the transparent definitions, the differences in the CDFs between the RL and CALIPSO are mainly related to the differences in instrument sensitivity [8]. The different lidar ratios used also play a role in these differences. Considering transparent profiles that are not necessarily temporally collocated might also contribute. Table 1 lists the number of profiles for all time, transparent, ice cloud transparent, and ice cloud transparent with ice cloud column optical depths below 0.01, 0.005, and 0.001 for the RLs and CALIPSO at SGP and TWP. The RL always observes more profiles of optically very thin ice clouds relative to total ice cloud transparent profiles in comparison to CALIPSO.
## 4 Results
### Laboratory Measurements Results
#### 4.2.2 XRD Analysis  
The qualitative mineral composition of samples SL1-3, KG1-14, and KG2-8 was determined by analyzing the powder diffraction patterns (XRD), as shown in Table 1. The mineral abundance was determined semi-quantitatively. In sample SL1-3 taken from the Slov\(\acute{\text{a}}\)cka Cave profile, the most abundant minerals are 14 angstrom (\(\AA\)) phyllosilicates. Additionally, kaolinite, quartz, calcite, goethite, and opaque minerals are present in significant amounts. Gibbsite and an amorphous component are minimally represented in the sample. It is assumed that hematite, anatase, chlorite, potassium feldspars, and irregular mixed-layer minerals are also present. Their proportions are very small, and their presence cannot be reliably confirmed.  
From the first profile measured in the Crnopac Cave System, sample KG1-14 was selected for X-ray powder diffraction analysis. Calcite predominates in composition (see Table 1). Significant amounts of 14 \(\AA\) phyllosilicates (vermiculite, smectite, mixed-layer minerals), kaolinite and/or chlorite, opaque minerals, and quartz were observed. Additionally, minor amounts of goethite, gibbsite, an amorphous component, and an unidentified mineral X2 are present in this sample. Some minerals (siderite, potassium feldspars, hematite, anatase, magnetite, and irregular minerals) are also considered as potentially present, but they cannot be certainly identified.  
On the second profile in the Crnopac Cave System, sample KG2-8 was chosen due to its suitable texture for analysis using this method. Quartz is predominant in the composition of this sample. Goethite and/or hematite, along with an unidentified mineral X1, are less abundant minerals in this sample (see Table 1). Plagioclases, potassium feldspars, opaque minerals, kaolinite and/or chlorite, and the amorphous component were presumed to be present in the mineral composition of sample KG2-8, but without additional analyses, their presence cannot be reliably asserted.
## 3 Description of Datasets  
The proposed technique is evaluated using the CWRU and the SUSU datasets, which consist of real data obtained from experiments, rather than simulated data.  
* The CWRU dataset was provided by Case Western Reserve University's Bearing Data Center [45] and has been used extensively in rolling bearing fault diagnosis. CWRU vibration data were collected using accelerometers, which were attached to the housing of a mechanism. This is the traditional approach for collecting diagnostic information from such technological processes.
* The SUSU dataset [30] was provided by South Ural State University and obtained using a sensor mounted directly on the shaft. This method of mounting potentially provides increased sensitivity to defects. Both datasets are described in more detail in Sections 3.1 and 3.2. For each time series, the first 70% of samples were used to create the training set, and the remaining 30% were used to create the corresponding test set, so that the training and testing time series did not overlap. The validation data were 10% of the test data.
# Extensive Experiments on Opportunistic Routing in Wireless Sensor Networks
## 1 Introduction  
The critical issue addressed in WSNs often relates to energy efficiency. Solutions have been proposed for fixed patterns of communication, such as collecting low frequency sensor measurements [1]. However, another opposite communication pattern is the bulk transmission characterising for instance the family of Wireless Multimedia Sensor Networks (WMSNs) [1; 2; 3; 4]. The latter have shifted the focus from the typical scalar WSNs to networks which deliver multimedia traffic as video, audio, still image in addition to scalar data. The promising application fields are numerous [1], such as smart cities including areas monitoring, missing persons locating, etc.  
Inspired by the new challenges of WMSNs, we propose ODYSSE (Opportunistic Duty cYcle based routing protocol for wireless Sensor nEtworks), a distributed and adaptative routing protocol, which can both handle infrequent and bulk traffic. Our objective is to minimize energy consumption and hence maximizing network lifetime while maintaining low end-to-end delays and overhead. Hereafter, we will introduce the main components of our proposal.
