esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The tremendous increase of Internet traffic requires the development of large-capacity, fully-transparent, and energy-efficient all-optical communication systems. Optical signal processing is one of the solutions to realize such a communication system, because the signal processing directly performs the required operation in the optical domain and reduces the power consumption in order to avoid optical-to-electrical/electrical-to-optical conversions. In particular, optical signal processing has been employed in several functional components, such as packet switching [1; 2], label recognition [3; 4], error detection and correction [5; 6; 7], encryption [8; 9], half- and full-adders [10; 11; 12], correlators [13; 14], equalizers [15; 16], and comparators [17; 18; 19; 20; 21; 22].  
Among these components, the optical comparator is considered to be fundamental because it can perform most of the operations, including packet switching, label recognition, error detection and correction, etc. In this context, various optical comparators have been proposed by using the several platforms such as a semiconductor optical amplifier [17; 18], a nonlinear fiber ring resonator [19], a Fabry-Perot laser diode [20], a micro ring resonator [21], and an electro-optical ring resonator [22].  
On the other hand, the previous studies have been confined to on-off keying (OOK) modulation format, not phase-shift keying (PSK) modulation. The optical comparator for PSK modulation is desirable due to the requirement of coherent optical transmission for high-speed and long-haul communication. However, to our best knowledge there have been no reports of this kind of optical comparator. Previously, the author proposed a novel type of optical comparator for PSK modulation, and demonstrated the comparison operation for 4- and 6-bit length quadrature PSK (QPSK)-modulated signals [23]. In this paper, the author improves the valid number of code word to 8-bit length, and reports a newly evaluated comparison operation for 8-bit QPSK signal by using a serially-cascaded delay line interferometer (DLI) at 10 Gbaud.
## 3 Results  
### Prevalence of Enterococcus spp. from Hospital Bacterial Isolates  
A total of 165,098 bacterial cultures, with 26,699 isolated bacterial strains (16.15%), were processed between 2019 and 2021, from three hospitals in Galati, Romania.  
The number of microbiological samples decreased by 44.1% from 2019 to 2021. We also noticed a reduction in the diversity of the biological samples and bacterial isolates due to the context of the long period of dedication of hospital care to COVID-19 (Figure 1). The ratio between Gram-positive cocci and Gram-negative rods was similar in each of the three years of the study. The frequency of _Staphylococcus_ species reported to be Gram-positive cocci trend to increase, from 67.9% to 80.9% (Table 1).  
The isolated clinically significant bacterial strains were mainly grouped into 55% Gram-negative bacilli and 44.7% Gram-positive cocci (Figure 2). From the Gram-negative bacilli group, the first place was held by Enterobacteriaceae: _E. coli_ 27.4%, _Klebsiella_ spp. 14.7% (_K. pneumoniae_, _K. oxytocin_), _Proteus_ spp. 3.4% (_P. mirabilis_, _P. vulgaris_), _Enterobacter_ spp. 1.37% (_E. cloacae_, _E. aerogenes_), _Salmonella_ spp. 0.8%, and other Enterobacterales 0.16% (_Serratia_ spp., _Citrobacter_ spp., _Morganella_ spp., _Providencia_ spp.). Non-ferromagnetic Gram-negative bacilli were less frequent, including _Pseudomonas_ spp. 6.03% (_P. aeruginosa_, _P. putida_, _P. fluorescens_, _P. stutzeri_), and _Acinetobacter_ spp. 1.2% (_A. baumannii_, _A. haemolyticus_). Regarding the Gram-positive cocci, the leading strain was _Staphylococcus_ spp. 32.8% (_S. aureus_, coagulase-negative _Staphylococcus_), followed by _Streptococcus_ spp. (\(\beta\)- Hemolytic group 5.5%, _Streptococcus pneumoniae_ 5.08%), and _Enterococcus_ spp. 0.86% (Table 1). Among the Gram-positive cocci, after the excluded duplicates, contaminates, and incompletely identified species, we found 86 strains of _Enterococcus spp._, meaning 0.32%. The distribution of the isolated species was evidenced by 47.7% _E. faecalis_ (41 strains), 47.7% _E. ficecium_ (41 strains), 3.55% _E. gallinarum_ (three strains), and 1% _E. hire_ (one strain) (Figure 3).  
The biological samples positive for enterococci consisted of 38.4% (33) urocultures, 32.6% hemocultures (28), 22% purulent secretions of leg ulcers or other skin lesions (19), and 7% others (6), including conjunctival and otic secretions. (Figure 1). The urine isolateswere 18 species of _E. ficeium_ and 15 strains of _E. ficealis_. From 28 positive hemocultures, the following species were identified: 13 _E. ficeium_, 11 _E. ficealis_, 3 _E. gallinarum_, and 1 _E. hirne_.
