esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper consists of a retrospective study that includes a total of 186 cases with facial skin tumors diagnosed and treated between January 2015 and December 2017. Our aim was to correlate the initial clinical diagnosis with the histological results, in order to observe the success rate of our clinical experience, but also to underline that certain clinical tumor aspects can be misleading as the histological findings can result in different final diagnostics. From the total number of cases, we counted a total of 226 tumors (both benign and malignant), from which 194 had the clinical diagnosis confirmed by the anatomopathological exam and 34 had a different laboratory result from the clinical presumption: 10 benign tumors and 22 malignant tumors (eight BCC and 14 SCC, 12 of the latter being confused with ulcerovegetant BCC). Detailed correlation data were presented, as well as different situations of incongruence between the clinical and the histological diagnosis. The long-term goal of the study was to use our experience of correlating the clinical and the histological diagnosis, in order to improve the existing knowledge on the clinical differential diagnosis of both malignant and benign facial skin tumors.  
facial skin tumors; malignant skin tumor; basal cell; squamous cell; clinical diagnosis; histological diagnosis +
Footnote †: journal: Medical Imaging  
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**C.O.  
cutaneous squamous cell carcinoma (CSCC) and [PERSON]'s disease. The high number of NMSC cases pose economic questions regarding spending and loss. Age, gender, ultraviolet light, fair skin, ionizing radiation, immunosuppression, previous skin cancer or premalignant lesions are common associated risk factors.  
Facial tumors are the most commonly addressed localization of tumors in patients treated by plastic surgeons, with Non-melanoma skin cancer (NMSCs) representing 99% of all presented cases. The incidence of both MM and NMSC is on the rise, with annual increase in MM of 0.6% among adults over 50 years [2]. In this present study, due to the high incidence of facial NMSC compared to MM and the miscorrelation between clinical diagnostics and histopathology, we evaluated and tried to define a series of clinical criteria for NMSC diagnostics and the correlations with the pathology exams.  
BCC, the most prevalent type of NMSC, is located with predilection on the nose and can be addressed by incisional biopsy. Treating BCC can be fulfilled by non-excisional or excisional means, the choice of treatment being dependent on the risk factors.  
CSCC is the second most frequent NMSC and aside from BCC, it can metastasize to the regional lymph nodes. Burn wounds and chronic non-healing wounds can transform into CSCC. Treatment is based on the pathological report [3].  
Finding a correlation between clinical aspects, risk factor parameters and type of tumor confirmed by pathology can aid in correct diagnostic, proper excision limits and early reconstruction of post-excisional facial defects.
## 3 Results
### Shielding Effectiveness Evaluation  
The transmission loss of the empty waveguide, the pristine resin and samples with 10 wt.% and 20 wt.% of sludge biochar are shown in Figure 9 (left panel). It can be seen that the addition of biochar reduces the value of transmission. The shielding effectiveness values for the biochar composites are shown in Figure 9 (right panel). For a composite sample with 10 wt.% biochar, the SE is almost 5 dB, which increases to 10 dB for a sample with 20 wt.% biochar. In order to achieve a further increase in SE, the biochar weight percentage can be increased. The thickness of the sample in this case is 4 mm; an increase of thickness can result in a further increase of the SE.  
As reported in Section 2.2, the shielding effectiveness can be split between the reflective and dissipative losses. In Figure 10, the reflective loss, dissipative loss and the total shielding effectiveness for the composites with 10 wt.% and 20 wt.% are reported. It can be seen that the dissipative losses increase with the increase in the filler weight percentage.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Feedstock Used to** & **Pyrolysis Temperature** & **DC Electrical Conductivity @ 1500 Bar** & **Reference** \\
**Produce Biochar** & [°C] & **(Order of Magnitude) [S/m]** & \\ \hline \hline Miscanthus & 650 & 0.3 & \\  & 700 & 2.2 & [22] \\  & 750 & 4 & \\ Coffee & 600 & 0 & \\  & 800 & 0 & [23] \\  & 1000 & 300 & \\ Carbon black & Commercial product & 1700 & [23] \\ Sewage sludge & 1000 & 360 & This work \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between conductivity of different biochar produced in a wide temperature range compared with commercial carbon black.  