## 2 Ultrasonic Pulse Velocity Test  
### P-Wave Velocity (Vp) Measurement  
The pulse velocities of pressure waves have been applied to measure the elastic modulus, strength and defects of concrete, by measuring a reflection wave or the velocity of an elastic wave in concrete [1]. The ultrasonic pulse velocity measurement can be used to assess the concrete quality characteristics, such as the density and uniformity, and to identify the presence of voids and cracks [24]. As the ultrasonic pulse velocity increases, the likelihood for the concrete to possess high quality (e.g., possessing no cracks), uniformity, and the designed density, is higher. Typically, this test uses an ultrasonic pulse with a 50-kHz center frequency transmitted from an electro-acoustic transducer that is placed in contact with one surface of the concrete. After passing the ultrasonic pulse, the other transducer placed on the opposite side detects the pulse. The elapsed time between both transducers is measured in milliseconds. Using the known traveling distance, D, and the measured travel time, T, the pulse velocity (V = D / T) can be calculated [25; 26]. The ultrasonic tests in this study were based on the indirect method, as shown in Figure 1.  
Using the test grid shown in Figure 2, the average velocity between two points in rows A-F was measured for each column (1-9). The \"o\" symbols denote the positions of the transducers, and the \"x\" symbols denote the average positions between adjacent points. In this test, access to the bottom surface was difficult, and indirect measurements were used on the surface. Six measurements were made for each column, and the average value was assumed to be the average P-wave velocity for this column.  
Figure 1: Ultrasonic pulse velocity test method. (**a**) Ultrasonic equipment; (**b**) Procedure of P-wave measurement.  
For verification, a cylinder core (10 cm \(\times\) 20 cm) in each column was extracted, and the compressive strength was measured according to the ASTM C 39 standard and used for comparative analysis. A total of 54 P-wave measurements were made, and nine specimens were taken for the compressive strength evaluation on each slab.  
Because the surface of the concrete was not smooth, couplants, such as Vaseline, were used to facilitate the transmission of energy between the transducer and the surface. The experiments were carried out by two people. One person placed an ultrasonic sensor at each point, started it, and read the result. The other person recorded the results on a printed grid. A total of six slabs were tested in the same manner.
## 4 Implementation and Discussion
### Performance Analysis
#### 4.2.6 Quality of Treatment  
As the proposed scheme allows patients to seek opinions from several healthcare stakeholders before making a medical decision, this upgrades the overall standard of treatment many times.
# Deep Anomaly Detection for In-Vehicle Monitoring--An Application-Oriented Review
## 2 Literature Review on Deep Anomaly Detection  
Anomaly detection in videos is commonly framed as a one-class classification task. Such a strategy requires a training set strictly composed of normal events, whilst the videos for testing represent normal and abnormal events. This approach can be defined as a semi-supervised strategy, which is a term regularly used in the categorisation of deep anomaly detection approaches [9]. Furthermore, weakly supervised techniques have also been referred to in the literature as a category for such approaches [10]. They differ from semi-supervised strategies by obtaining video-level labels, which allow for the training of the models using normal and abnormal snippets. As far as fully supervised strategies are concerned, the cost of collecting and labelling large-scale data for this purpose is unbearable. However, the inclusion of synthetic data and a focus on locality have been studied as assets to leverage the advantages of fully supervised approaches, introducing them to semi-supervised and weakly supervised strategies.
## 2 Background and Related Work
### Partitioning Algorithms
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Approach** & \multicolumn{2}{c}{**Reduce DNN to Fit**} & \multicolumn{2}{c}{**Loss of**} & \multicolumn{2}{c}{**Offload to**} & \multicolumn{1}{c}{**Partitioning**} & \\  & **Constrained Memory?** & **Accuracy?** & **the Cloud?** & **Type** & **Type** \\ \hline Big-Little [8] & Yes & Yes & Yes & per layers & ML IoT \\ DIANNE [15] & No & No & No & per layers & ML IoT \\ DeepX [16] & Yes & Yes & Yes & per layers & ML IoT \\ TensorFlow [14] & No & No & No & per layers ** & ML \\ DeepIoT [13] & Yes & No & No & N/A * & ML IoT \\ Li, Ota, and Dong [31] & No & No & Yes & per layers & ML IoT \\ DeepThings [32] & No & No & No & along the neural & ML IoT \\ Multifidelity [10] & Yes & Yes & No & N/A & ML IoT \\ [PERSON] et al. [30] & No & No & Yes & per neurons & IoT \\ \hline \hline \end{tabular}  
* Not applicable. ** To use implemented functions.  