# Assessment of Aquatic Weed in Irrigation Channels Using UAV and Satellite Imagery
## 2 Materials and Methods
### Image Capture
#### 2.3.2 Satellite  
In order to assess the effectiveness of the developed weed detection methods in classifying much larger areas of irrigation channels, two images from the World-View 3 satellite constellation were purchased. These were selected from the archive, on 7 January 2017 and 13 March 2018, with both captures occurring within 15 min of local noon. The common area between the images was used for analysis, covering 38.5 km\({}^{2}\). This covers part of the MIA around the town of Whitton, and the capture area is shown in Figure 1. The images had a pan-sharpened resolution of 30 cm, and eight multispectral bands. Four of the eight instrument bands were used, and are specified in Table 1.
# Interference-Robust Transmission in Wireless Sensor Networks
## Appendix B Derivation of Equation (2)  
It can be shown that the packet error rate (PER) without channel coding in the presence of interference can be represented as:  
\[p=1-\left(1-b\left(\gamma_{\text{SNR}}\right)\right)^{L_{\text{pkt}}-L_{\text {int}}}(1-b\left(\gamma_{\text{SINR}}\right))^{L_{\text{int}}} \tag{3}\]  
where \(b\left(\gamma\right)\) denotes the BER at an SNR of \(\gamma\), \(\gamma_{\text{SNR}}\) denotes the SNR in the absence of interference, \(\gamma_{\text{SINR}}\) denotes the SINR in the presence of interference signal, \(L_{\text{pkt}}\) denotes the bit size of a packet and \(L_{\text{int}}\left(\leq L_{\text{pkt}}\right)\) denotes the number of bits colliding with the interference signal. Since \(L_{\text{int}}\) can randomly be changed, the average PER can be represented as:  
\[\begin{array}{rcl}\bar{p}&=&E_{L_{\text{int}}}\left[p\right]\\ &=&1-\left(1-p_{s}\right)E_{L_{\text{int}}}\left[K^{L_{\text{int}}}\right] \end{array} \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(L\) & 20 Bytes & 40 Bytes & 60 Bytes & 80 Bytes & 100 Bytes & 120 Bytes \\ \hline \(\hat{\gamma}\) & 0.40 dB & 0.68 dB & 0.83 dB & 0.93 dB & 1.01 dB & 1.07 dB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Minimum SINRs for IEEE 802.15.4 communications.  
where \(p_{s}=1-(1-b\left(\gamma_{\text{SNR}}\right))^{L_{\text{int}}}\) and \(K=(1-b\left(\gamma_{\text{SINR}}\right))/(1-b\left(\gamma_{\text{SNR}}\right))\). Note that \(0.5<K<1\) since \(0<b\left(\gamma_{\text{SNR}}\right)<0.5\), \(0<b\left(\gamma_{\text{SINR}}\right)<0.5\) and \(b\left(\gamma_{\text{SNR}}\right)<b\left(\gamma_{\text{SINR}}\right)\). Without information on the probability distribution function of \(L_{\text{int}}\), it may not be easy to calculate the expectation. For ease of calculation, define a random variable \(\varphi\) by:  
\[\varphi=\left\{\begin{array}{l}1;L_{\text{int}}=0\\ 0;L_{\text{int}}>0\end{array}\right. \tag{10}\]  
It can be shown from \(\varphi\leq K^{L_{\text{int}}}\) for all \(L_{\text{int}}\) that:  
\[\begin{array}{rcl}\tilde{p}&=&1-(1-p_{s})\left(1-p_{c}\right)\\ &\geq&1-(1-p_{s})\,E_{L_{\text{int}}}\left[K^{L_{\text{int}}}\right]=\tilde{p} \end{array} \tag{11}\]  
where \(p_{c}\) denotes the probability of transmission failure assuming that the packet transmission is failed whenever the collision occurs and equals \(1-E_{L_{\text{int}}}\left[\varphi\right]\). The assumption may be valid if SINR is sufficiently low. Table 1 summarizes pairs of \(\gamma_{\text{SINR}}\) and \(L_{\text{int}}\) yielding \(K^{L_{\text{int}}}<0.01\) for IEEE 802.15.4 communications.