Figure 9: Transmission loss versus frequency of composites with thermal annealed sludge biochar (**left**). Shielding effectiveness of biochar composites (**right**).
# Study on Seismic Attenuation Based on Wave-Induced Pore Fluid Dissolution and Its Application
###### Abstract
In order to combine well logging and seismic data, it is necessary to select an appropriate rock physical model for dispersion calibration, thereby improving the accuracy of well-seismic matching. Because the parameters of a rock physical model are large, difficult to obtain and have no actual physical significance, it is better to use a viscoelastic model instead of a rock physical model. The [PERSON] model can fit the mesoscale White model [17], the Biot-Rayleigh model [18] and the dispersion and attenuation at two scales (meso and micro) [19]. Some researchers feel that the Cole-Cole model can more accurately simulate the acoustic properties of porous media than the rough approximation of [PERSON]'s mechanical model [20; 21], but there are five parameters, which is relatively more, while the Power Law model only needs two parameters [22], which makes it easier to use. Ref. [10] studied the seismic attenuation in Berea sandstone saturated with bidirectional fluid, compared the attenuation curves of the WIFF model and the pore fluid dissolution model under different saturation and pressure, and used SLS model to fit, and they found that the fitting effect with pore fluid dissolution is very good. Some scholars also compared multiple viscoelastic models. [PERSON] compared the attenuation and frequency relationship curves of Maxwell Model, Voight Solid, Standard Linear, Burgers Solid and Power Law. It was found that attenuation and elastic stiffness curves vary considerably with frequency, and each model has a different variation law [23]. [PERSON] et al. compared eight models using the zero-offset vertical seismic profile (VSP) dataset: the [PERSON] model, Power Law model, [PERSON] model, [PERSON] model, [PERSON] second model, [PERSON] third model, [PERSON] model and standard linear-solid-state model (SLS). It was found that in the same depth region, the SLS model has the best results in simulating attenuation [24].
# Recent Advances in Bacterial Persistence Mechanisms
## 5 Recent Progress of Technologies on Persister Studies  
As the formation mechanism and treatment of persisters are still under study, there is no doubt that the current study methods and techniques will be further improved. Nowadays, physiological studies of persisters mainly depend on a time-lapse microscope or fluorescence-activated cell sorting (FACS) [84]. Although the basic properties of persisters have been explored using these techniques, they are low-throughput and time  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Strategies** & **Targets/Options** & **Examples** \\ \hline \multirow{4}{*}{Directly target the existing persisters} & Bacterial membranes and cell walls & AM-0016 [72], boromycin [73] \\  & Drug combination & Daptomycin combined with \\  & & ceftonline [74] \\  & Anti-cancer drugs & Mitomycin C [75] \\ \hline \multirow{4}{*}{Block persister formation} & SOS response & Engineered bacteriophage [76] \\  & Biofilm formation & Quercetin [78], Dispersin B [79], \\ \cline{1-1}  & & peptide 1018 [80] \\ \cline{1-1}  & Before the infection & Vaccination [4] \\ \hline \multirow{2}{*}{Resuscitate persisters} & Signaling pathways & Cis-2-decenoic acid [81] \\ \cline{1-1}  & Stimulate antibiotic influx & Silver [83] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Strategies for the treatment of persistence.  
consuming, which restricts their application in persister phenotypic studies. Therefore, to further analyze persister physiology and heterogeneity, scientists have developed a high-throughput technology that combines next-generation sequencing and FACS, named Persister-FACSeq [85]. Persister-FACSeq parallelizes the current FACS approach with a fluorescent reporter library. A positive control strain, holding a promoter that gives distinct normal cell and persister distributions and can be distinguished from other reporter strains easily when plated, was introduced. It was used to confirm the anticipated distribution and is seeded into each sample at equal abundance to facilitate cross-quantile comparisons. Thus, it acts as an internal control to normalize the gene expression distributions after calculating the promoter read abundances in each antibiotic-untreated and -treated sample. It not only retains the original advantage of not needing to isolate persisters from bacteria, but also would not be restricted by the choice of the fluorescent protein. Carrying fluorescent reporters is the only prerequisite. Another research group combined the flow cytometry using fluorescence and a confocal laser scanning microscope to study the formation of _P. aeruginosa_ persistent cells under different antibiotics: ceftazidine, gentamicin, and ciprofloxacin [40]. The outcome demonstrated that those treated with ceftazidine displayed a higher persistence in the planktonic stage, and all three antibiotics could induce persistent cells during the biofilm stage.