\end{table} 
Table 1: Summary of ML and IoT tools discussed in the related work. not yield the best result for other objective functions such as inference rate. This solution is fast but sacrifices the bandwidth result. All the general-purpose approaches discussed so far in this subsection are edge-cut partitionings, i.e., the algorithms partition the graph vertices into disjoint subsets [38]. Another strategy to general-purpose graph partitioning is vertex-cut partitioning, which partitions the graph edges into disjoint subsets, while the vertices may be replicated among the partitions. [PERSON] et al. [39] proposed JA-BE-JA-VC, an algorithm that performs vertex-cut partitioning. Their approach attempts to balance the partitioning aiming to satisfy memory constraints. The main disadvantage of this approach is that it needs vertex replicas, that is, computation replicas, and synchronization, which may involve more communication. When we consider constrained IoT devices and their computational performance, the computation replicas may decrease the inference rate of neural networks to a value that does not comply with the application requirements. As this algorithm is for general purpose, it also does not eliminate redundant edges and does not account for the shared parameters and biases of CNNs adequately. The tools presented in this section may be useful for distributed execution of neural networks, although the ML frameworks do not present an automatic, flexible partitioning and the general-purpose partitioning algorithms do not treat memory restrictions, redundant edges, shared parameters, and biases properly. We summarize the partitioning algorithms discussed in this subsection in Table 2 with their main characteristics. The next section presents the proposed DN\({}^{2}\)PCIoT and discusses how we deal with these issues.
## 3 Technologies Supporting Intelligent Acoustic Monitoring
### Summary and Comparison  
The technologies described in this section demonstrate how the intelligent interplay between sensing hardware, signal processing methods, and AI algorithms shapes acoustic monitoring. On the one hand, we have diverse and highly specialised sensor types--from MEMS microphones to hydrophones and directional arrays--which differ in cost, deployment complexity, and target environment. On the other hand, the increasing use of edge AI and adaptive processing brings advanced analytical capabilities closer to the source of the signal. What emerges is a modular system where sound is not only captured but also intelligently processed and interpreted in near real time. Compared to earlier, more centralised approaches, modern architectures promote decentralisation, context awareness, and low-latency responses, especially in safety-critical or mobile applications.
# IoT Firmware Emulation and Its Security Application in Fuzzing: A Critical Revisit
## 4 State-of-the-Art Firmware Emulation and Its Application in Fuzzing
### Solutions to Peripheral Interaction Support
#### 4.2.1 High-Level Peripheral Interaction Replacement  
For Linux-based firmware, researchers can hook the Linux system call interface and redirect it to driver function replacements for full-system emulations, as seen in _Firmadyne_[21], _FirmAE_[22], and _ECMO_[23]. Inspired by this, although MCU-based firmware lacks a standard system call interface and has diverse OS kernels, researchers found that hardware abstraction layer (HAL) interface functions are more common. They proposed intercepting firmware calls to the HAL and replacing them with emulator functions, as performed by _HALucinator_[24]. However, this approach requires a library matching tool to identify HAL functions in the firmware and manual creation of replacement functions, limiting its applicability and increasing labor costs.
## 3 Results and Discussion
### Acute Immobilisation Test with Daphnia magna  
For the RE samples, a pH adjustment (between 6 and 7) was made for the highest concentrations (66.7% and 100%), such that the effect was due to the effluent composition and not because of the acidity of the sample. The RE was acutely toxic to _D. magna_, with EC\({}_{50}\) ranging from 24.18% (48 h) to 35.49% (24 h).  
In contrast, for the TE1, no significant acute effects were observed for _D. magna_ (F\({}_{7,31}\) = 1.00, \(p\) = 0.455). [PERSON] [4] evaluated the acute toxicity to _D. magna_ for three different textile effluents.  
Figure 2: Average growth rate (day\({}^{-1}\)) of _L. minor_ based on the frond number (**a**) and the dry biomass (**b**) exposed to different dilutions of the effluent. Error bars represent the standard deviation of the mean (n = 3); asterisks indicate statistically significant differences from the control ([PERSON]’s test \(p\) < 0.05).  
The effluent T1 came from an industry that used multiple dyes (disperse dye, acid and basic dyes, reactive dye and indigo dye), the effluent T2 contained reactive and vat dyes, and the effluent T3 contained reactive dyes only. Among the effluent samples studied by [PERSON] [4], the effluent T2 was likely the most similar to our sample (TE1) since it came from a process that also used vat dyes in the dying process. However, no toxic effects to \(D\). _magna_ were recorded for the TE1 sample, while the sample T2 showed an EC\({}_{50}\) of 62.97% after 48 h of exposure. The TE1 sample was not toxic, while the RE registered a low EC\({}_{50}\); this suggests that the treatment performed by the wastewater treatment plant of the industry was at least efficient in removing the acute toxicity to \(D\). _magna_. [PERSON] et al. [40] also tested the toxicity to \(D\). _magna_ of treated and untreated textile effluents from different industries, and although they recorded toxicity in all the samples, the treatment plant of the corresponding industries was able to improve the quality of the effluents, reducing their toxicity.
