esa-sceva/satcom-chunk-collection huggingface dataset:
# SIMECK-T: An Ultra-Lightweight Encryption Scheme for Resource-Constrained Devices
## 2 Methodology
### Implementation  
As with SPECK-R, SIMECK-T is implemented to operate in block counter mode [59]. An initialization vector (IV) is obtained by initializing the Rand1 pseudorandom generator from [60] using the file size, the filename of the output file and the modification time of the source file as the seed. The Fowler-No-Vo 1a hash on 64 bits is used to convert the filename and the file modification time string into numbers [61]. The generator is queried a number of \(psum+pmul\) times, where these two values depend on the derived password obtained using a password-based key derivation scheme. This makes it improbable for files with the same filename and the same file size to make use of the same pair of (\(IV\), \(password\)) while using the same modification time attribute. The IV is used as a starting value to set the cipher in counter mode--line 4 in the Algorithm 1. SIMECK-T from Algorithm 1 uses LROT32 and ROUND64 functions from SIMECK [62]. Lines 11-12 depend on the hash of the password. The constants \(psum\) and \(pmul\) are produced based on the sum and product of the bytes in the derived key, respectively, on the XOR of its byte values. This means that the SIMECK-T algorithm is tweaked so that an attacker cannot know upfront the number of external SIMECK rounds and the number of internal TEA rounds. In order to validate the proposed encryption algorithm against other suitable ciphers for resource-constrained devices, a selection of algorithms that are well known for their simplicity and efficiency are modified using the strategy already proven by the authors of SPECK-R [19]. The encryption primitives are combined with dynamic substitution boxes which are designed to retain the same cryptographic properties for the cipher but without having to use the full number of rounds. SPECK-R authors use RC4 as a key scheduling algorithm (KSA) to produce 3 substitution boxes [19]. This algorithm is known to contain vulnerabilities as described in [63; 64; 65; 66]. In order to mitigate for that, it was replaced by a secure version called RC4D [20; 22]. Three substitution boxes are used exactly as shown in [19], but where applicable, the full expanded key is used because the expanded keys are important for the SPECK and SIMON ciphers [7]. The 7-round SPECK-R cipher uses a round key truncated to 7 32-bit words despite the fact that the SPECK cipher on 64 bits using a 96-bit key size requires a 26-word round key. Consequently, in our implementation [21], even though there are only 7 rounds of SPECK-R, a full 26-word round key is used by cycling the full length of the expanded key.
## 4 Cellular Component of the CRC microenvironment
### Cancer-Associated Fibroblasts (CAFs)
Particularly, CAFs expressing the transcription factor-snail family zinc finger 1 (Snai1), which may be a cause of CRC resistance to oxaliplatin and cetuximab, are considered highly unfavorable in this tumor microenvironment. This is probably an effect of ECM modification, including a change in the orientation, degradation, or deposition of fibers. Therefore, SNAI1 is suspected to be an inducer of CRC resistance to chemotherapy, through the fibrosis of the ECM and the EMT process [155]. Another example of the protective action of CAFs is the exosomal transfer of miRNA (miR8-93-5p), which induces resistance in CRC cells to radiation treatment. Additionally, CAFs produce more miR-93-5p than normal fibroblasts, emphasizing their protective role against the tumor [156; 157]. CAFs as Potential Therapeutic Targets  
The diversity of fibroblasts in the CRC microenvironment, resulting from their different origins, makes it difficult to define their role. Nevertheless, being the most numerous infiltrating cells in the tumor, fibroblasts exhibit potential in anti-cancer therapy. Therefore, it is crucial to continue the exploration of therapeutic possibilities, taking into account the quantities and diversity of particles secreted by CAFs [160; 161]. IL-6, which promotes cancer by stimulating proliferation, migration, and angiogenesis, is abundantly produced not only by CRC cells but also by CAFs [66]. In the case of cancers such as ovarian tumors, the efficacy of IL-6-targeted therapy has been confirmed in preclinical and clinical studies [162]. There is limited evidence for the effectiveness of this approach in CRC; however, [PERSON] et al. observed that blocking the IL-6 receptor limits its transfer between stromal cells and CRC, thereby limiting further steps of invasion [66]. Attention was also paid to IL-11, which belongs to the IL-6 family. This cytokine produced by fibroblasts worsened the prognosis of CRC patients and promoted tumor progression. Its action led to the recruitment of additional IL-11-positive fibroblasts, initiating the division of cancer cells. Therefore, directing synthetic inhibitors towards these cytokines may have a beneficial effect in cancer therapy [163]. It has been shown that CAF products, such as fatty acids, initiate the movement of CRC cells in vitro. Fatty acid synthase, responsible for their production, is therefore another element that may influence cancer progression [164]. CAFs participate in the migration process of CRC cells. Phenotypically altered cells increase the production of metabolites that stimulate the migratory phenotype of CRC cells. Fibroblasts associated with CRC metastasis have been isolated and called metastasis-associated fibroblasts (MAFs). They differ from CAFs in surface proteins and secreted products. The presence of CD38 and the expression of the IGFBP2 gene were considered characteristic for MAFs [165; 166].