# Performance Evaluation of Mobile RPL-Based IoT Networks under Hello Flood Attack
## 6 Performances Analysis
### The Pursue Mobility Model (PMM)
#### 6.4.4 Expected Transmission Count  
Figure 20 offers a detailed examination of the ETX metric in a mobile network environment utilizing the PMM model, particularly focusing on variations in node count and the presence of attacker nodes. Notably, as the number of nodes increases, there is a corresponding increase in ETX values, indicating an augmented transmission count is required for successful packet delivery as the network expands. This pattern persists regardless of changes in the percentage of attacker nodes, emphasizing the consistent influence of network size on transmission count. Moreover, the presence of attacker nodes amplifies ETX values, signaling a decline in overall link quality and reliability. The graph shows how network density and attacker presence have a big effect on ETX values. It also shows how the PMM model changes network behavior, which makes it clear how important it is to think about mobility factors when evaluating and improving network performance.  
The PMM mobility model and the Hello Flood attack collaborate in various ways to raise the ETX (Expected Transmission Count). First, the PMM's structured movement patterns may cause the network to get crowded as nodes chase a target. This can cause more packet collisions and transmission delays, which raises the ETX. Additionally, the Hello Flood attack floods the network with excessive traffic, saturating channels and exacerbating interference, further delaying packet transmission and raising ETX. Secondly, the PMM's movement patterns may introduce routing instabilities, resulting in suboptimal routing decisions and increased packet loss. This is compounded by the attack's disruption of routing protocols, further amplifying ETX. Both the PMM and the attack can also cause more activity on the network, which can make it harder to obtain resources like bandwidth and processing power. This can cause transmission delays and raise ETX. Additionally, the attack may introduce additional processing overhead and congestion, further delaying packet transmission and raising ETX.  
Figure 19: Throughput without and with attack using PMM.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 3 Smart Meter Technological Solutions in Europe and Projects for Their Implementation
### Greece  
In Greece the first pilot project has been accomplished by the year 2015 and was led by Hellenic Electricity Distribution Network Operator (HEDNO). According to this program, around 160,000 smart meters are placed in selected geographical areas throughout the country, like Athens, Thessaloniki and the island of Lesvos. The main technologies that have been/will be used for the smart meters implementation are/will be the power line communications and wireless technologies like GPRS, GSM, and 3G. The selection of the equivalent technology depends on the geographical and population characteristics of each area [73].
# A Network Traffic Prediction Method for AIOps Based on TDA and Attention GRU
## 3 System Model
### Topological Data Analysis (TDA)
#### 3.1.2 Continuous Homology  
Continuous homology is a method used to measure the topological characteristics of shape and function. It can transform data into a simple complex and describe the spatial topology at different spatial resolutions [21; 22]. More persistent topologies can be detected on a wider spatial scale and are more representative of the real characteristics of the underlying space. The input of continuous homology is usually a point cloud or function [23], whereas the output depends on the nature of the analysis and is usually composed of a persistence graph or landscape.  
The calculation steps of topological features are summarized as follows:  
Step 1: Calculate the Euclidean distance matrix, \(D_{E}=\{D_{E}(v_{1,l},v_{2})\}\), \(i_{1}\), \(i_{2}\in\{1,\ldots,N\}\).  
Step 2: Construct the birth and death of homology groups for the added value of \(\lambda\).  
For each \(\lambda\), use the closed loop \(B_{\lambda}(v_{i})\) of \(v_{i}\) with radius \(\lambda/2\) and \(\widetilde{K}(\lambda)\) to calculate \(\widetilde{\alpha}_{\widetilde{p},k}\). If an older topology, \(\widetilde{\alpha}_{\widetilde{p},k1}\), and a younger topology, \(\widetilde{\alpha}_{\widetilde{p},k2}\), merge into a single \(\widetilde{\alpha}_{\widetilde{p},k}\) at some point of \(\lambda\), \(\widetilde{\alpha}_{\widetilde{p},k1}\) will become \(\widetilde{\alpha}_{\widetilde{p},k}\) and \(\widetilde{\alpha}_{\widetilde{p},k2}\) will die.  