## 3 Results
### Structure Formation under Deep Cryogenic Treatment (DCT)  
The following cryogenic treatment had a significant effect on the microhardness of the structural components of steel (Figure 6a) and the amount of RA (Figure 6b). This indicates that the use of DCT in the heat treatment contributed towards the RA transformation into martensite. The increase in microhardness obtained for the 38 CrNi3 MoV steel samples was about 22.3%. Measurements of the content of RA both by the X-ray and ultrasound methods confirm its metastability and tendency towards transformation during DCT (Figure 6b).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Points** & **V** & **Cr** & **Mn** & **Si** & **Ni** & **Mo** & **Fe** \\ \hline
1 & 0.00 & 1.21 & 0.48 & 0.34 & 2.87 & 0.17 & 94.93 \\
2 & 0.06 & 1.02 & 0.21 & 0.18 & 3.11 & 0.69 & 94.73 \\
3 &  
The transformation of RA into martensite during DCT is fully confirmed by the electron microscopic investigation of the steel structure (see Figure 7).  
These microstructures clearly show how the RA in the form of globules (Figure 7a,b), needles (Figure 7c) and packets (Figure 7d) transformed into martensite of the corresponding morphology. Figure 7d shows, that one part of the austenite grain has turned into a martensite packet, and the other part has been preserved as RA.  
Increasing the exposure time of the samples at liquid nitrogen temperature had almost no effect on the microstructure of the samples observed using a light microscope (Figure 8a,b), but affected both the microhardness of the structural components and the content of RA. When held for 10 min in liquid nitrogen, the largest amount of RA was converted and the microhardness of the steel increased significantly (Figure 6a).  
Holding samples at the temperature of liquid nitrogen for a longer time contributed to the further transformation of RA, increased microhardness (Figure 6a), and the formation of finer martensitic laths [38,39] (Figure 8c,d). A possible mechanism for such a process will be discussed below.  
Figure 7: Microstructures of 38 CrNi3 MoV steel after cryogenic treatment for 10 min (large arrows—martensite, small arrows—retained austenite): (**a**,**b**) martensite in the form of globules; (**c**) needles; (**d**) packets.  
Cryogenic treatment of steel for 10 min followed by heating had practically no effect on the distribution of alloying elements in the structure (see Figure 9 and Table 2). The exception was vanadium, which became more isolated, presumably in carbide inclusions.  
Figure 8: Microstructures of 38 CrNi3 MoV steel samples after cryogenic treatment for 1 h (**a,c**) and 22 h (**b,d**).  
Figure 9: X-ray microanalysis of 38 CrNi3 MoV steel after isothermal heat treatment + DCT 10 min (see Table 2 for values 1–10).
## 2 Orexins  
Orexins A (hypocretin 1: 33 amino acids) and B (hypocretin 2: 28 amino acids) are excitatory neuropeptides (C-terminally amidated and showing 46% homology), produced in the lateral hypothalamus/perifornical region and are involved in energy homeostasis, feeding (promote food intake: orexin A has a higher action than orexin B), reward-related appetitive behaviors (mesolimbic pathway), glucose metabolism, motor function, sleep, and wakefulness (during sleep orexin neurons are silent) [14; 15; 16; 17] (Table 1). Figure 2: Relationships between different hypothalamic and extrahypothalamic regions involved in the control of feeding. Orexinergic neurons of the lateral hypothalamus send direct projections to other hypothalamic nuclei (posterior hypothalamus), different regions of the brainstem and spinal cord as well as forebrain structures and receive inputs from the median raphe or the ventromedial hypothalamus. Green arrows represent direct activation exerted from the orexinergic neurons to thalamo-cortical neurons, the ventral tegmental area or the NPY/AgRP neurons of the arcuate nucleus, and the red arrow represents the inhibitory projection from orexigenic neurons to POMC/CART neurons of the arcuate nucleus. Abbreviations: Acb—acumbens nucleus; Arc—arcuate hypothalamic nucleus; LC—locus oceruleus; LH—lateral hypothalamus; NPY/AgRP—neurons of the arcuate nucleus containing neuropeptide Y and agouti-related peptide; PAG—periaqueductal gray matter; POMC/CART—neurons of the arcuate nucleus containing pro-opiomelanocortin and cocaine- and amphetamine-regulated transcript; PVH—paraventricular hypothalamic nucleus; VTA—ventral tegmental area. Orexin A increases food intake, especially diets that are rich in saturated fat [34] (Table 1), whereas orexin B is less stable and lipophilic than orexin A, which is consistently observed in the cerebrospinal fluid of healthy human individuals [15]. It is known that the orexinergic system stimulates the hypothalamus-pituitary-adrenal axis and that this system regulates intestinal motility and insulin secretion [14] (Table 1). Orexin was also observed in the enteric nervous system and endocrine cells in the gut [20] and it is known that orexin A decreases tumor development [21], exerts an excitatory effect on hippocampal neurons through the orexin 1 receptor [22], and is involved in immunomodulatory mechanisms (e.g., by decreasing the level of pro-inflammatory cytokines) [20] (Table 1). It was reported