# A Two-Dimensional Non-Destructive Beam Monitoring Detector for Ion Beams
###### Abstract  
A two-dimensional beam monitoring detector named \(\pi^{2}\) has been developed and tested at the Bern University Hospital, using an 18 MeV proton beam provided by a medical cyclotron. This non-destructive device utilises a scintillating compound (P47 phosphor) coated onto a thin aluminium foil that is angled at 45\({}^{\circ}\) with respect to the beam axis. The scintillating light produced when the beam passes through the foil is captured by a CMOS camera, resulting in a two-dimensional image of the beam profile. Custom software is then used to analyse the image and extract valuable information about the beam's position, shape, and intensity. The focus of the experimental work was on characterising the performance of the \(\pi^{2}\) with the 18 MeV proton beam. The linearity of the detector's output signal was evaluated for proton fluxes ranging from \(2\cdot 10^{10}\) cm\({}^{-2}\cdot\) s\({}^{-1}\) to \(5\cdot 10^{11}\) cm\({}^{-2}\cdot\) s\({}^{-1}\). Furthermore, the beam profiles measured with the \(\pi^{2}\) were found to be consistent with reference measurements obtained using alternative beam monitors. Additionally, the experiments also involved studying the beam scattering caused by the foil and scintillating layer. Finally, in a long-term radiation test, the detector demonstrated a stable response up to an integrated proton flux of \(3\cdot 10^{15}\) cm\({}^{-2}\). The \(\pi^{2}\) is currently being used at the Bern cyclotron for monitoring beams in the development of new methods for medical radioisotope production and for radiation hardness studies. The \(\pi^{2}\) has potential applications in several fields that involve the use of accelerated ions, such as cancer particle therapy, medical radioisotope production and radiation hardness studies.  
Beams _Appl. Sci._**2023**, 13, 3657. [[https://doi.org/10.3390/](https://doi.org/10.3390/)]([https://doi.org/10.3390/](https://doi.org/10.3390/))  
sup13063657
## 2 Theoretical Analysis and Simulations  
### Theoretical Analysis  
A schematic diagram of the micro taper in-line fiber MZI is shown in Figure 1.  
In Figure 1, \(L\) is the length of the sandwiched SMF between two taper waists of the sensor, \(l_{1}\) and \(l_{2}\) are the lengths of the micro taper, and \(D_{1}\) and \(D_{2}\) are the waist diameters of the micro taper. Light is injected from the broadband source into the input SMF and then transmitted in the core of the SMF. In the taper section, since the taper is not adiabatic, the light in the core of the SMF will excite the cladding modes, and these modes, together with the core mode, will transmit independently within both the core and the cladding of the sandwiched SMF between the two tapers (\(D_{1}\) and \(D_{2}\)). Since these modes have different propagation constants, when they recouple into the output SMF at the second taper \(D_{2}\), interference between these modes will take place, resulting in power variations. The intensity of the output of the proposed MZI owing to the interference between the core mode and order cladding mode is:  
\[I_{T}=I_{1}+I_{2}+2\sqrt{I_{1}I_{2}}\cos(\Delta\varphi) \tag{1}\]  
where \(I_{1}\) and \(I_{2}\) are the light intensities of the core and cladding modes, and \(\Delta\varphi\) is the phase difference between them. Assuming \(L\) is the length of the sandwiched SMF between two taper waists the phase \(\Delta\varphi\) can be expressed as below:  
\[\Delta\varphi=2\pi\frac{(n_{co}-n_{cl})L}{\lambda} \tag{2}\]  
Figure 1: Schematic diagram of the micro taper in-line fiber Mach–Zehnder interferometer (MZI).  