## 2 Materials and Methods  
### Dataset and Annotations  
The corpus is a collection of 200 de-identified Spanish EHR notes, with an average number of sentences per note of 9.74, that were provided by a reference institution of high obstetric risk, which has highly complex Gynecology and Obstetrics services. The EHRs correspond to those women who gave birth between 2015 and 2019. A total of 25% of the EHR notes were labeled by two clinical professionals in an annotation software called _INCEpTION_[24]. The remaining 75% were annotated by a single clinical professional. Figure 1 shows an example of an annotated note. The selected entities are related to risk factors and outcomes in pregnancy and childbirth. The entities were annotated according with the _Unified Medical Language System (UMLS)_ and its semantic network. The corpus had a total of 8 entities, corresponding to five categories (Idea or Concept was itemized into negation, quantitative, qualitative, and temporal concepts):  
1. Anatomy (T023): Body part, organ, or organ component.
2. Disease or Syndrome (T047): Observations made by patients or clinicians about the body or mind that are considered abnormal or caused by disease.
3. Healthcare activity (T058): Descriptions of diagnostics and laboratory procedures.
4. Idea or Concept: 1. Negation (C1518422): Those concepts that indicated something does not exist or that it lacks veracity. 2. Quantitative concepts (T081): Those concepts that refer to the numerical nature of data, such as measurements obtained in a laboratory test. 3. Qualitative concepts (T080): Concepts that are used to name what is linked to the quality, such as the way of being or the properties of something. 4. Temporal concepts (T079): These refer to a point in time that can be a time, but they exclude the concepts that refer to duration (duration is an amount).
5. Sign or Symptom (T184): An observable manifestation of a disease or condition based on clinical judgment, even if a disease or condition experienced by the patient and is reported as a subjective observation.  
At the end of the process, the corpus contained 1842 sentences and 13,998 labeled entities belonging to 7 classes, as shown in Table 1. We obtained a value of \(\kappa=0.636\) from the 50 clinical notes annotated by two physicians, which is considered a substantial level, in accordance with [25]. This result is a very competitive outcome in the clinical context, where the interpretation of medical entities may vary slightly between experts. To mitigate potential discrepancies, joint discussions were held regarding annotation differences, thus improving consistency in the rest of the documents labeled by a single annotator.
## 3 Results and Discussion
### Biparametric Diagram  
Figure 6 shows a biparametric diagram with \(k_{1}\) on the abscissa axis and \(k_{2}\) on the ordinate axis. This figure helps identify stability from \(1T-\) to \(7T\)-periodic orbits. For this figure, the same initial conditions \((2.5\ \ 2.1875)^{T}\), \(\gamma=0.35\), and \(T=0.18\) seg are considered. In this plot, red corresponds to areas with \(1T\)-periodic orbits, yellow to \(2T\)-periodic orbits, blue to \(3T\)-periodic orbits, green to \(4T\)-periodic orbits, white to \(5T\)-periodic orbits, black to \(6T\)-periodic orbits, magenta to \(7T\)-periodic orbits, and cyan to higher orbits.  
Regarding Figure 6, in an experimental test, it is desired that the vast majority of points in the biparametric diagram belong to \(1T\)-periodic orbits (red point), which represent stability in the controlled system. It is desired that \(1T\)-periodic orbits have the same frequency as the switch (MOSFET). This ensures that chattering is eliminated in the system, subharmonics are reduced, and the system is more stable. In this paper, the ZAD technique is used to control the voltage, but the user must give it the values of \(k_{1}\) and \(k_{2}\) in such a way that the system evolves to \(1T\)-periodic orbits. The biparametric diagram of Figure 6 shows the \(k_{1}\) and \(k_{2}\) values to obtain stability.
## 5 Scheme Construction
### Encryption  
When the data owner outsources EHR data, they use a symmetric encryption algorithm and a key K to encrypt the data. Then, they encrypt key K using the following encrypt algorithm with the access policy \(\psi\). Encryption is performed as follows:  
**Encrypt(K, \(\psi\), GPP,\(\cup APK_{k}\)) \(\rightarrow\) (CT):** As we mentioned to choose the DNF of a set of sub-policies, let us assume there are q sub-policies, i.e., \(\{\psi_{0}\}_{i=1,2,\ldots,q}\). For simplicity, let us call each of them \(W\).The sub-policy \(W\) is the LSSS matrix. Let us take some rows and columns in the LSSS matrix as land n. The function \(\rho\) associates each row \(W_{j}\) of \(W\) to attribute \(\rho(j)\). To make the policy hidden, a set of attribute values is denoted as \(Z=(Z_{\rho(1)},\ \ldots\ Z_{\rho(l)})\) and is attached to the access policy. Thus, the sub-policy can be expressed by \((W,\ \rho,\ Z)\). For each sub-policy, the following steps are run:  
Step 1. The data owner chooses \(s\in Z_{p}\) and a random vector  
\[v=(s,\ v_{2}\ \ldots\ v_{n})^{T}\in\ Z_{N}^{n}\]  
Step 2. For each row of the matrix, the following is calculated:  
\[\lambda\mathrm{x}=W_{x}\cdot v\]  
Step 3. Then, for every \(x\ \epsilon\ [l]\), it selects a random exponent \(r_{x}\ \epsilon Z_{p}\) and calculates:  
\[C=K\cdot\left(\prod\
olimits_{k\in SAA}e(g,g)^{\alpha_{k}}\right)\mathrm{s}=K \cdot e(g,g)^{s\sum_{k\in SAA}a_{k}} \tag{5}\]  
\[C_{0}=g^{s} \tag{6}\]  
\[C_{1,x}=g^{\sum_{k\in SAA}\beta_{k}\lambda_{x}}\cdot\left(\tau_{\rho(x)}^{Z_{ \rho(x)}}\right)^{-r_{x}} \tag{7}\]  
\[C_{2,x}=g^{r_{x}} \tag{8}\]  
Here, SAA is defined as the index set of \(\mathrm{AA_{k}}\). This set consists of an index of AAs whose attributes are present in the policy.  