Step 3: The persistence graph is the output of a group of points representing the birth and death relationships of homology groups in the point cloud, expressed as  
\[\widetilde{\Omega}=\left\{\widetilde{\tau}_{\widetilde{p},k}=\left(\lambda_{ \widetilde{p},k1},\lambda_{\widetilde{p},k,2}\right):\widetilde{p}=0,1, \ldots;k=1,2,\ldots,k_{\widetilde{p}}\right\} \tag{1}\]  
Finally, \(\lambda_{\widetilde{p},k1}\) is drawn on the \(x\)-axis and \(\lambda_{\widetilde{p},k2}\) on the \(y\)-axis.
###### Abstract  
Hundreds of medications commonly prescribed for anticancer treatments and some infections are known to cause hearing damage, referred to as ototoxicity. Preventing or minimizing ototoxicity is critical in order to preserve quality of life for patients receiving treatment and to reduce the societal burden of hearing loss. Current clinical evaluations are restricted to a limited frequency range (\(\leq\)8 kHz); however, this approach does not permit the earliest detection of ototoxicity, most likely to be observed at the highest frequencies (9-20 kHz). Distortion product otoacoustic emissions (DPOAEs) offer a noninvasive, objective approach to monitor cochlear health in those unable to respond via conventional methods. The current report analyzes different DPOAE paradigms used in patients undergoing chemotherapy treatments with various platinum derivatives. Individualized serial monitoring protocols were completed at the highest frequencies with measurable DPOAEs. This allowed the exploration of potential clinical translation opportunities for further quantification of the earliest signs of underlying cochlear damage, which may go undetected with conventional methods. Clinical practice has the potential to be enhanced by emerging DPOAE applications, including targeted monitoring protocols and high-frequency stimuli to assess cochlear function, especially at the highest frequencies, and advanced calibration techniques to ensure the stability of serial measurements.  
distortion product otoacoustic emissions; serial monitoring; hearing loss; normal hearing; ototoxicity; cisplatin; carboplatin; oxaliplatin Article
## 3 Methodology
### Data Description
#### 3.1.1 UNSW-NB15 Dataset (D1)  
The UNSW-NB15 dataset _(D1)_, developed by the Australian Cyber Security Centre [46], is divided into separate training and testing sections. There are 175,341 records in the training set and 83,332 records in the test set. Records in _D1_ are categorized into two main groups: normal network traffic and different categories of cyberattacks. _D1_ consists of 44 features, along with two class labels: the attack category and label. In the _D1_ dataset, there are ten classes in total: one represents normal traffic, while the other nine correspond to various attack types, including Exploits, Worms, Analysis, Fuzzers, Backdoors, Reconnaissance, Generic, DoS, and Shellcode. The class distribution is presented in detail in Table 2.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Category of Attack** & **Instance for Training** & **Instance for Testing** \\ \hline Fuzzers & 18,184 & 6062 \\ Generic & 40,000 & 18,871 \\ Reconnaissance & 10,491 & 3496 \\ Analysis & 2000 & 667 \\ Exploits & 33,393 & 11,132 \\ Backdoor & 1746 & 583 \\ Worms & 130 & 44 \\ Denial of Service (DoS) & 12,264 & 4089 \\ Shellcode & 1133 & 378 \\ Normal traffic & 56,000 & 37,000 \\ Overall total (instances) & 175,341 & 82,332 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The class distribution in the UNSW-NB15 dataset [47].  
Figure 1: The proposed method.  
In dataset \(D1\), the 'Worms' class, which contains 174 records, constitutes the minority class, while the 'Normal' class, with 93,000 records, represents the majority class. Equation (1) outlines the formula used to calculate these imbalance ratios.  
\[\text{Imbalance Ratio}=\frac{\text{\emph{Count of instances in majority class}}}{\text{\emph{Count of instance in minority class}}} \tag{1}\]  
As illustrated in Figure 2, dataset \(D1\) exhibits a significant imbalance. Specifically, the 'Worms' class is outnumbered by the 'Normal' class, which appears more than 534 times as frequently.