where \(n_{\omega}\) and \(n_{cl}\) are the effective refractive indexes of the core and cladding, respectively, \(\lambda\) is the input wavelength and \(L\) is distance of sandwiched SMF between two taper waists. The free spectral range (FSR) of the fabricated MZI can be expressed as:  
\[\text{FSR}= \frac{\lambda^{2}}{(n_{\omega}-n_{cl})L} \tag{3}\]  
The FSR is inversely proportional to the interaction length, and the wavelength with minimum output light intensity are located at:  
\[\lambda_{dip}=\frac{2}{2m+1}(n_{\omega}-n_{cl})L \tag{4}\]  
where \(m=0\), \(1\), \(2\dots\) When the surrounding temperature of the fiber changes, the sensitivity of the sensor can be expressed as:  
\[\frac{d\lambda}{dT}\approx\frac{\left[\frac{\lambda}{\Delta n_{eff}^{m}}( \frac{\partial\Delta n_{eff}^{m}}{\partial n_{\omega}}\frac{dn_{\omega}}{dT}+ \frac{\partial\Delta n_{eff}^{m}}{\partial n_{cl}}\frac{dn_{cl}}{dT})+\frac{ \lambda dL}{LdT}\right]}{1-\frac{\lambda}{\Delta n_{eff}^{m}}\frac{\partial \Delta n_{eff}^{m}}{\lambda}} \tag{5}\]  
where \(\Delta n_{eff}^{m}\) is the effective refractive index (RI) difference between the core and cladding mode.
## 2 Places, Instruments, and Data
### BF-1 Mission and Instruments  
\"Bu Feng (\(\#\)\(\#\)\(\#\))\" means \"chasing the winds\" in Chinese; BF-1 A/B is the first Chinese GNSS-R mission comprising two demonstration satellites. The payloads of the BF-1 A/B twin satellites, which are the main payloads of the carrier, were developed by the China Aerospace Science and Technology Corporation (CASC). Each platform has two nadir GNSS-R antennas, one navigation antenna, one auxiliary antenna, four low-noise amplifiers with blackbody calibration, and a GNSS-R receiver. The two nadir antennas are directed at the left and right sides of the platform with inclination angles of 26\({}^{\circ}\). In general, the number of specular points located in the antenna footprint of the Global Positioning system (GPS) is approximately four. Both the instruments have four digital channels to receive the best four signals out of the specular points by different GNSS satellites. For every second, the onboard receivers can simultaneously process the four reflected signals into DDMs. The specifications of the receiver are shown in Table 1.  
Each GNSS-R receiver collects four DDMs per second for 24 h each day. In this mission, the world's first in-orbit BDS DDM was mapped on 10 June 2019 and its first DDM was acquired on 9 June 2019. The in-orbit-measured DDM has dimensions of \(128\times 21\), corresponding to 32 delay chips in quarter chip increments and a 5000 Hz Doppler frequency with 250 Hz intervals. Examples of DDMs measured by the BF-1A instrument are shown in Figure 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Value** \\ \hline Frequency & GPS L1 and BeiDou B1 \\ Antenna gain & \(\geq\)14 dBi \\ Mass & \(\leq\)10 kg \\ Power consumption & \(\leq\)30 W \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of BF-1 A/B global navigation satellite system reflectometry (GNSS-R) receiver.  
Figure 1: BuFeng-1 (BF-1) A/B science data coverage of the first 7 days of July 2019. The color shows the wind speed corresponding to the European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis wind datasets. The distributions of the specular points are within \(\pm\)53\({}^{\circ}\) latitude. The maximum wind speed is 40.5 m/s located at 56.6\({}^{\circ}\)E and 39.1\({}^{\circ}\)S. The red diamonds in the middle of the equatorial Pacific are the National Data Buoy Center (NDBC) Tropical Atmosphere Ocean (TAO) mooring buoy array.
## 3 DC Bus Voltage Control Strategy  
The performance and stability of the UPQC are closely related to its internal control structure and DC voltage regulation technology. Since the inverter circuit contains numerous power semiconductor devices, which generate switching losses during operation, the DC side capacitor voltage will continuously decrease without energy replenishment, causing the UPQC to fail in its compensation tasks. On the other hand, when switching devices are turned on or off, they also interfere with the DC side voltage, leading to voltage fluctuations.  
From the perspective of energy conservation in the UPQC, the energy required to maintain a constant DC side voltage is provided by the parallel converter. The parallel converter exchanges power with the AC grid by outputting active current components, thereby achieving control of the DC side voltage.  
Figure 3: UPQC shunt side structure block diagram.