Step 4. Finally, the data owner sends the ciphertext data to a cloud \(\mathrm{E(K)}=\{\{\psi_{1}\}_{i=1,2,3},\ldots,\lambda_{x}\}\) {\(\mathrm{E_{i}}\}_{i=1,2,3},\ldots,\lambda_{x}\), where, \(E_{i}=\left\{C,\ C_{0},\ (C_{1,x},\ C_{2,x})_{x\in\{1,\ldots,l\}}\right\}\).  
The decryption of the data by the user is possible when their attribute set matches any of the sub-policy \((W,\ \rho,\ Z)\). The matching process is successful when for all \(x\in\{1,\ \ldots,\ l\}\), \(a_{\rho(x)}=Z_{\rho(x)}\) and constants \(\mu_{x}\in Z_{N}\) such that \(\sum_{x\in l}\mu_{x}\lambda_{x}=s\).
# Slotted ALOHA on LoRaWAN-Design, Analysis, and Deployment
###### Abstract  
LoRaWAN is one of the most promising standards for long-range sensing applications. However, the high number of end devices expected in at-scale deployment, combined with the absence of an effective synchronization scheme, challenge the scalability of this standard. In this paper, we present an approach to increase network throughput through a Slotted-ALOHA overlay on LoRaWAN networks. To increase the single channel capacity, we propose to regulate the communication of LoRaWAN networks using a Slotted-ALOHA variant on the top of the Pure-ALOHA approach used by the standard; thus, no modification in pre-existing libraries is necessary. Our method is based on an innovative synchronization service that is suitable for low-cost wireless sensor nodes. We modelled the LoRaWAN channel with extensive measurement on hardware platforms, and we quantified the impact of tuning parameters on physical and medium access control layers, as well as the packet collision rate. Results show that Slotted-ALOHA supported by our synchronization service significantly improves the performance of traditional LoRaWAN networks regarding packet loss rate and network throughput.  
Keywords:Internet of Things; wireless sensor networks; long range radio; LoRa; LoRaWAN; ALOHA; Slotted ALOHA; synchronization; low-energy wireless protocols +
Footnote †: journal: Sensors
## 5 Results and Discussion
The maximum Gumbel case with MCET stands out with a 10.0% increase over the Gaussian-based MCET and 3.94% over the log-normal method, despite conceptual similarities with the log-normal method. Conversely, for left-skewed histograms, the minimum Gumbel case produced a noticeable improvement rate. It surpasses other distributions with a 9.98% \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**(a)** & **(b)** & **(c)** & **(d)** & **(e)** & **(f)** \\ \hline IMG 1 & 0.79471 & 0.79471 & 0.81531 & 0.80269 & 0.84069 \\ IMG 2 & 0.78977 & 0.81086 & 0.81966 & 0.80571 & 0.85816 \\ IMG 3 & 0.80253 & 0.80896 & 0.80719 & 0.85880 & 0.81705 \\ IMG 4 & 0.75995 & 0.74663 & 0.75137 & 0.83207 & 0.76492 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average evaluation scores of images in Figure 6. (a) Original images’ (b–f) the evaluation of MCET based on the Gaussian [3], Gamma, log-normal [25], Gumbel minimum, and Gumbel maximum, respectively. Figure 6: Selected samples: (**a**) original images, 1 and 2, Sentinel-2 satellite water bodies, and images 3 and 4, dermoscopic skin lesions; (**b–f**) segmented images using MCET based on the Gaussian [3], Gamma, log-normal [25], Gumbel minimum, and Gumbel maximum, respectively. (**g**) The corresponding ground truths for the images in column (**a**). increase over Gaussian distribution and up to 8.60% over others, as in shown in the rates of Table 3.