## 7 Implementation and Evaluation  
We implemented the service-oriented architecture introduced in Section 4 to realize video adaptation on-the-fly during streaming the media content through the network.  
Web service frameworks offer a set of standards for service-oriented architectures. On the whole we relied on standard components. Since we implemented our framework in Java, we chose the Apache CXF Web service framework to ease the Web service creation. Since Web services are stateless and multimedia data is continuous we additionally implement a session id concept.
## 3 Results
### Sentinel-2 (Synthetic)  
We applied eight existing and five new algorithms in the form of 23 variants to the 20-m, synthetic Sentinel-2 imagery. Sentinel-2 lacks a narrow band capable of measuring the 620 nm phycocyanin absorption feature. The performance of each algorithm in Tables 2 and 3 applied to the synthetic Sentinel-2 imagery was evaluated using 29 coincident surface observations coincident surface observations chosen to avoid pixels that mixed land and water at 20 and 30 m spatial resolutions (Tables S2 and S4). Six algorithms, S2 Be162 Bsub, S2 SiO52 BDA, S2 Am092 Bsub, S2 MM12 NDCI, S2 Mi092 BDA, and S2 Be16 NDphyI, had acceptable performance ([PERSON]'s r\({}^{2}\)\(>\) 0.6; \(p\)\(<\) 0.001) with this sensor in this experiment. The S2 Be162 Bsub, S2 SiO52 BDA, S2 MM12 NDCI, S2 Mi092 BDA, and S2 Be16 NDphyI algorithms also appear to have good portability between CASI, WorldView-2/-3 and Sentinel-2 imagery (Tables S2 and S4 and Figure 9). The strong performance of these algorithms with synthetic Sentinel-2 imagery and their wide swaths and dual constellation suggest that Sentinel-2 satellites will play a key role in future BGA monitoring systems for inland water quality. Index imagery with raw and normalized index Type 1 (Pearson's r) linear regressions are shown for the best performing algorithm (S2 Be162 Bsub) in Figure 9.  
Figure 9: _Cont._
## 4 Results and Discussion
### Feature Scoring  
Table 5 provides an insightful comparison of the top 10 features obtained by the Chi2 and Gini selection models and their relevance in malware detection. Each API, based on its scoring and categorisation, plays a unique role in identifying and classifying malware behaviour. In the comparison, NtAllocateVirtualMemory and NtProtectVirtualMemory emerge as key APIs, with Chi2 assigning them scores of 10.18% and 9.70% and Gini assigning 6.81% and 7.86%, respectively, indicating their significant role in kernel management. Involvement in memory allocation and protection is a common characteristic of advanced malware, which often manipulates memory to execute malicious code or evade detection. Their high relevance in both models shows their critical role in identifying malware that interacts with system memory. LdrGetProcedureAddress and NtFreeVirtualMemory, categorised under system and kernel management, respectively, show notable differences in their Chi2 and Gini scores, reflecting their varied impact in these specific categories. This disparity indicates their varying impact on malware operations, such as loading procedures and freeing memory resources, which are essential for understanding malware's interaction with a system's core processes. Other APIs, such as FindResourceExA, NtClose, and GetSystemTimeAsFileTime, despite having lower Gini scores, are still significant in their respective categories of resource, kernel, and system information management. They are involved in resource location, system resource management, and time-based operations, respectively. Their inclusion, even with lower scores, highlights their role in specific malware activities, such as resource exploitation and time manipulation. The lowest-scoring features, NtDelayExecution and NtDeviceInControlFile, categorised under system and file system management, were still recognised within the Chi2's and Gini's top 50 rankings, highlighting their relevance in these specific API categories. NtDelayExecution is involved in delaying execution, a tactic used by malware to avoid detection during analysis, while NtDeviceInControlFile deals with device I/O operations, which can be critical in understanding malware's interaction with hardware components. The strategic selection and scoring of these APIs contribute significantly to the effectiveness of malware classification systems. By understanding the specific roles and impacts of these APIs, malware detection systems can be tailored to recognise and respond to a wide range of malicious activities.  
Figure 2: Top 50 API categories according to Chi2 and Gini.