# Transcription of _Listeria monocytogenes_ Key Virulence Genes on Tomato, Cucumber and Carrot
## 2 Materials and Methods
### In Vitro and In Situ Gene Transcription Assay  
After sample homogenization, 10 mL of the homogenate was transferred in sterile Falcon(r) tubes and centrifuged (12,000\(\times\)_g_; 1 min; sample temperature). Then, the supernatant was discarded and the biomass was mixed with 200 \(\upmu\)L of RNAlater(r) solution (Ambion, Whaltham, MA, USA). RNA was extracted with the NucleSpin(r) RNA Kit (Macherey-Nagel, Dueren, Germany); cDNA synthesis took place with the PrimeScript(tm) One Step RT-PCR Kit (Takara Bio, Shiga, Japan) using random hexamers. Two reverse transcription reactions (+RT) and one without the addition of reverse transcriptase (-RT) were performed for each sample, containing ca. 0.5 \(\upmu\)RNA each. The primers and PCR conditions were according to [PERSON] et al. [45]. Real-Time qPCR was performed using KAPA SYBR qPCR Kit Master Mix (2X) ABI Prism (KapaBiosystems, Boston, MA, USA). No template controls (NTC) were also included to detect reagent contamination. The Ct values of the genes under study were processed only if the difference between +RT and -RT was at least 10 cycles and no amplification was observed in NTC.
## 2 Frequency Characteristic by Exchange of Frequency Containment Reserves Control
### Communication Infrastructure System  
As illustrated in Figures 5 and 6, the communication infrastructure system of the WAMPAC platform is utilized [23]. The synchro phasor data are gathered via the networks and processed in phasor data concentrators (PDCs) to depict the electric power grid's real-time operational characteristics. The communication networks integrate many PDCs that are geographically distributed at various substations using a variety of media, including metal wire, dedicated optical fiber, satellite, radio frequency, and microwave. Local PDCs may also interact with super PDCs at ISO/RTO to provide WAMPAC applications and situational awareness. The data may be enforced in wide area controls (WACs), software, and sophisticated data analysis tools to virtualize, evaluate, and operate each linked bulk power system as a whole [24]. Different WACs may be developed using the WAMPAC platform to provide higher performance that is not possible with conventional power systems. All PDCs should have reliable access to Coordinated Universal Time (UTC), which should be given via satellite and GPS time clocks, independent of the communication network. Regarding the difference color on communication infrastructure system is made according to hierarchy of communication infrastructure level.  
Figure 4: PDC and PMUs within a substation.
## 3 Enhanced Frequency Response Strategy of a DFIG
### Enhanced Frequency Response Strategy of the Doubly Fed Induction Generator  
Figure 4 illustrates the control concept of the enhanced frequency response strategy of a DFIG. To boost the frequency stability consisting of reducing the maximum ROCOF, boosting the frequency nadir, and improving the settling system frequency, this paper suggests a rapid frequency response strategy that the operating point moves toward to the torque limit (\(P_{tor}\)) to feed more power to the power grid.  
The power reference during the frequency response period can be represented as:  
\[P_{ref}(\omega_{r})=\frac{P_{tor}(\omega_{1})-P_{MPPT}(\omega_{3})}{\omega_{1}- \omega_{3}}\times(\omega_{r}-\omega_{3})+P_{tor}(\omega_{1}), \tag{10}\]  
Figure 4: Control diagram of the enhanced frequency response of the DFIG-based over-speed de-loaded curve.  
where \(P_{nr}(\omega_{r})\) is the power reference when performing frequency support. \(\omega_{1}\) and \(\omega_{3}\) are the rotor speeds at operating point 1 and operating point 3, respectively, in Figure 5. \(\omega_{r}\) is the rotor speed of the wind turbine. \(P_{tor}(\omega_{1})\) is the torque limit referred to the power at \(\omega_{1}\). \(P_{MPPT}(\omega_{3})\) is the maximum available power at operating point 3 in Figure 5.  
Figure 5 displays the control characteristics of the proposed enhanced frequency support scheme of the DFIG. The active power reference increases to \(P_{tor}\) (which is the torque limit and the maximum power) to provide enhanced and more power from the trajectory of operating point 1 to operating point 2 so that the maximum ROCOF and the frequency nadir can be improved. After that, for boosting the settling frequency, the power reference linearly decreases with the rotor speed toward to operating point 3, which corresponds to the MPPT operating point. At operating point 3, the output power of the DFIG is equal to the mechanical power; thereby, the rotor speed keeps operating in point 3. As a result, the proposed enhanced frequency support scheme can inject more power to the grid than that prior to the disturbance so as to improve the settling frequency.