# A Brief, In-Depth Survey of Deep Learning-Based Image Watermarking
## 2 Preliminaries
### Typical Metrics and Factors
On the other hand, multi-bit watermarking allows for the embedding of additional information within the image, facilitating a broader spectrum of applications including copyright protection, content annotation, and data tracking. However, the embedding of multiple bits may potentially impact the perceptual quality of the image, necessitating a careful balance between data capacity and image fidelity. A distinct concept is zero watermarking, where no watermark is directly embedded [26; 27]. Instead, a relationship is established and stored between the original content and the watermark (a master share). In case of disputes or verification, this relationship is used to demonstrate the presence of the watermark. This technique prefers advantages, such as eliminating the need to alter the image, thus preserving its original quality. Nonetheless, limitations exist, primarily due to its dependency on the uniqueness of image data, rendering it less effective for images with homogeneous content.
## 3 Results and Discussion
### XRD Analysis of Al-BiSn Composites  
XRD patterns of all extruded and polished samples were analyzed. Figure 4 shows the XRD results of extruded Al and Al-BiSn composites containing various concentrations (5, 10, and 15 wt.%) of BiSn. In pure Al, characteristic peaks of (111), (200), (220), (311), and (222) are noticed, representing a typical face-centered cubic structure (FCC) of Al. Moreover, it is also noticed that in Al-BiSn composites containing 5 wt.% of BiSn, the peaks of the reinforcements are not visible. This may be due to either the low concentration of BiSn in the composite, which is below the detectable limit of the XRD machine, or to the high relative intensity of peaks of crystalline Al.
# CBM: An IoT Enabled LiDAR Sensor for In-Field Crop Height and Biomass Measurements
## 2 Materials and Methods
### Field Experiment and Data Collection  
The developed CBM sensor system was deployed in a wheat field trial at the Plant Breeding Centre, Agriculture Victoria, Horsham, Victoria, Australia. The field trial comprised of 36 wheat genotypes that were planted in individual plots (Figure 4a) with a sowing density of approximately 150 plants per m\({}^{2}\). The list of wheat genotypes is provided in Supplementary Table S2. Each plot was 1 m wide and 5 m long (Figure 4a). Field observation that included both the sensor readings and ground truth manual measurements were performed at 100 and 140 days after sowing (DAS) where the plants were at first-node and anthesis growth stages, respectively.  
The CBM sensor was mounted in a downward nadir facing angle. The field mount was a manual push-type vehicle with a wheelbase or width of 1.25 m, which enabled traversing the sensor along the lengthwise direction of the wheat plots (Figure 4a,b). A sinuous traverse path trajectory was used to cover each row in an individual range, followed by the next range in a reverse direction. This pattern enabled the least movement of the rover or the equivalent time in data acquisition. The LiDAR data scan was recorded continuously throughout the scanning mission at a rate of 60 Hz. The field phenotyping data using the CBM sensor was captured on the same days before the manual plant height and biomass harvest. The manual crop height was measured from the ground level to the highest point of the plant with the average of the four random height measurements per plot. The LiDAR sampling method is arguably more accurate than the manual crop height measurement method, at least for assessing average crop height within each plot of -750 individual plants (150 per m\({}^{2}\)). Thereafter, each plot was manually harvested separately for the above-ground biomass and weighed soon after harvest to measure the fresh weight (FW) biomass and oven-dried at 70 \({}^{\circ}\)C for five days to measure the dry weight (DW) biomass.  
Figure 3: The laboratory distance measurement experimentation. The CropBioMass (CBM) sensor was positioned at different reference distances. The LiDAR measurement response was empirically calibrated.
# Ecosystem Water Use Efficiency in the Three-North Region of China Based on Long-Term Satellite Data
## 1 Introduction
However, effects of these projects have raised wide controversies, as the limited precipitation (less than 400 mm) in the region may not be enough to support such large scales of afforestation. [PERSON] [38] proposed that the limited precipitation and high potential ET here may even deteriorate the ecological environment. Other studies have different views, and debates have been raised [39; 40; 41; 42]. The main aim of this study was to assess the spatial dynamics of WUE in the Three-North region of China to evaluate the vegetation restoration effects in the ecologically fragile region by: (1) analyzing the spatiotemporal variations of WUE in the Three-North region of China; (2) investigating the fluctuation and sustainability trends of WUE in this region; and (3) studying the influencing factors of WUE and the WUE of different vegetation covers.