# Reliability Analysis of Small-Sample Failure Data for Random Truncation High-Voltage Relay
## 2 Principle of Random Truncation Small-Sample Reliability Analysis
### Basic Principle of Average Rank Method  
Assuming five samples of the same type are subjected to random truncation tests, the failure time and truncation time are arranged in ascending order, denoted as \(t_{1}\leq t_{2}+\leq t_{3}\leq t_{4}\leq t_{5}\), where \"\(+\)\" represents right truncated data. If the test continues until the No.2 product fails, then the fault time \(t_{2}\) may occur at any time within the interval \((t_{2}+,t_{3})\), \((t_{3},t_{4})(t_{4},t_{5})(t_{5},\infty)\). It can be considered that the failure probability of the No. 2 product is the same within these four time intervals. The fault rank of \(t_{1}\) is 1 regardless of which time interval \(t_{2}\) occurred in. The fault rank of \(t_{3}\) is between 2 and 3, with a probability 3/4 of 2 and a probability 1/4 of 3. From the perspective of equal probability, the fault rank of \(t_{3}\) is \(2\times 3/4+3\times 1/4=9/4\). Similarly, the fault rank of \(t_{4}\) and \(t_{5}\) are \(3\times 2/4+4\times 2/4=14/4\) and \(4\times 1/4+5\times 3/4=19/4\), respectively.  
The above fault rank calculation process can be generalized as follows. Assuming \(n\) samples are subjected to random truncation experiments, of which \(p\) samples are faulted and \(n-p\) samples are right truncated, we arrange all the \(n\) data in ascending order, and  
Figure 1: Random truncation sample data.  
Figure 2: Flow chart of reliability analysis for random truncation small-sample fault data.  
their ranks are numbered from 1 to \(n\), denoted as \(j\). Then, arrange \(p\) fault data in ascending order, and the fault rank of the \(i\)th fault data is calculated using Equation (1):  
\[r_{i}=r_{i-1}+\left(n+1-r_{i-1}\right)/\left(n+2-j\right) \tag{1}\]  
where \(r_{i}\) is the fault rank of the \(i\)th fault data, \(1\leq i\leq p\), \(r_{0}=0\).
## 2 Architecture  
### The \(\upmu\)Doc.tS Information System  
To address the ever-growing demands of the end users, the proposed data fusion incorporates a three-tier architecture.  
Figure 1 illustrates the architecture of the information system \(\upmu\)Doc.tS, showcasing its most recent version and the arrangement of its building blocks and presenting the integral role of the API Gateway in the information system. The architecture has already been successfully validated in other industrial sectors (e.g., Shipping [24], Oil and Gas [25]). The algorithmic processes operate in the background (first layer), utilizing the information and requests received from the front through a Graphical User Interface (third layer). Acting as a request director and workload manager, the intermediate layer (second layer) operates between these two layers.  
Figure 1: The \(\upmu\)Doc.tS information system architecture.  
**First layer.** The \(\mu\)Doc.tS information system is composed of several tools and applications designed to enhance the digital representation of a manuscript and facilitate its transcription.  
* The **Document Layout Analysis Tools** aim to eliminate unwanted noise, convert the image into a binary format, and identify specific areas of the image that contain text;
* The **Handwritten Text Transcription Tool** is designed to transcribe handwritten text from a digital image;
* The **Content-Based Word Tracking Tool** facilitates the exploration of a digitized manuscript by efficiently scanning a collection of manuscript images and pinpointing the precise locations where a specific word has been detected;
* The **Language Models and Dictionaries From TLG And Body of Texts** application facilitates a spelling check on the transcribed text of a manuscript.  
**Second layer.** The **API Gateway** facilitates the seamless integration of the software tools and packages, ensuring the smooth flow of information between them. Finally, the **SQL Database** serves as a repository for document collections, document files, transcriptions, and partitioning models.  
**Third layer.** The user of the platform can do the following:  
* Create and manage a collection of manuscripts;
* Use preprocessing tools for manuscript collection images;
* Produce the layout analysis of the manuscript;
* Transcribe the images of the manuscripts;
* Edit the extracted result of the transcription and correct it efficiently;
* Search for words in the images of the collection (keyword spotting);
* Train document layout analysis and transcription models to manage new collections with special features;
* Save the transcription of images in a collection in a manageable format for further use.  
The API Gateway, being the main focus of the present manuscript, is presented in detail in the Section 2.2.